electronics

Article Potential of Pressure Sensor Based Mass Estimation Methods for Electric

Utz Spaeth *, Heiko Fechtner, Michele Weisbach and Benedikt Schmuelling Chair of Electric Mobility and Energy Storage Systems, University of Wuppertal, 42119 Wuppertal, Germany; [email protected] (H.F.); [email protected] (M.W.); [email protected] (B.S.) * Correspondence: [email protected]; Tel.: +49-202-439-1518

 Received: 18 March 2020; Accepted: 24 April 2020; Published: 26 April 2020 

Abstract: One approach to improve the economic efficiency of trolleybuses in the so-called BOB Project in the German town of Solingen is to use them as mobile energy storages in a smart grid. To achieve this, reliable information on available energy is essential, which in turn needs to be derived from a precise range calculator. As shown in this article, vehicle mass is a strong influencing factor, especially in urban traffic. Depending on passenger volume, the total mass and range of the varies by about 30 percent. The currently available mass on the bus fluctuates by more than 2 tons for constant payloads, and there is no proven solution for a more accurate mass estimation for buses in public passenger transportation. Therefore, this article presents a viable methodology to detect changes in payload, using high precision pressure sensors on the bus’s and air suspensions. These mass inducted pressure changes are extracted from the measurement data, using a filter to be later converted back into the corresponding masses. As the article will show, both approaches have their respective advantages and disadvantages, but have high potential and should therefore be investigated further.

Keywords: mass estimation; pressure sensors; range prediction; smart grid; sensor fusion; air suspension; tires

1. Introduction Trolleybuses provide local public transportation in 300 cities worldwide [1], as in the German town of Solingen, where trolleybuses have run for more than 65 years. Although much has changed over the years, one major shortcoming—dependence on overhead lines for their electrical supply—has constantly limited their operating range and flexibility. Supplementary diesel units (and, on other routes, diesel-powered buses) have always proven necessary—up to now. At a cost of 900,000 Euro, battery-powered trolleybuses are anything but a cheap investment. Nevertheless, they are vital in times of bans on diesel-powered vehicles in some downtown areas. For this reason, it is especially important to increase their market penetration by maximizing their economic efficiency and scope of application.

1.1. Worldwide Spread of Electric Buses Electric mobility is playing an increasingly important role in the long-term fight against climate change, such as to eliminate local emissions. For example, in Paris, the public transport operator has recently ordered 800 electric buses [2]. Additionally, over 800 electric buses are already registered in Germany by 2019 [3]. However, only 154 of them are full electric vehicles [3]. In the United States of America, a mere 0.6% of the 70,000 transit buses were electric vehicles in 2018 [4]. Moreover, 95% of the American school buses are powered by diesel engines [4]. In this context, the authors of [4] emphasize that a total substitution of all American school buses by electric buses would lead

Electronics 2020, 9, 711; doi:10.3390/electronics9050711 www.mdpi.com/journal/electronics Electronics 2020, 9, 711 2 of 18 to an annual mitigation of 5.3 million tons of greenhouse gases (GHG). The reduction of GHG is indispensable for climate protection, especially regarding the historic highest value of energy-related CO2 emissions (33.1 Gt) in 2018 [5]. The International Energy Agency (IEA) highlights in their report [6] from 2019, a new high of CO2 emissions (32.8 billion tons) was caused by fuel combustion in 2017 as well. This report [6] highlights that the transportation sector has a share of 24% of the global CO2 emissions in 2017. The reduction of climate-damaging gases is one of the main tasks relating to public health. Only 8% of the Asian and pacific population live in an environment with healthy air in 2015 [7]. As a result, approximately 4 billion people in this region have to live with health risk based on air pollution. The increasing air pollution is a great challenge for the big metropolises in South and East Asia like Beijing. A look at the current sales volume of electric vehicles and national subsidies programs for electric vehicles demonstrates that the governments of countries in South and East Asia have already recognized this problem. The Chinese government, for example, subsidies electric vehicles like commercial passenger vehicles [8]. These subsidy programs make the purchase of electric vehicles more attractive. For example, the World Bank estimates a stock of 340,982 electric buses for China and 1273 electric buses for Europe, as of summer 2018 [9]. Hence, it can be summarized that China is the market leader for electric buses. This finding can be underlined by the development of Shenzhen’s public transportation system. In December 2017, Shenzhen was the first city worldwide that have already achieved a 100 percent electric bus fleet (16,359 electric buses) [10–12]. These figures underline the important role of China inside the market for electric buses. Nevertheless, two of the main limiting factors of electric buses are the higher costs and the significantly lower range compared to conventional buses—even though it is sometimes just range anxiety [13]. Therefore, many researchers are working to overcome this limitation, in one way or another. The system approach presented in this article is a promising solution to overcome range anxiety by enabling a more precise estimation of energy consumption with regard to range prediction based on precise information about the vehicle weight.

1.2. Project Introduction The largest overhead line infrastructure of Germany is located in Solingen (North Rhine-Westphalia) and it provides a good test field for correspondent technologies and concepts. In 2017, the German Federal Ministry of Transport and Digital Infrastructure (“Bundesministerium für Verkehr und digitale Infrastruktur”) has decided to fund the project “BOB Solingen” with 15 million Euros to decarbonize the urban transportation system of Solingen by replacing diesel units in Solingen’s trolleybus fleet with battery-driven ones as a stand-by, enabling the buses to function on routes without overhead lines. Thereby, the various challenges of a cross-sectoral electrification have to be overcome by new solutions to combine the traffic system with the energy supply system [14]. A project with a quite similar research focus is “SwissTrolley plus”, which is located at Zurich (Switzerland). The project goals are e.g., a vehicle-based energy management and charging strategy and a battery-health conscious operating strategy for maximum lifetime [15,16]. Another example is the project “E-Bus 2020”, which is a cooperation between the Hogeschool van Arnhem en Nijmegen (Netherlands), the Hochschule Niederrhein (Germany) and some industrial partners [17]. The project “BOB Solingen” focuses on the holistic approach of a smart trolley bus system (STS), where each of the battery-overhead-buses (BOB) represents one of many actors to locally manipulate the overhead line load. Figure1 schematically displays the di fferent subprojects, like the stationary storages, bidirectional substations, charging infrastructure and integration of photovoltaic. Electronics 2020, 9, x FOR PEER REVIEW 3 of 18

