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Energy Procedia 88 ( 2016 ) 854 – 859

CUE2015-Applied Energy Symposium and Summit 2015: Low carbon cities and urban energy systems A Comparison Study of Energy Management for A Plug-in Serial Hybrid

Wei Liua, Hongwen Hea,*, Zexing Wangb

aNational Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing, 100081,China bBeijing Electric Vehicle CO., LTD., Caiyu Economic Development Area, Beijing, 102606, China

Abstract

This paper demonstrates a comparison study of two energy management methods for a plug-in serial . The two methods are the optimal single point start-stop (SPSS) control and the optimal operation line power track (OLPT) control respectively. The control logics of the two methods are designed in this paper and the performances are verified and compared under simulation condition, which reveals that the power track control strategy performs better energy economy both theoretically and practically.

© 20162015 Published The Authors. by Elsevier Published Ltd. This by isElsevier an open Ltd.access article under the CC BY-NC-ND license (Selectionhttp://creativecommons.org/licenses/by-nc-nd/4.0/ and/or peer-review under responsibility). of CUE Peer-review under responsibility of the organizing committee of CUE 2015

Keywords: Plug-in serial hybrid electric vehicle; Energy management; Engine start-stop control; Power track control

Nomenclature Abbreviation OFC Overall Fuel consumption EV Electric Vehicle WFC Weighted Fuel Consumption HEV Hybrid Electric Vehicle Symbols PHEV Plug-in Hybrid Electric Vehicle SOC State of Charge

RHEV Range-extending HEV Pd,req Demanded power

APU Auxiliary Power Unit Pb Battery power

BSFC Brake Specific Fuel Consumption Pe APU power

SPSS Single point start-stop control ne APU working speed

OLPT Optimal operation line power track St Total average daily driving distance

NEDC New European Driving Circle Se Pure electric driving range

*Corresponding author. Tel.: +86-6891-4842; fax: +86-6891-4842. E-mail address: [email protected]

1876-6102 © 2016 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of CUE 2015 doi: 10.1016/j.egypro.2016.06.064 Wei Liu et al. / Energy Procedia 88 ( 2016 ) 854 – 859 855

1. Introduction

Taking advantage of both EVs and HEVs, the plug-in serial hybrid electric vehicle can act as a range- extending hybrid electric vehicle [1-2]. For a longer cruise mileage, the range-extending HEV is equipped with a range-extender, which is also called the Auxiliary Power Unit (APU), on the basis of pure EV. To achieve higher fuel economy and longer cruise mileage is the crucial task of the energy management [3]. It is important to make clear the relationship between the Hybrid Electric Vehicle (HEV), the Plug-in Hybrid Electric Vehicle (PHEV) and the Range-extending Hybrid Electric vehicle (RHEV). Firstly, PHEV and RHEV are both HEVs, since they both have two energy resources including the fuel tank and the battery. Secondly, RHEV is a kind of PHEV because that the battery in RHEV can be charged from the external power grid. Thirdly, RHEV has a fixed serial hybrid power train, which means that the engine in APU cannot drive the vehicle directly. However, PHEVs have a variety of hybrid power trains including the serial, the parallel and the mixed hybrid type, etc. Lastly, HEVs usually have a narrow SOC working range, as the charge-sustaining strategy is usually used. PHEVs have a wide SOC working range with the charge-depleting strategy. Meanwhile, RHEVs have stranger power performance within the whole SOC working range, which means that the battery in RHEV can provide enough power for driving even when SOC is low. The APU is mainly used for extending the mileage, rather than supplement the driving power. The research object in this paper is a RHEV. Many optimal control methods, including the dynamic programming, the minimum principle, the equivalent consumption minimization strategy, etc., have been researched theoretically for the energy management control problem of HEVs [4-7]. The methods are applicable to different types of HEVs when different constraints of SOC are applied. But these methods are usually not practical because of the unpredictable driving condition or great calculation burden. The rule-based APU control strategies, including the optimal single point start-stop control (SPSS) and the optimal operation line power track control (OLPT), etc., are still the most widely used energy management methods for HEVs [8-9]. This paper attempts to make a comparison study between the optimal single point start-stop control strategy and the optimal operation line power track control strategy, taking a plug-in serial hybrid electric vehicle, namely, a RHEV, as the research object. Section 2 gives a brief introduction about the RHEV platform. In section 3, the detailled control logics of the SPSS and OLPT strategies are discussed, and the simulation results of these two method are analyzed. Finally, section 4 conclude this paper.

