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Modelling the Effect of Driving Events on Electrical Vehicle Energy Consumption Using Inertial Sensors in Smartphones

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Modelling the Effect of Driving Events on Electrical Vehicle Energy Consumption Using Inertial Sensors in Smartphones energies Article Modelling the Effect of Driving Events on Electrical Vehicle Energy Consumption Using Inertial Sensors in Smartphones David Jiménez 1,* ID , Sara Hernández 2, Jesús Fraile-Ardanuy 3 ID , Javier Serrano 1, Rubén Fernández 2 ID and Federico Álvarez 1 ID 1 Grupo de Aplicación de Telecomunicaciones Visuales (GATV), IPTC, Universidad Politécnica de Madrid, 28040 Madrid, Spain; [email protected] (J.S.); [email protected] (F.Á.) 2 Grupo de Aplicaciones de Procesado de Señales (GAPS), IPTC, Universidad Politécnica de Madrid, 28040 Madrid, Spain; [email protected] (S.H.); [email protected] (R.F.) 3 Grupo de Sistemas Dinámicos, Aprendizaje y Control (SISDAC), IPTC, Universidad Politécnica de Madrid, 28040 Madrid, Spain; [email protected] * Correspondence: [email protected]; Tel.: +34-913367344 Received: 15 November 2017; Accepted: 7 February 2018; Published: 10 February 2018 Abstract: Air pollution and climate change are some of the main problems that humankind is currently facing. The electrification of the transport sector will help to reduce these problems, but one of the major barriers for the massive adoption of electric vehicles is their limited range. The energy consumption in these vehicles is affected, among other variables, by the driving behavior, making range a value that must be personalized to each driver and each type of electric vehicle. In this paper we offer a way to estimate a personalized energy consumption model by the use of the vehicle dynamics and the driving events detected by the use of the smartphone inertial sensors, allowing an easy and non-intrusive manner to predict the correct range for each user. This paper proposes, for the classification of events, a deep neural network (Long-Short Time Memory) which has been trained with more than 22,000 car trips, and the application to improve the consumption model taking into account the driver behavior captured across different trips, allowing a personalized prediction. Results and validation in real cases show that errors in the predicted consumption values are halved when abrupt events are considered in the model. Keywords: energy consumption; electric vehicle; smartphone; sensors; driving behavior 1. Introduction The technology behind the design of electric vehicles is continuously improving and the European Union (E.U.) predicts that these vehicles could be in mass production by 2020 [1,2]. By developing a completely new way of using information from location and smartphone sensors, refined models of energy consumption can be simulated enabling further studies on the impact of user behavior on mobility and, subsequently, on other main topics related to electrical mobility and electricity distribution networks. The aim of this paper was the characterization of the behavior of drivers using only the sensors in a smartphone, avoiding the installation of additional hardware and allowing the prediction of the energy consumption of the user considering the captured driving style. Although most driver monitoring methods make use of extra in-vehicle hardware, connected to the vehicle Electronic Control Unit, typically known as On-Board Diagnostics (OBD) devices, systems that replace this capture on-board approach with a simple smartphone are becoming ever more frequent. Since smartphones Energies 2018, 11, 412; doi:10.3390/en11020412 www.mdpi.com/journal/energies Energies 2018, 11, 412 2 of 23 are easily accessible, widely available and low cost, these devices can be used to collect, process and Energies 2018, 11, x FOR PEER REVIEW 2 of 24 analyze driving data as well as detect maneuvers and classify aggressive driving behaviors. Inprocess this work,and analyze users’ driving driving data habits as well are as characterized detect maneuvers on the and basis classify of the aggressive Drivies driving application (Figurebehaviors1) from. PhoneDrive S.L. [ 3] a successful spin-off of Telefonica, the main telecommunications provider inIn this Spain. work, This user app,’s driving which habits canbe are freely characterized installed on in the devices basis of with Drivies Android application and IOS(Figure operating 1) systems,from allowsPhoneDrive the captureS.L. [3] a ofsuccessful this sensor spin- dataoff of (inTelefonica, particular, the main accelerometer telecommunications and gyroscopes) provider and providesin Spain. information This app, aboutwhich can how be and freely where installed you in drive. devices Exploiting with Android this and sensor IOSdata operating to model system, driving behaviorallows requires the cap someture of sophisticated this sensor data algorithmic (in