ElectronicsElectronics 20202020,,9 9,, 711x FOR PEER REVIEW 33 ofof 1818

Figure 1. Schematic of the whole project. Figure 1. Schematic of the whole project. Figure 1. Schematic of the whole project. The intended decarbonization is not only a question of the replacement of trolley buses with supplementaryThe intended diesel decarbonization units, but also of is notthe electric only aity question generation of the for replacement the whole bus of trolleyfleet. In buses addition with The intended decarbonization is not only a question of the replacement of trolley buses with tosupplementary the use of 100% diesel sustainable units, but energy also of (since the electricity 2009 [18]), generation the prospective for the production whole bus fleet.and consumption In addition to supplementary diesel units, but also of the electricity generation for the whole bus fleet. In addition ofthe regenerative use of 100% energy sustainable directly energy at the (since overhead 2009 line [18]), is the already prospective scheduled, production and is going and consumption to be realized of to the use of 100% sustainable energy (since 2009 [18]), the prospective production and consumption byregenerative several photovoltaic energy directly (PV) power at the overheadplants. In linecaseis of already a high energy scheduled, production, and is going where to fully be realized charged by of regenerative energy directly at the overhead line is already scheduled, and is going to be realized BOBseveral are photovoltaicpassing the PV (PV) supply power areas, plants. neither In case the of bus a high battery energy nor production, the overhead where line fully is able charged to absorb BOB by several photovoltaic (PV) power plants. In case of a high energy production, where fully charged theare recuperation passing the PVenergy. supply Therefore, areas, neither bidirectional the bus sub battery stations nor are the going overhead to substitute line is ablethe conventional to absorb the BOB are passing the PV supply areas, neither the bus battery nor the overhead line is able to absorb onesrecuperation to enable energy. an energy Therefore, transport bidirectional to the upstream sub stations grid arelevel going (AC to 10 substitute kV). Additionally, the conventional stationary ones the recuperation energy. Therefore, bidirectional sub stations are going to substitute the conventional batteryto enable storages an energy are transporta second topossibility the upstream to handle grid level temporally (AC 10 kV).peak Additionally, loads in the stationary overhead battery line, ones to enable an energy transport to the upstream grid level (AC 10 kV). Additionally, stationary especiallystorages are during a second high possibility utilization to handleperiods. temporally In order peakto meet loads the in theaspiration overhead for line, an especiallyefficient use during of battery storages are a second possibility to handle temporally peak loads in the overhead line, resources,high utilization the battery periods. storages In order are to built meet up the ou aspirationt of decommissioned for an efficient BOB use batteries, of resources, which the have battery a especially during high utilization periods. In order to meet the aspiration for an efficient use of remainingstorages are State built of upHealth out ofof decommissionedless than 80%. Hence, BOB batteries,they are not which usable have for a remainingtraction applications State of Health any resources, the battery storages are built up out of decommissioned BOB batteries, which have a longer,of less but than are 80%. still Hence,good enough they are to notoperate usable in an for energy traction buffer applications storage. anyThe longer,initial batch but are of stillbatteries good remaining State of Health of less than 80%. Hence, they are not usable for traction applications any comesenough from to operaterepurchased in an energytraction bu batteriesffer storage. from Theother initial buses. batch of batteries comes from repurchased longer, but are still good enough to operate in an energy buffer storage. The initial batch of batteries tractionBesides batteries the system from other preservation buses. components, the energy consumers are the most important comes from repurchased traction batteries from other buses. actorsBesides of the project the system and Solingen’s preservation urban components, transporta thetion energy system. consumers These are are on the the most one hand important the trolley actors Besides the system preservation components, the energy consumers are the most important busesof the (including project and the Solingen’s new BOB as urban well), transportation and on the other system. hand Thesecharging are points on the (connected one hand to the the trolley DC actors of the project and Solingen’s urban transportation system. These are on the one hand the trolley overheadbuses (including lines), which the new are to BOB be asbuilt well), in the and course on the of otherthe research hand charging project, too. points The (connected latter also toinvolves the DC buses (including the new BOB as well), and on the other hand charging points (connected to the DC copingoverhead with lines), challenges which such are to as be time-critical built in the power course fluctuations of the research due project, to the too.sensitive Thelatter overhead also involvesline, as overhead lines), which are to be built in the course of the research project, too. The latter also involves wellcoping as measurements with challenges that such comply as time-critical with legal calibration power fluctuations and billing due regulations. to the sensitive Therefore, overhead adapted line, coping with challenges such as time-critical power fluctuations due to the sensitive overhead line, as andas well intelligent as measurements charging strategies that comply have with to legalbe developed calibration and and implemented billing regulations. to increase Therefore, the all adapted over well as measurements that comply with legal calibration and billing regulations. Therefore, adapted occupancyand intelligent rate and charging efficiency strategies of each have charging to be developedpoint. The andtopics implemented of our chair to are increase all those the that all overare and intelligent charging strategies have to be developed and implemented to increase the all over directlyoccupancy related rate to and the ebus,fficiency as shown of each in Figure charging 2a. point.The first The actual topics bus of is our shown chair in are Figure all those 2b. that are occupancy rate and efficiency of each charging point. The topics of our chair are all those that are directly related to the bus, as shown in Figure2a. The first actual bus is shown in Figure2b. directly related to the bus, as shown in Figure 2a. The first actual bus is shown in Figure 2b.

Battery Overhead Line Bus (BOB) Battery Overhead Line Bus (BOB)

Driver Information Vehicle Mass System Estimation Driver Information Vehicle Mass Energy System Estimation Consumption and FlexibilityEnergy Prediction Consumption and Flexibility Prediction

(a) (b)

FigureFigure 2. 2. (a()a )Vehicle Vehicle based based(a) Research Research Focuses. Focuses. (b (b) )The The first first battery-overhea battery-overhead-busd-bus(b) (BOB) (BOB) of of Solingen. Solingen.

Figure 2. (a) Vehicle based Research Focuses. (b) The first battery-overhead-bus (BOB) of Solingen.

ElectronicsElectronics 20202020,, 99,, 711x FOR PEER REVIEW 44 ofof 1818

1.3. Topic Introduction 1.3. Topic Introduction There are currently four battery-overhead-buses owned by the transport company for active (line Thereoperation) are currently and scientific fourbattery-overhead-buses use. The choice for the test owned track by fell the on transport the route company of the previously for active diesel (line operation)bus line 695, and as scientificplotted in use. Figure The 3a, choice since forit has the a test challenging track fell combination on the route of of total the previously route length diesel and busdistribution line 695, between as plotted overhead in Figure (2.33a, sincekilometers) it has aan challengingd battery (12 combination kilometers) of mode. total routeAs the length route and is a distributionloop, every meter between of altitude overhead has (2.3 to km)be driven and battery both uphill (12 km) and mode. downhill. As the A routedata logger is a loop, with every a separate meter ofGPS altitude module has was to beconnected driven bothto the uphill CAN andbus downhill.of the BOB, A and data then logger the withtest track a separate was traversed GPS module with wasdifferent connected loads. to The the collected CAN bus data of the were BOB, then and analyzed then the testin MATLAB, track was traversedamong others, with ditoff identifyerent loads. the Thefactors collected that influence data were the then range analyzed of the in buses. MATLAB, It is amongimportant others, for tothis identify specific the topic factors to underline that influence the theimpact range of ofhigher the buses. payloads It is on important energy forconsumption. this specific The topic driving to underline force was the calculated impact of higherfrom the payloads torque onand energy the speed consumption. at the output The drivingshaft of force the wastwo calculatedelectric motors. from theIn torqueFigure and3b, the speedmass (in at thetons) output and shafteffective of the energy two electricconsumption motors. including In Figure recuperation3b, the mass (in (in kilowatt-hours) tons) and e ffective for the energy different consumption test drives includinghave been recuperationplotted. An increase (in kilowatt-hours) of mass by about for the 34% diff erentfrom test20.8 drivestons (nearly have been empty plotted. bus, trip An 1) increase to 27.9 oftons mass (full by bus, about trip 34% 5) fromleads 20.8 to tonsan increase (nearly emptyin energy bus, consumption trip 1) to 27.9 tonsfor driving (full bus, by trip about 5) leads the same to an increaseamount. in energy consumption for driving by about the same amount.

(a) (b)

Figure 3.3.( a()a Route) Route of firstof first BOB-Line BOB-Line 695 in Solingen,695 in Solingen, Germany. Germany. (b) Correlation (b) Correlation of mass and of consumption mass and forconsumption the 695 route. for the 695 route.

Further test drives substantiate these findings,findings, although of coursecourse they are subject to slightslight fluctuations,fluctuations, mainly due to publicpublic roadroad tratrafficffic andand drivingdriving style.style. A larger amount of data fromfrom upcoming test drives is necessarynecessary toto smoothsmooth outout thethe curves.curves. Another findingfinding from thesethese testtest drivesdrives werewere thatthat the the currently currently available available mass mass on theon busthe fluctuatesbus fluctuates by more by than more 2 tonsthan with 2 tons constant with payloads,constant whichpayloads, leaves which a lot leaves of room a lot for of improvement. room for improvement.