2. The Plug-in Serial Hybrid Electric Vehicle Configuration

The overall structure of the plug-in serial hybrid electric vehicle is depicted in figure 1. It is a typical chargeable serial PHEV with a gasoline range-extender. The vehicle is driven by the main motor. A with fixed gear ratio connects the main motor and rear wheel. The electricity comes from two energy resources: the chargeable battery and the gasoline range-extender, namely the APU system.

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Fig. 1. The plug-in serial hybrid electric vehicle 856 Wei Liu et al. / Energy Procedia 88 ( 2016 ) 854 – 859

The basic parameters of the researched RHEV is listed in table 1. As a typical light passenger car, the designed overall cruise mileage is 350km. The RHEV is equipped with a 40 Ah, 320V battery, which can support a pure electric driving range of 55km.Introduction: to explain the background work, the practical applications and the nature and purpose of the paper.

Table 1. Basic parameters of the RHEV

Parameter Value Full loaded mass 1607(kg) Tire rolling radius 0.298(m) Transmission ratio 7.79 Maximum APU power 25(kW) Maximum motor power 45(kW) Maximum motor torque 149(Nm) Battery voltage 320(V) Maximum battery capacity 40(Ah) Maximum fuel volume 25(L) Pure electric driving range 55(km)

3. Research on The Two Energy Management Strategies

3.1. Control logic of SPSS and OLPT strategies

As shown in figure 2, under SPSS control logic, the engine has only one working speed, while the engine has a wide working range along the minimal BFSC operation line under OLPT control logic. Meanwhile, under OLPT control logic, the BFSC at different generated power is relatively lower than that of SPSS control logic. The optimal working point is the lowest point in the minimal BFSC operation line, which is obtained from the experimental fuel economy map data of the APU system. 1000 4000 BFSC under OLPT control 900 BFSC under SPSS control Working speed under OLPT control 800 Working speed under SPSS control 3500

ͨ 700 (rpm) e

3000 n

g/kWh 600 ͧ e 500 b 2500 400

300 2000 200 APU working speed BFSC of APU, 100 1500 0 0510 15 20 25 APU Power P (kW) e Fig. 2. Working points under two control logic According to the minimal BFSC operation line in figure 2, the optimal single working point of the APU system is listed in table 2.

Table 2. APU optimal working point Optimal parameters Value ˄ ˅ Speed nE,opt 3350(rpm) ˄ ˅ Torque TE,opt 51.61(Nm) ˄ ˅ Power PE,opt 16.80(kW) ˄ ˅ BSFC bAPU,opt 258.2617(g/kWh) Wei Liu et al. / Energy Procedia 88 ( 2016 ) 854 – 859 857

Under both of the two strategies, to maintain a suitable SOC level and prevent over-charging and over- discharging of the battery, when SOC>SOCh, battery discharging takes precedence to provide the driving power; when SOC

Table 3. Control logic of SPSS strategy

Condition Strategy ­ PPPd,req ¬¼ªº b,min, b,max ° Pe 0 ® ­SOC SOC, SOC ° >@lh n 0 °SOCt SOCh or ® e ¯° ¯°M 0 0 PP P °­SOC! SOCh ed,reqb,max  ® PP! ¯° d,req b,max nne E,opt