particular, processing accelerometer and machine and gyroscopes) learning techniquesand provides to cope information about how and where you drive. Exploiting this sensor data to model driving behavior with phone orientation changes, different type of phone sensor characteristics, road type, and more. requires some sophisticated algorithmic processing and machine learning techniques to cope with A deepphone learning orientation framework changes, (neural different networks) type of phone is used sensor in thischaracteristics, work, due road to thetype, good and performancemore. A that itdeep has learning demonstrated framework in this(neural kind networks) of activities is used and in forthis thiswork, kind due of to signalsthe good [4 performance]. This approach that has also twohas demonstrated main advantages. in this kind Firstly, of activit it avoidsies and the for time-intensivethis variety of signals process [4]. ofThis designing approach has handcrafted also features,two main as convolution advantages. filtersFirstly areit is trained avoiding during the time supervised-intensive process training of of designing the model. handcrafted The second advantagefeatures, is itsas higherconvolution accuracy filters compared are trained to during the common supervised approach training of inputtingof the model. engineered The second features into aadvantage discriminative is its higher classifier accuracy (i.e., compared Support to the Vector common Machines). approach Anotherof inputting key engineered benefit offeatures using this kindinto of algorithma discriminative is related classifier to (i.e. the, Support signals Vector that are Machines). generated Other by key the benefit sensors of using on mobilethis kind devices. of Thesealgorithm measurements is related are to very the noisy,signals beingthat are hard genera to findted aby distribution sensors on ablemobile to model,devices. inThese practice, measurements are very noisy, being hard to find a distribution able to model, in practice, with high with high accuracy that noise. Neural networks have proven to be extremely effective to accommodate accuracy that noise. Neural Networks have proven to be extremely effective to accommodate diverse diversesensor sensor noise noise patterns patterns [5]. [5]. FigureFigure 1.1. Drivies app app.. For modelling of energy consumption, a model was developed, considering the prediction of Forenergy modelling consumption the energywith different consumption, variablesa intervening model was in developed, the vehicle dynamics, considering and the adding prediction the of energyeffect consumption of the driving with behavior different der variablesived from intervening smartphone in sensors, the vehicle captured dynamics, using and Drivies adding app. the In effect of theparticular, driving behavior it takes into derived account from the smartphone type of driving sensors, maneuver captured (turns, using accelerations the Drivies and app. brakes) In particular, and it takeswhether into accountthey are abrupt the type or ofnot. driving The model maneuver has developed (turns, a accelerations completely new, and and braking) highly detailed, and whether theytime are abrupt-space approach or not. The based model on the has use developed of vast quantities a completely of data new,from andthe location highly and detailed, smartphone time-space approachsensors. based on the use of vast quantities of data from the location and smartphone sensors. Global Navigation Satellite Systems (GNSS) and Inertial Measurements Units (IMU) are two Global Navigation Satellite Systems (GNSS) and Inertial Measurements Units (IMU) are two key keys features of smartphones that have been increasing in the last years. These are two essential featuresinnovations of smartphones for Location that-Based have Services been increasing (LBS) and user in use activity in the monitoring last years. applications. These are The two most essential innovationsdeveloped for domain Location-Based is the analysis Services of driving (LBS) behavior and user for activityinsurance monitoring telematics, obtaining applications. data from The most developedsmartphone domain sensors is the [6,7]. analysis of driving behavior for insurance telematics, obtaining data from smartphone sensors [6,7]. Energies 2018, 11, 412 3 of 23 However, these systems still show performance problems, specially related to the battery consumption. The high battery cost of activating geopositional systems such as Global Positioning System (GPS) is the main challenge identified in [6]. The authors in [8] proposed an intelligent procedure to reduce battery cost of geolocation systems based on triggering the location with regard to specific activities, by using tridimensional accelerometer data. One of the existing solutions, proposed in [9], suggests enabling smartphone accelerometer signals in order to manage the activation of geolocation
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