1.4. Related Work The sections before show the overall goals of thethe BOB project and highlight the influenceinfluence of the vehiclevehicle mass on needed traction traction energy energy and and therefore therefore on on the the range range of of the the buses. buses. One One of of these these goals goals is isthe the vehicle vehicle mass mass detection detection of of electric electric buses buses by by th thee analysis analysis of of vehicles vehicles tires and air suspensionsuspension systems.systems. The following sections present the commoncommon methodologies for vehicle mass detectiondetection andand somesome selectedselected applicationapplication examples,examples, whichwhich useuse thethe informationinformation aboutabout thethe currentcurrent vehiclevehicle mass.mass. InIn thethe lastlast few years, interest in the topic of vehicle mass detection has experienced an upswing. TheThe mainmain driversdrivers forfor thisthis developmentdevelopment areare autonomousautonomous andand electricelectric vehiclesvehicles andand thethe improvementimprovement of roadroad safety.safety. The information about the current vehicle mass can be usedused toto improveimprove advancedadvanced driver assistantassistant systemssystems (see (see Section Section 1.5 1.5).). The The common common applied applied methodologies methodologie cans can be dividedbe divided into into the twothe two categories: categories: “static” “static” and and “dynamic”. “dynamic”. Static Static methods methods are ableare able to detect to detect the vehiclethe vehicle mass mass while while the velocitythe velocity is equal is equal tozero. to zero. Compared Compared to to this, this, dynamic dynamic methods methods calculate calculate the the vehicle vehicle mass mass whilewhile thethe velocityvelocity isis greatergreater thanthan zero.zero. Therefore, one advantage of the static method is thethe knowledgeknowledge of thethe current vehicle mass before the journey starts. For example, the dynamic method presented in [19]

Electronics 2020, 9, 711 5 of 18 current vehicle mass before the journey starts. For example, the dynamic method presented in [19] needs approximately 80 s to reach vehicle mass values, which are close to the real mass. This is longer than the mean driving time between two urban bus stops, which is less than a minute for this route, and thus not practical. In addition, static methods are able to detect the vehicle mass distribution, which is a great advantage compared to other methods. The need for extra sensors, like high-sensitive pressure sensors, is one disadvantage of the static method. The dynamic method usually obtained the necessary data from the sensors of modern vehicles and thus, this method can be cheaper compared to the static approach. The application of sensors to detect the suspension deflection is one common static method and is used in [20]. In addition, further methods, which analyze different tire parameters (tire contact area, tire pressure etc.), have already been presented in [21,22]. These methods have been developed for the application at passenger vehicles. Our method is developed for the application at electric buses and needs to handle some challenges (e.g., moving people), which do not occur in passenger vehicles. In summary, it can be said that no static methods for electric buses which use tire pressure sensors for vehicle mass estimation are known. The dynamic method presented in [23] uses acceleration sensors to estimate the vehicle mass. A further system [24] applies a state-parameter observer that analyzes the signals of suspension deflection sensors and a vertical accelerometer. Hence, this method combines the strength of both approaches (static and dynamic). The parameters obtained by the dynamic method can also be used to train neural networks for vehicle mass and road grate estimation (e.g., for heavy-duty vehicles) [25]. The analysis of the vehicle’s longitudinal and lateral motion is also a common dynamic method to estimate the vehicle mass [26]. The preconditions for the method presented in [26] are a vehicle acceleration with a small angle (longitudinal motion) and the turn of the vehicle at constant longitudinal speed (lateral motion). This means, if the preconditions are not fulfilled, the accuracy of vehicle mass may not be satisfactory. In the context of the system approach shown in this article, the inventors of the patent [27] analyze the tire contact area and tire forces, while the velocity is greater than zero to estimate the vehicle mass. Hence, tire pressure sensors can also be helpful by the application of dynamic methods.

1.5. Scope of Application The results of the energy consumption measurements of the BOB on line 695 regarding different payloads show that the mass has a significant influence on the consumption and thus the range of vehicles. This finding can also be confirmed by further science publications [21,28,29]. If this influence can be quantified, it can be handled in many cases. However, as stated in [30], in the majority of vehicles no information on mass is available. The energy consumption of vehicles is mainly based on the aerodynamic resistance force, rolling resistance force, climbing resistance force, and acceleration resistance force. Besides the aerodynamic resistance force, all other resistance forces depend on the vehicle mass [28,31]. Thus, a precise range prediction without the knowledge on current vehicle mass is not feasible. In [32], a method for online mass estimation for electric vehicles is presented. The aim of this method is to realize an optimal charging schedule for electric vehicles based on relevant information like the payload of the vehicle. A further example for the improvement of the range prediction of electric vehicles based on the knowledge of the vehicle mass is shown in [28]. The estimation of the vehicle mass can be also combined with road slope detection [25]. Both parameters are relevant for gearshift controlling systems [23]. In recent years, the range of electric vehicles has increased. Nevertheless, the charging time is still greater compared to refueling conventional vehicles. Hence, the above presented examples highlight that the vehicle mass is an important parameter to optimize the utilization of the energy stored in the high voltage battery of electric vehicles. Furthermore, knowledge about mass and mass distribution would improve different safety aspects, ranging from improvements in adaptive cruise control to further safety systems like rollover Electronics 2020, 9, 711 6 of 18 mitigation [33,34]. For example, the knowledge about current vehicle mass can be used to enhance the control performance of autonomous and electric vehicles [35]. The topography, breaking and acceleration events, and routes with many curves can lead to a load transfer. Hence, if the vehicle mass as well as the load transfer is known, the control of autonomous vehicles for emergency maneuvers can be optimized as well [36]. For vehicle dynamic controls and the trajectory of autonomous vehicles, different methods have been already introduced in the last few years. Some of these methods use the so-called model predictive control (MPC). The authors of [37] emphasize that these methods need many parameters, like tire data and payload. Therefore, the system presented in this article can be a helpful tool to improve the dynamic control of autonomous vehicles by tire pressure monitoring and vehicle mass calculation, respectively. A lane keeping system (LKS) is a further application example that may benefit of the information about the vehicle mass [38]. Furthermore, safety aspects are not the only motivation for the estimation of the vehicle mass. The driving comfort can also benefit by the application of the systems mentioned in this section [24]. A further application example can be the vehicle mass estimation of electric . The vehicle mass of trucks can vary up to approximately 400% [24]. Hence, electric trucks can benefit by these systems as well. Thus, the growing spread of electric vehicles and the great number of activities of automobile manufactures in the field of autonomous vehicles show the high relevance of systems for vehicle mass detection regarding future mobility. In the European Union [39] and the United States of America [40] tire pressure monitoring systems are required for by law to enhance road safety and to reduce GHG emissions. In the case that this regulation may be expanded to buses and trucks, the presented system for tires could become even more attractive for the manufacturers of buses and commercial vehicles, as it can fulfil the regulation, while offering the additional advantages that come with knowledge about mass and its distribution.

2. Materials and Methods Now that the indispensability of the vehicle mass for an accurate range prediction—and therefore the whole smart-trolley-system, as well as all the other scopes of application—has been clarified, the three investigated approaches to mass estimation will be briefly presented. This will be followed by some details regarding the used hardware and software for the results presented in this article.

2.1. Overall Concept The general goal of this study was to provide information on vehicle mass as fast, reliably and precisely as possible, and with the most cost-efficient hardware. In terms of sensor fusion, a three-layered concept was pursued, as shown in Figure4 and described below.

2.1.1. Dynamic Mass Estimation This method only needs information provided by the bus’s already existing sensors, like motor torque and speed. By solving the dynamic driving equations for the mass and filtering out implausible pairs of data using a state observer, it should be possible to estimate a converging mass—given there are enough high dynamic events. Testing has currently started with the first available yet limited datasets. The data include measurement values of, for instance, motor torque and speed, and is compared to the expected results from the physical traction force equations containing the acceleration force and other forces and variables. Nevertheless, as these neither are the main topic of this article nor are needed for comprehension, they will not be considered further. Electronics 20202020,, 99,, 711x FOR PEER REVIEW 7 of 18