PPee,max SOC SOCl nne E,opt

PPPe1 min^` e,opt , e,max

Other PPPe max^` e1 , e,min

nne E,opt

Table 4. Control logic of OLPT strategy

Condition Strategy

°­SOC! SOCh ® Pnee 0, 0 PPPªº ¯° d,req¬¼ b,min, b,max PP  P °­SOC! SOCh ed,reqb,max ® PP! ¯° d,req b,max nne e,opt P e

min bPee Pe SOC>@SOC , SOC lh PPPe ªº¬¼ e,min, e,max

nne e,opt P e

PPee,max SOC SOCl nne e,opt P e

3.2. Simulation Results Analysis

The two control method of SPSS and OLPT are compared under simulation condition. The New European Driving Cycle (NEDC) is chosen as the test condition. To illustrate the different performances 858 Wei Liu et al. / Energy Procedia 88 ( 2016 ) 854 – 859

of the two methods, a particular simulation condition was set where the initial SOC was 0.5, the initial APU state was start and the permitted battery charging/discharging rate was 3C. The simulation results of the two methods during 3 consecutive NEDC is shown in figure 3. We can see from figure 3(b) that the power of APU was nearly the same during the driving process, but the working points of the engine were much different from each other. Under SPSS method, the APU speed was restricted to be , and the working torque and the BSFC varies as the generating power changes. Under OLPT method, the APU working speed had a large changing scale. For different required generating power, an optimal engine working point is obtained from the minimal BSFC line (figure 3(c)). We can see from figure 3(d) that the BSFC discrepancy of the two methods gets greater when the APU power is further away from the optimal point shown as table 2. The OLPT method showed better fuel economy as desired, but the APU speed may change greatly and rapidly, which will bring much tough work for engine control. 0.6 SPSS method OLPT method

0.55 SOC

(a) 0.5 30 SPSS method OLPT method ͨ

kW 20 ͧ Pe (b)

10 4000 SPSS method OLPT method 3800

(rpm) 3600 e n 3400 (c)

3200

ͨ 350 SPSS method OLPT method

g/kWh 300 ͧ

250 (d) BSFC 0 200 400 600 800 1000 1200 Time (s)  Fig. 3. Simulation results under NEDC driving circle.

To demonstrate the accumulative effects of the BSFC discrepancy shown in figure 3(c), a longer range simulation test was carried out. The initial fuel tank is full (25L), and the battery is fully charged, the vehicle keeps driving until the fuel tank is empty and the SOC is below 0.1. Table 5 shows the mileage and fuel economy information of the two methods.

Table 5. The mileage and fuel economy comparison

Strategy SPSS OLPT Mileage(km) 404.5 406.2 OFC (L/100km) 6.18 6.15 WFC (L/100km) 1.93 1.92 Wei Liu et al. / Energy Procedia 88 ( 2016 ) 854 – 859 859

In table 5, OFC is the overall fuel consumption every hundred kilometer. WFC is the weighted fuel consumption calculated as follow: SS WFC OFC te (1) St where St is total the average daily driving distance of the designed RHEV (80km); Se is the pure electric driving range of the designed RHEV (55km).

4. Conclusions

Two kinds of rule-based energy management control methods, including the SPSS and OLPT methods are designed for a plug-in serial hybrid electric vehicle. The control methods are designed to obtain a longer cruise mileage and prevent battery over-charging and over-discharging at the same time. Meanwhile, the start/stop frequency of the APU is also considered. To compare the performances of the two rule-based control methods. The simulation for fuel economy test is carried out. As a result, The OLPT method shows better but not obvious fuel economy, at the cost of harder APU control. The simulation result gives guidance for energy management control in real application.

5. Copyright

Authors keep full copyright over papers published in Energy Procedia

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Biography

Hongwen He is currently a Professor with the National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology and a researcher with the Beijing Co-innovation Center for Electric Vehicles. His research interests include power battery modeling and simulation on electric vehicles, design, and control theory of the hybrid power train.