Figure 4. Overview of the combined vehicle mass detection system. Figure 4. Overview of the combined vehicle mass detection system. 2.1.2. Time-of-Flight (ToF) Passenger Counting 2.1.2.This Time-of-Flight approach is (ToF) based Passenger on the application Counting of an advanced automatic passenger counting (APC) system.This Most approach APC-systems is based on are the based application on simple of an light advanced barriers. automatic But there passenger are also widespread counting (APC) and similarlysystem. Most priced APC-systems systems that are use based ToF-Sensors. on simple These light have barriers. the ability, But there in addition are also to merelywidespread counting, and tosimilarly also record priced the systems height that profile use of ToF- persons,Sensors. which These allows, have the at least ability, statistically, in addition conclusions to merely aboutcounting, the approximateto also record weight. the height Since profile such aof system persons, was which to be installedallows, at anyway, least statistically, it is self-evident conclusions to have about chosen the a ToF-basedapproximate one. weight. Initial Since tests insuch our a laboratorysystem was have to be shown installed the reliabilityanyway, it but is self-evident also the inaccuracies to have chosen of the system.a ToF-based Further one. tests Initial will tests be madein our tolaboratory determine have the possibleshown the accuracy reliability by usingbut also more the complexinaccuracies image of analysisthe system. methods. Further tests will be made to determine the possible accuracy by using more complex image analysis methods. 2.1.3. Static Payload Detection 2.1.3.With Static cost Payload efficient Detection sensor units, developed by ourselves, and custom tailored algorithms, it is possibleWith cost to efficient convert sensor small units, changes developed in tire orby air ourselves, suspension and pressurecustom tailored into payloads. algorithms, This it is apossible reliable to approach convert alreadysmall changes successfully in tire tested or air on susp carension tires with pressure the highest into payloads. stand-alone This potential is a reliable [41]. Theapproach feasibility already for busessuccessfully was doubted tested foron somecar tires reasons with explained the highest later, stand-alone but as this articlepotential will [41]. show, The it isfeasibility also a promising for buses approach was doubted here. for A some combination reasons withexplained both oflater, the aforementionedbut as this article approaches will show, atit is a lateralso a time promising should approach even further here. increase A combination the accuracy with andboth reliability of the aforementioned of the static mass approaches detection. at a later time should even further increase the accuracy and reliability of the static mass detection. 2.2. Used Hardware 2.2. UsedThe Hardware sensor units used for all the presented measurements on the bus are based on the high resolution pressure sensor module MS5803, consisting of a piezo-resistive pressure sensor and a The sensor units used for all the presented measurements on the bus are based on the high 24 bit analog-to-digital-converter (ADC). Two variants of the sensor are used. One that delivers up to resolution pressure sensor module MS5803, consisting of a piezo-resistive pressure sensor and a 24 0.04 millibar resolution in the necessary range of up to 7 bars for the air suspension and one with up bit analog-to-digital-converter (ADC). Two variants of the sensor are used. One that delivers up to to 0.2 millibar resolution that can withstand up to 14 bars for the tires. Wired to the ESP8266 based 0.04 millibar resolution in the necessary range of up to 7 bars for the air suspension and one with up microcontroller board Wemos D1 Mini and glued into custom-made extensions, pressure data to 0.2 millibar resolution that can withstand up to 14 bars for the tires. Wired to the ESP8266 based microcontroller board Wemos D1 Mini and glued into custom-made valve extensions, pressure data

Electronics 2020, 9, 711 8 of 18 Electronics 2020, 9, x FOR PEER REVIEW 8 of 18 can be collected using SPI, sent via Wi-Fi and stored in an SQL-database. The tire sensor prototype, can be collected using SPI, sent via Wi-Fi and stored in an SQL-database. The tire sensor prototype, as can be seen in Figure5a, is especially designed for hassle free testing during the development stage as can be seen in Figure 5a, is especially designed for hassle free testing during the development stage to overcome the need for tire changes and to allow easy debugging. In a further stage, the transition to to overcome the need for tire changes and to allow easy debugging. In a further stage, the transition a Bluetooth 5 Low Energy based microcontroller will be made in favor of the significantly reduced to a Bluetooth 5 Low Energy based microcontroller will be made in favor of the significantly reduced energy consumption and smaller form factor. Moreover, after finished development the tire sensor energy consumption and smaller form factor. Moreover, after finished development the tire sensor unit could be mounted on the rim inside the tire, as most standard tire pressure monitoring systems do. unit could be mounted on the rim inside the tire, as most standard tire pressure monitoring systems The air suspension sensor shown in Figure5b was a first attempt to minimize the sensor size. do. The air suspension sensor shown in Figure 5b was a first attempt to minimize the sensor size.

(a) (b)

Figure 5. Attached Sensor Prototypes. ( a)) Tire Tire Sensor. ( b) Air Suspension Sensor. 2.3. Filter Algorithm 2.3. Filter Algorithm The idea behind the algorithm is to detect a step in the pressure inside the air suspension or tire The idea behind the algorithm is to detect a step in the pressure inside the air suspension or tire and, depending on the height of the step, the mass is calculated. The algorithm starts by calculating and, depending2 on the height of the step, the mass is calculated. The algorithm starts by calculating the variance s of the last nvar time steps. The running variance is computed by the two-pass algorithm, the variance 𝑠 of the last 𝑛 time steps. The running variance is computed by the two-pass which, in the first step, computes the mean pressure algorithm, which, in the first step, computes the mean pressure Xn 1 var p =𝑝̅ ∑ p𝑝i,, (1) nvar i=1

where 𝑝 is the respective pressure value and 𝑛 is the count of pressure values. This is followed where pi is the respective pressure value and nvar is the count of pressure values. This is followed by calculationby calculation of theof the variance variance 1 Xnvar 2 = ( )2 s 𝑠 ∑ 𝑝pi 𝑝p ̅ .. (2) nvar1 i=1 − − 2 In thethe nextnext step,step, itit is is checked checked if if that that variance variance exceeds exceeds the the bound bounds lim𝑠 , and, and if it if does, it does, a counter a countertc is triggered𝑡 is triggered and the and time thet timej is saved 𝑡 is forsaved later for reference. later reference. The counter The countertc is incremented, 𝑡 is incremented, while the while variance the 2 ofvariance the pressure of the ispressure above the is boundabove thes lim bound. As soon 𝑠 as. the As variancesoon as the drops variance below thedrops bound, below the the counter bound, is checkedthe counter to see is checked if it has beento see active if it has for been a minimum active for duration a minimum of tj,min duration. If the counterof 𝑡, has. If the not counter been active has fornot abeen suffi activecient time,for a sufficient it will reset time, to zero, it will and reset the to algorithm zero, and starts the algorithm checking starts the variance checking for the deviations variance again.for deviations If the counter again. was If the active counter for a timewas tactivej,min, the for mean a time value 𝑡,pstart, theof mean the step value position 𝑝̅ is of saved. the step For theposition next t ism timesaved. steps, For the meannext 𝑡 value time is calculated.steps, the mean Once valuetm time is stepscalculated. have beenOnce reached, 𝑡 time the steps absolute have dibeenfference reached, of the the two absolute mean difference values for of the the start two and mean end values position for ofthe the start step and are end compared position of the step are compared Xtj+tc+tm Xtj ∆p = p p = p p . (3) end start = i = i ∆𝑝 ̅ 𝑝 − 𝑝̅ i∑ tj tc 𝑝− ∑i tj tm 𝑝. ± − (3)

If the difference ∆𝑝 did not exceed the minimum value for a step 𝑝, the values for step position and the mean values are deleted. Otherwise, (if the difference is greater or equal than the

Electronics 2020, 9, 711 9 of 18 Electronics 2020, 9, x FOR PEER REVIEW 9 of 18

If the difference ∆p did not exceed the minimum value for a step pmin, the values for step position bound 𝑝) the mass mpayload that was the cause of the step is calculated with the measurement- and the mean values are deleted. Otherwise, (if the difference is greater or equal than the bound based quadratic equation pmin) the mass mpayload that was the cause of the step is calculated with the measurement-based quadratic equation 𝑚 𝑐𝑜𝑛𝑠𝑡 ⋅∆𝑝 𝑐𝑜𝑛𝑠𝑡 ⋅∆𝑝. (4) 2 m = consta ∆p + const ∆p. (4) At the same moment, the algorithmpayload starts ·again and isb ·ready to detect the next entry. The constantsAt the vary same with moment, chassis the and algorithm tire type. starts For again largely and varying is ready toinitial detect tire the pressures, next entry. another The constants linear varycorrection with chassisfactor can and be tire added type. to For the largelywhole varyingequation initial. Derived tire from pressures, the experiences another linear with correction cars, it is expectedfactor can that be added inclusion to the of wholeall 10 tires equation. of the Derived bus will from increase theexperiences the reliability with of cars,step detection it is expected and thatthe accuracyinclusion of allmass 10 tiresdetection. of the The bus willsame increase filter can the be reliability applied ofto step the detectionair suspensions and the pressure accuracy curve of mass as well.detection. The same filter can be applied to the air suspensions pressure curve as well.

3. Results Results This chapter introduces and discusses the result resultss of the tire and air suspensionsuspension measurements separately, beforebefore finallyfinally comparingcomparing themthem inin thethe discussiondiscussion chapter.chapter.

3.1. Measurements Measurements on the Tire This sectionsection isis usedused to to present present the the tire tire measurement measurement results. results. Where Where applicable, applicable, the the diff erencesdifferences and andsimilarities similarities to previous to previous tests withtests carswith will cars be will shown, be shown, and topics and topics that need that further need further investigation investigation will be pointedwill be pointed out. out.

3.1.1. Influence Influence of Initial Pressure Increasing thethe initialinitial pressure pressure of of the the tire tire has has already already led led to a to noticeable a noticeable reduction reduction in pressure in pressure steps stepsby cars by (see cars Figure (see Figure6a). 6a).

(a) (b)

Figure 6. (a) InfluenceInfluence ofof initial initial pressure pressure on on height height of of mass mass induced induced pressure pressure steps, steps, sum sum of all of four all four tires ().tires (car). (b) Influence (b) Influence of seat of positionseat position on pressure on pressure changes changes (car). (car).

As the initial pressure of bus tire tiress is three times higher and the re resolutionsolution of the pressure sensor for this larger area is less accurate,accurate, only one sensorsensor was carefully manufactured and tested in the beginning. Therefore, the results discusseddiscussed in detail below exceeded expectations.

3.1.2. Influence Influence of Entry Position As seats, doors and tires are not symmetrically aligned, the induced pressure changes vary with the entry and seat position, as can be seen in Figure6 6b.b. Fortunately,Fortunately, inin standardstandard passengerpassenger carscars thethe entry position cancan easilyeasily bebe identified,identified, as as the the pressure pressure change change in in the the diagonally diagonally opposite opposite tire tire (related (related to tothe the seat-position) seat-position) is significantly is significantly lower lower than that than in allthat other in tires.all other To explain tires. theTo pressureexplain the distributions, pressure distributions,Figure7b presents Figure a schematic 7b presents drawing a schema of thetic drawing involved of vehicle the involved parts. vehicle parts.

Electronics 20202020, 9, x 711 FOR PEER REVIEW 1010 of 18 18

(a) (b)

(c)

Figure 7. ((aa)) Differences Differences in in total total pressure pressure change change for for front front and and rear rear seats, seats, sum sum of of all all four four tires tires (car). (car). (b) Seat and tire positions (car) (car).. ( (cc)) Arrangement Arrangement and and numbering numbering of of tires, tires, air springs and doors (bus) (bus).. The rear seats are much closer to the rear tires, whereas the front seats are placed more centrally The rear seats are much closer to the rear tires, whereas the front seats are placed more centrally between the front and rear tires. This results in significantly greater pressure steps for the respective between the front and rear tires. This results in significantly greater pressure steps for the respective rear tire, if somebody sits down on the back seats. For the front seats, changes in tire pressure are more rear tire, if somebody sits down on the back seats. For the front seats, changes in tire pressure are evenly distributed. more evenly distributed. In order to allow a precise conversion of the detected steps into weights, this is essential information In order to allow a precise conversion of the detected steps into weights, this is essential for the further development of the algorithm, and must therefore be observed. Even in the sum of information for the further development of the algorithm, and must therefore be observed. Even in all tire pressures, the entry position is not negligible. For example, have a look at the dotted bars in the sum of all tire pressures, the entry position is not negligible. For example, have a look at the dotted the middle of Figure7a. Without knowledge about the entry position, it is not possible to distinguish bars in the middle of Figure 7a. Without knowledge about the entry position, it is not possible to between a 72-kg person in the front row and an 81-kg person in the back of a car. With ten tires, three distinguish between a 72-kilogram person in the front row and an 81-kilogram person in the back of doors on the right side of the trolleybus and simultaneous entrances and exits in any combination, a car. With ten tires, three doors on the right side of the trolleybus and simultaneous entrances and this will be an even bigger challenge for the bus. If a filter alone cannot solve this satisfactorily, it is exits in any combination, this will be an even bigger challenge for the bus. If a filter alone cannot possible to combine the system with an automatic passenger counting system. Figure7b illustrates the solve this satisfactorily, it is possible to combine the system with an automatic passenger counting tire and seat positions for the reference car, and Figure7c illustrates the tire, air suspension and door system. Figure 7b illustrates the tire and seat positions for the reference car, and Figure 7c illustrates positions of the bus and how they will be referenced. The tire sensor unit was attached to the front the tire, air suspension and door positions of the bus and how they will be referenced. The tire sensor right tire (FR) of the bus, next to the front door (FD). In the upper part of Figure8, the raw and filtered unit was attached to the front right tire (FR) of the bus, next to the front door (FD). In the upper part pressure values from tire FR are shown for five successive entries, each by the front and middle door, of Figure 8, the raw and filtered pressure values from tire FR are shown for five successive entries, of a 72-kg person. The lower part of Figure8 shows the detected changes in tire pressure, determined each by the front and middle door, of a 72-kilogram person. The lower part of Figure 8 shows the by the aforementioned algorithm, which can then be converted into weight. The payload-induced detected changes in tire pressure, determined by the aforementioned algorithm, which can then be pressure-steps can be clearly distinguished from measurement noise. All entries presented in Figure8 converted into weight. The payload-induced pressure-steps can be clearly distinguished from measurement noise. All entries presented in Figure 8 were made by the same person, but as the front

Electronics 2020, 9, x FOR PEER REVIEW 11 of 18

door is closer to the front tire, the induced pressure steps differ in height. This leads, as already mentioned before, to the need for a filter, which is able to distinguish between the different entry positions, either based only on the pressure values of all ten tires or with the help of a passenger counting system. Electronics 2020, 9, 711 11 of 18 3.1.3. Proportionality of Pressure Steps and Weight Electronics 2020, 9, x FOR PEER REVIEW 11 of 18 were madeFurther by the tests same were person, performed but asto thedetermine front door the de istectability closer to theof different front tire, and the especially induced lower pressure stepsdoorweights. differ is in closer In height. order to theto This be front able leads, tire, to estimate as the already induced the mentioned poss pressureible accuracy, steps before, differ it to must the in height.be need determined for This a filter,leads, how which as much already is the able to distinguishmentipressureoned between steps before, of different the to the diff erentneed payloads for entry a differfilter, positions, fromwhich each is either able other. basedto Fordistinguish this only reason, on be thetween the pressure bus the was different valuesloaded entry with of all ten different weights and test persons. The averaged results are shown in Figure 9. They are used to tires orpositions, with the either help based of a passengeronly on the counting pressure system.values of all ten tires or with the help of a passenger countingdetermine system. the constants of the pressure step to mass conversion part of the algorithm.

3.1.3. Proportionality of Pressure Steps and Weight Further tests were performed to determine the detectability of different and especially lower weights. In order to be able to estimate the possible accuracy, it must be determined how much the pressure steps of different payloads differ from each other. For this reason, the bus was loaded with different weights and test persons. The averaged results are shown in Figure 9. They are used to

determineRight Tire Front the constants of the pressure step to mass conversion part of the algorithm. Pressure p [mbar]

(a)

p [mbar] Jump Height (a)

(b)

FigureFigure 8. Raw 8. andRaw filteredand filtered data curvesdata curves (a) and (a) corresponding and corresponding detected detected pressure pressure steps steps from thefrom algorithm the (b) foralgorithm five entries (b) for each five in entries the front each andin the middle front and door middle (bus). door (bus).

3.1.3. Proportionality of Pressure Steps and Weight Further tests were performed to determine the detectability of different and especially lower weights. In order to be able to estimate the possible(b) accuracy, it must be determined how much the pressure steps of different payloads differ from each other. For this reason, the bus was loaded with different weightsFigure 8. Raw and and test filtered persons. data Thecurves averaged (a) and corresponding results are showndetected inpressure Figure steps9. They from are the used to determinealgorithm the constants (b) for five of entries the pressure each in the step front to and mass middle conversion door (bus). part of the algorithm.

Figure 9. Pressure values for different payloads (bus).

FigureFigure 9. 9.Pressure Pressure valuesvalues for different different payloads payloads (bus) (bus)..

Electronics 2020, 9, x FOR PEER REVIEW 12 of 18 Electronics 2020, 9, 711 12 of 18 3.1.4. Estimation of the Lowest Detectable Payload

3.1.4.With Estimation the sensor of the mounted Lowest to Detectable the front right Payload tire, it was feasible to detect pressure steps for weights as low as 10 kilograms occurring in the front door. Reliable results for the middle door were achieved With the sensor mounted to the front right tire, it was feasible to detect pressure steps for weights for weights of more than 30 kilograms. The raw and filtered data are shown in Figure 10a,b. As can as low as 10-kg occurring in the front door. Reliable results for the middle door were achieved for be seen, there is a detectable large difference between 10 and 20-kilogram payloads in the front door. weights of more than 30-kg. The raw and filtered data are shown in Figure 10a,b. As can be seen, In Figure 10c,d the noticeable large difference between the steps of the 30 kilogram payload in there is a detectable large difference between 10 and 20-kg payloads in the front door. the front and middle door can be observed. Front Right Tire Front Right Tire Pressure p [mbar] p Pressure [mbar] p Pressure

(a) (b)

8651 Raw Data Filtered 8650.5

8650

Front Right Tire 8649.5 Front Right Tire Pressure p [mbar] Pressure p [mbar]

8649 12.5 25 37.5 50 62.5 Time [s]

(c) (d)

Figure 10. Raw and filtered data for small payloads (bus). (a) Ten kilogram front door. (b) Twenty Figure 10. Raw and filtered data for small payloads (bus). (a) Ten kilogram front door. (b) Twenty kilogram front door. (c) Thirty kilogram front door. (d) Thirty kilogram middle door. kilogram front door. (c) Thirty kilogram front door. (d) Thirty kilogram middle door. In Figure 10c,d the noticeable large difference between the steps of the 30-kg payload in the front 3.2. Measurements on the Air Suspension and middle door can be observed. In this section, the suspension measurement results are presented and discussed. The bus 3.2.consists Measurements of three axes. on the Each Air Suspension is suspended on the left and right by an air , as can be seen in FigureIn 7c. this Although section, the they suspension are all fed measurement from the same results compressor, are presented they have and very discussed. different The air bus pressures, consists ofranging three axes.from Each2.7 to axle 5.8 isbar suspended (absolute, on while the leftleveled). and right In addition, by an air the spring, right as side can of be the seen bus in Figure(with the7c. Althoughdoors) is often they lowered are all fed at fromthe stops the same by reducing compressor, the pressure they have there, very to di makefferent boarding air pressures, easier. rangingAs will frombe shown, 2.7 to this 5.8 barhas (absolute,a considerable while influence leveled). on In addition,the height the and right recognizability side of the bus of the (with pressure the doors) steps. is oftenOnly loweredone air suspension at the stops sensor by reducing was made the pressurefor this firs there,t feasibility to make boardingtest. In order easier. to be As able will to be make shown, a thisstatement has a considerableabout the pressure influence changes on the heightin the andindi recognizabilityvidual air bellows, of the the pressure sensor steps. was Onlyinstalled one airin suspensionsuccession sensoron all wassix air made suspensions, for this first and feasibility the en test.tries In were order made to be ableas similar to make as a possible. statement Using about thesuperposition, pressure changes the relationship in the individual between air all bellows, entry positions the sensor and was all installedair suspensions in succession can be on derived all six for air suspensions,the two test persons and the of entries 70 and were 105 madekilograms as similar in weight, as possible. respectively. Using superposition, the relationship

Electronics 2020, 9, 711 13 of 18 between all entry positions and all air suspensions can be derived for the two test persons of 70 and 105-kg in weight, respectively. Electronics 2020, 9, x FOR PEER REVIEW 13 of 18 3.2.1. Distribution among the Individual Air Springs 3.2.1. Distribution among the Individual Air Springs For the air suspensions, it is also advisable to start by looking at the distribution of the load For the air suspensions, it is also advisable to start by looking at the distribution of the load respective to the pressure on the individual air springs. Therefore, the four diagrams of Figure 11 show respective to the pressure on the individual air springs. Therefore, the four diagrams of Figure 11 the detected changes in pressure broken down to the individual air springs for both persons and both show the detected changes in pressure broken down to the individual air springs for both persons states (leveled, lowered) of the bus in comparison. Figure 11a shows the detected pressure steps for and both states (leveled, lowered) of the bus in comparison. Figure 11a shows the detected pressure the entry of a 70-kg person through each of the three doors, split up to the individual suspensions. steps for the entry of a 70 kilogram person through each of the three doors, split up to the individual Interestingly, the biggest pressure change when entering through the front door is not at the front, suspensions. Interestingly, the biggest pressure change when entering through the front door is not but at the middle air suspension. at the front, but at the middle air suspension.

(a) (b)

(c) (d)

Figure 11. ResultingResulting pressure pressure changes in in the the respective respective air air suspensions suspensions for for two two loads loads and and conditions. conditions. (a) Seventy kilograms, leveled. ( b) One-hundred-and-five One-hundred-and-five kilograms, leveled. ( (cc)) Seventy Seventy kilograms, kilograms, lowered. ( (d)) One-hundred-and-five One-hundred-and-five kilograms, lowered.

It can also be noted that the total pressure change across all air suspensions for a front entry is It can also be noted that the total pressure change across all air suspensions for a front entry is substantially larger than for the other two entry positions. In Figure 11b, it can be seen that it is basically substantially larger than for the other two entry positions. In Figure 11b, it can be seen that it is the same for a heavier person with regard to the distribution, only of course with correspondingly basically the same for a heavier person with regard to the distribution, only of course with larger pressure changes. It should also be noted that the left side of the bus (opposite the doors) is correspondingly larger pressure changes. It should also be noted that the left side of the bus (opposite partially relieved, and this even makes a negative pressure change noticeable there. If the bus is the doors) is partially relieved, and this even makes a negative pressure change noticeable there. If the bus is lowered to make it easier to get in, the load-related pressure changes are much smaller, as can be seen in Figure 11c and Figure 12a,b. The cause of this is very likely the lower initial pressure, but it is still to be determined completely. The distribution is also worth mentioning as it changes. Due to the slope to the right, the air suspensions there are now subjected to greater loads. However, the total pressure change drops to about a third or even less, depending the entry position. It also

Electronics 2020, 9, x FOR PEER REVIEW 14 of 18 remains to be seen how reproducible this is, due to the fact that the lowering process can be non- binary. As Figure 11d suggests, the relative ratio between the two loads also remains lowered as expected.

Electronics 2020, 9, 711 14 of 18 3.2.2. Influence of Lowering A look at the raw data in Figure 12b reveals that the pressure is much more restless shortly after lowered to make it easier to get in, the load-related pressure changes are much smaller, as can be seen the sudden change in pressure due to the lowering, which makes it more difficult to detect changes, in Figures 11c and 12a,b. The cause of this is very likely the lower initial pressure, but it is still to be as the payload-induced steps are smaller and the base pressure more volatile. Lowering does only determined completely. The distribution is also worth mentioning as it changes. Due to the slope to marginally affect tire pressure, as can be seen in comparison in Figure 12c,d. While leveled, the first the right, the air suspensions there are now subjected to greater loads. However, the total pressure entry of the 70 kilogram test person at the front door results in a pressure change of about 5 millibar change drops to about a third or even less, depending the entry position. It also remains to be seen in the front right air suspension and 2 millibar in the front right tire. Whereas, while lowered, the how reproducible this is, due to the fact that the lowering process can be non-binary. As Figure 11d value of the air suspension decreases so much that it is no longer detectable, while the value from the suggests, the relative ratio between the two loads also remains lowered as expected. tire remains nearly the same.

(a) (b)

(c) (d)

Figure 12. Influence of lowering on suspension and tire pressure. (a) Front Right Suspension, leveled. Figure 12. Influence of lowering on suspension and tire pressure. (a) Front Right Suspension, leveled. (b) Front Right Suspension, lowered. (c) Front Right Tire, leveled. (d) Front Right Tire, lowered. (b) Front Right Suspension, lowered. (c) Front Right Tire, leveled. (d) Front Right Tire, lowered. 3.2.2. Influence of Lowering 3.2.3. Influence of Entry Position A look at the raw data in Figure 12b reveals that the pressure is much more restless shortly after the suddenConsidering change the in total pressure pressure due tochanges the lowering, summed which up over makes all it six more air disuspensionsfficult to detect, as shown changes, in asFigure the payload-induced13, one can conclude steps that are the smaller gap between and the basethe sum pressure of all more pressure volatile. changes Lowering for the does different only marginallytest persons a ffisect large tire enough pressure, to as safely can bedistinguish seen in comparison both persons in Figure from another12c,d. While. This leveled,applies theto both first entrystates ofof the 70-kgbus and test all person doors, atalthough the front not door to the results same in extent a pressure. The summed change of up about pressure 5 millibar changes in thefor frontentries right at the air front suspension door are and substanti 2 millibarally in larger the front than right those tire. from Whereas, the middle while or lowered, rear door. the value of the air suspension decreases so much that it is no longer detectable, while the value from the tire remains nearly the same. Electronics 2020, 9, 711 15 of 18

3.2.3. Influence of Entry Position Considering the total pressure changes summed up over all six air suspensions, as shown in Figure 13, one can conclude that the gap between the sum of all pressure changes for the different test persons is large enough to safely distinguish both persons from another. This applies to both states of the bus and all doors, although not to the same extent. The summed up pressure changes for entries at

Electronicsthe front 20 door20, 9, arex FOR substantially PEER REVIEW larger than those from the middle or rear door. 15 of 18

Figure 13. OverviewOverview of of all all accumulated accumulated pressure pressure changes changes from from all all six air suspensions for two test persons and all three doors.doors.

44.. Discussion Discussion SectiSectionon 1.3 presentedpresented thethe huge huge impact impact of of vehicle vehicle mass mass on on consumption, consumption and, and therefore therefore the the need need for forprecise precise information information about about the the current current mass mass of theof the bus bus for for being being able able to maketo make forecasts forecasts on on range range or orpossible possible excess excess energy energy for for other other means. means. The The subsequent subsequent section sectionthen then gavegave anan overviewoverview ofof existing methods for for mass mass detection detection for for other other vehicles vehicles,, and and explain explaineded why why they they are areunsuitable unsuitable for b foruses buses.. For thForat thatreason, reason, thisthis article article examine examinedd if an if anapproach approach to todetect detect single single person person entries entries using using tire tire pressure pressure from carscars cancan bebe adapted adapted to to work work with with buses, buses and, and compared compare itd to it an to approach an approach well knownwell known from heavyfrom heavyduty vehicles duty vehicles to originally to originally detect detect larger larger changes chan inges payloads in payloads using using air suspension air suspension monitoring. monitoring. To Tosummarize summarize the findingsthe findings briefly: briefly whereas: whereas the main the advantagemain advantage of the mass of the inducted mass inducted pressure pressure changes changesfrom the from air suspensions the air suspensions is that they is that are severalthey are times several larger times than larger those than from those the tires,from andthe tires therefore, and thereforebetter accuracy better canaccuracy be assumed, can be theirassumed disadvantage, their disadvantag is that theye is greatly that they decrease greatly and decrease are much and more are muchvolatile more when volatile the bus when is lowered. the bus In is other lowered. words, In the other pressure words, steps the in pressure the tires steps are generally in the tires smaller are generallybut also more smaller stable. but Therefore, also more there stable. is noTherefore, real winner there in thisis no category—before real winner in furtherthis category measurements.—before furtherWhen it measurements. comes to power When supply, it comes no matter to power how andsupply, where no the matter tire pressurehow and sensors where the will tire be mountedpressure sensorsdue to thewill be mounted turning, due the to powerthe wheels needs turning to come, the from power the needs battery, to come and thefrom data the must battery be, sentand thewirelessly. data must The be radio sent wirelessly technology. The needs radio to betechnology switched n fromeeds Wi-Fito be switched to Bluetooth from Low Wi-F Energyi to Bluetooth or ISM Lowbands, Energy to ensure or ISM an acceptablebands, to ensure battery an life. acceptable This was battery planned life. anyway, This was but planned of course anyway, the technical but of coursefeasibility the checktechnical had feasibility priority. Thecheck connectors had priority. to the The air connectors suspensions, to the on theair sus otherpensions hand,, are on lockedthe other in handplace, andare locked overcome in place this limitation,and overcome as they this could limitation be wired, as they to the could bus’s be battery. wired to However, the bus’s it battery should. However,not be left unmentionedit should not thatbe left the unmentioned tire pressure sensorsthat the additionally tire pressure off sensorser their protectiveadditionally function offer their with proteregardctive to monitoring function with the regard formation to monitoring of flat or burst the tires.formation Both of systems flat or canburst off ertires information. Both systems on mass can offerdistribution information and thus on mass enhance distribution vehicle safety and functions,thus enhance as mentioned vehicle safety in Section functions 1.5. In, as order mentioned to be able in Stoection make 1.5 a well-founded. In order to be statement able to make about a accuracy well-founded and to statement be able to compareabout accuracy itself with and other to be systems, able to comparefurther data itself must with be other recorded systems, and further evaluated. data must be recorded and evaluated. 5. Conclusions 5. Conclusions Measurement from test drives showed that consumption for traction of the examined bus on its Measurement from test drives showed that consumption for traction of the examined bus on its route increases almost linearly with its payload by up to 30%. For this reason, it is clear that knowledge route increases almost linearly with its payload by up to 30%. For this reason, it is clear that knowledge of its mass is important for a precise range prediction, and thus the presented project goals, like using the buses to support the grid. In addition, many other aspects that can benefit from knowledge of mass and mass distribution, such as various vehicle assistance systems, have been pointed out. Furthermore, a broad literature search showed that there already are some methods for vehicle mass estimation, but also indicated that none of them suits buses well. Therefore, this article presented two approaches for mass detection using high-precision pressure sensors. These sensors were combined with Wi-Fi enabled microcontrollers to measure small payload inducted pressure changes in the bus’s air suspension and tires. A filter was used to detect these pressure changes and to convert them back into weights. Pressure changes from different weights in different doors showed

Electronics 2020, 9, 711 16 of 18 of its mass is important for a precise range prediction, and thus the presented project goals, like using the buses to support the grid. In addition, many other aspects that can benefit from knowledge of mass and mass distribution, such as various vehicle assistance systems, have been pointed out. Furthermore, a broad literature search showed that there already are some methods for vehicle mass estimation, but also indicated that none of them suits buses well. Therefore, this article presented two approaches for mass detection using high-precision pressure sensors. These sensors were combined with Wi-Fi enabled microcontrollers to measure small payload inducted pressure changes in the bus’s air suspension and tires. A filter was used to detect these pressure changes and to convert them back into weights. Pressure changes from different weights in different doors showed a high level of distinctiveness. The presented results of these first measurements therefore clearly show that both approaches are viable methods to mass detection for buses and that satisfying accuracy can be expected. However, it has also been shown that a number of further tests will be necessary to achieve this goal in practice. For this reason, it is planned to produce a full set of six plus ten sensor units and realize a large-scale test series with simultaneous recording of all tires and air suspensions, as well as more different weights, more repetitions and different scenarios.

Author Contributions: Research and writing, U.S.; writing—related work, H.F.; writing—introduction, H.F., M.W.; research and supervision, B.S. All authors have read and agreed to the published version of the manuscript. Funding: The presented article arises from the research project “BOB Solingen”, which is funded by the German Federal Ministry of Transport and Digital Infrastructure (“Bundesministerium für Verkehr und digitale Infrastruktur”). Acknowledgments: Thanks to the transport company “Stadtwerke Solingen GmbH” for making the measurements possible. Conflicts of Interest: The authors declare no conflict of interest.

References

1. Kellerman, A. Automated and Autonomous Spatial Mobilities; Edward Elgar Publishing: Cheltenham, UK, 2018. 2. France 24. Paris Orders 800 New Electric Buses to Fight Smog. 9 April 2019. Available online: https://www. france24.com/en/20190409-paris-orders-800-new-electric-buses-fight-smog (accessed on 14 June 2019). 3. PricewaterhouseCoopers GmbH. E-Bus-Radar; PwC: Berlin, Germany, 2019. 4. Frontier Group & U.S. PIRG Education Fund. Electric Buses—Clean Transportation for Healthier Neighborhoods and Clean Air; Frontier Group: North Shields, UK, 2018.

5. International Energy Agency. Global Energy & CO2 Status Report—The Latest Trends in Energy and Emission in 2018; International Energy Agency: Paris, France, 2019.

6. International Energy Agency. CO2 Emissions from Fuel Combustion, 2019 Edition; International Energy Agency: Paris, France, 2019. 7. United Nations Environment Programme. Air Pollution in Asia and the Pacific: Science-Based Solutions; UNEP: Bangkok, Thailand, 2019. 8. The International Council on Clean Transportation. Policy Update: China Announced 2019 Subsidies for New Energy Vehicles; The International Council on Clean Transportation: Washington, DC, USA, 2019. 9. The World Bank. Electric Mobility & Development. The World Bank: Washington, DC, USA, 2018. 10. Keegan, M. Shenzhen’s Silent Revolution: World’s First Fully Electric Bus Fleet Quietens Chinese Megacity. The Guardian. 12 December 2018. Available online: https://www.theguardian.com/cities/2018/dec/12/silence- shenzhen-world-first-electric-bus-fleet (accessed on 14 June 2019). 11. Lin, Y.; Zhang, K.; Shen, Z.-J.M.; Miao, L. Charging Network Planning for Electric Bus Cities: A Case Study of Shenzhen, China. Sustainability 2019, 11, 4713. [CrossRef] 12. Transport & Environment. Electric Buses Arrive on Time: Marketplace, Economic, Technology, Environmental and Policy Perspectives for Fully Electric Buses in the EU; Transport & Environment: Brussels, Belgium, 2018. 13. Melliger, M.; van Vliet, O.; Liimatainen, H. Anxiety vs reality—Sufficiency of battery electric vehicle range in Switzerland and Finland. Transp. Res. Part D Transp. Environ. 2018, 65, 101–115. [CrossRef] 14. Federal Ministry of Transport and Digital Infrastructure. Pressemitteilung BMVI übergibt 15-Mio-Euro-Förderung für Hybrid-Oberleitungsbusse; Federal Ministry of Transport and Digital Infrastructure: Berlin, Germany, 2017. Electronics 2020, 9, 711 17 of 18

15. Ritter, A.; Elbert, P.; Onder, C. Energy Saving Potential of a Battery Assisted Fleetof Trolley Buses. IFAC-PapersOnLine 2016, 11, 377–384. [CrossRef] 16. Santis, A.; Lauber, L.; Luder, D.; Broennimann, S.; Filliger, R.; Vezzini, A. SwissTrolley plus: R&D on battery lifespan. In Proceedings of the SCCER Mobility 4th Annual Conference, Zurich, Switzerland, 15 September 2017. 17. HAN Automotive Research. Project Description of “E-Bus 2020”; HAN Automotive Research: Arnhem, The Netherlands, 2020. 18. Stadtwerke Solingen GmbH. Elektromobilität bei den Stadtwerken; Stadtwerke Solingen GmbH: Solingen, Germany, 2018. 19. Zhang, X.; Xu, L.; Li, J.; Ouyang, M. Real-Time Estimation of Vehicle Mass and Road Grade Based on Multi-Sensor Data Fusion. In Proceedings of the 2013 IEEE Vehicle Power and Propulsion Conference (VPPC), Beijing, China, 15–18 October 2013. 20. Kim, C.; Ro, P.I. Reduced-order modelling and parameter estimation for a quarter- system. Part D J. Automob. Eng. 2000, 214, 851–864. [CrossRef] 21. Fechtner, H.; Teschner, T.; Schmuelling, B. Range prediction for electric vehicles: Real-time payload detection by tire pressure monitoring. In Proceedings of the IEEE Intelligent Vehicles Symposium, Seoul, Korea, 28 June–1 July 2015. 22. Ogawa, A. Tire State Quantity Detecting Apparatus and Method. USA Patent US7243534B2, 17 March 2005. 23. Hao, S.; Luo, P.; Xi, J. Estimation of vehicle mass and road slope based on steady-state Kalman filter. In Proceedings of the IEEE International Conference on Unmanned Systems (ICUS) 2017, Beijing, China, 27–29 October 2017. 24. Boada, B.L.; Boada, M.J.L.; Zhang, H. Sensor Fusion Based on a Dual Kalman Filter for Estimation of Road Irregularities and Vehicle Mass Under Static and Dynamic Conditions. IEEE/ASME Trans. Mechatronics 2019, 24, 1075–1086. [CrossRef] 25. Torabi, S.; Wahde, M.; Hartono, P. Road Grade and Vehicle Mass Estimation for Heavy-duty Vehicles Using Feedforward Neural Networks. In Proceedings of the 2019 4th International Conference on Intelligent Transportation Engineering (ICITE), Singapore, 5–7 September 2019. 26. Kim, I.; Kim, H.; Bang, J.; Huh, K. Development of estimation algorithms for vehicle’s mass and road grade. Int. J. Automot. Technol. 2013, 14, 889–895. [CrossRef] 27. Hac, A.B. Dynamic Estimation of Vehicle Inertial Parameters and Tire Forces from Tire Sensors. USA Patent US20090177346A1, 9 July 2009. 28. Grewal, K.S.; Darnell, P.M. Model-based EV range prediction for Electric Hybrid Vehicles. In Proceedings of the Hybrid and Electric Vehicles Conference 2013 (HEVC 2013), London, UK, 6–7 November 2013. 29. Carlson, R.B.; Lohse-Busch, H.; Diez, J.; Gibbs, J. The Measured Impact of Vehicle Mass on Road Load Forces and Energy Consumption for a BEV, HEV, and ICE Vehicle. SAE Int. J. Altern. Powertrains 2013, 2, 105–114. [CrossRef] 30. Kidambi, N.; Harne, R.L.; Fujii, Y.; Pietron, G.M.; Wang, K.W. Methods in Vehicle Mass and Road Grade Estimation. SAE Int. J. Passeng. Cars-Mech. Syst. 2014, 7, 981–991. [CrossRef] 31. Ehsani, M.; Gao, Y.; Emadi, A. Modern Electric, Hybrid Electric, and Fuell Cell Vehicles; CRC Press: Boca Raton, FL, USA, 2010. 32. Maalej, K.; Kelouwani, S.; Agbossou, K.; Dube, Y.; Henao, N. Long-Trip Optimal Energy Planning with Online Mass Estimation for Battery Electric Vehicles. IEEE Trans. Veh. Technol. 2014, 64, 4929–4941. [CrossRef] 33. Feng, Y.; Xiong, L.; Han, W. Vehicle Identification Method and System for Quality. Chinese Patent CN103946679, 25 May 2016. 34. Pence, B. Recursive Parameter Estimation using Polynomial Chaos Theory Applied to VehicleMass Estimation for Rough Terrain. Ph.D. Thesis, The University of Michigan, Ann Arbor, MI, USA, 2011. 35. Li, B.; Li, W.; Du, H. Trajectory control for autonomous electric vehicles with in- motors based on a dynamics model approach. IET Intell. Transp. Syst. 2016, 10, 318–330. [CrossRef] 36. Subosits, J.; Gerdes, J.C.; J., S.; J.C., G. Autonomous vehicle control for emergency maneuvers: The effect of topography. In Proceedings of the 2015 American Control Conference (ACC), Chicago, IL, USA, 1–3 July 2015. 37. Weiskircher, T.; Wang, Q.; Ayalew, B. Predictive Guidance and Control Framework for (Semi-) Autonomous Vehicles in Public Traffic. IEEE Trans. Control Syst. Technol. 2017, 25, 2034–2046. [CrossRef] Electronics 2020, 9, 711 18 of 18

38. Pohl, J.; Ekmark, J. A Lane Keeping Assist System for Passenger Cars-Design Aspects for the Driver Interface. In Proceedings of the 18th International Technical Conference on the Enhanced Safety of Vehicles, Nagayo, Japan, 19–22 May 2003. 39. Economic Commission for Europe of the United Nations. Regulation No 64 of the Economic Commission for Europe of the United Nations (UN/ECE); Economic Commission for Europe of the United Nations: Geneva, Switzerland, 2010. 40. U.S. Department of Transportation. National Highway Traffic Safety Administration, Federal Motor Vehicle Safety Standards: Tire Pressure Monitoring Systems; U.S. Department of Transportation: Washington DC, USA, 2005. 41. Fechtner, H.; Spaeth, U.; Patelkos, E.; Schmuelling, B. Optimisation of driver and driving assistance systems of electric vehicles by a static vehicle mass estimation. In Proceedings of the IET Hybrid and Electric Vehicle Conference, IET HEVC 2016, London, UK, 2–3 November 2016.

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).