Energy management of plug-in hybrid electric vehicle based on trip characteristic prediction

Wang, Pengyu; Li, Jie; Yu, Yuanbin; Xiong, Xiaoyong; Zhao, Shijie; Shen, Wangsheng

doi:10.1177/0954407020904464

Cited by 17 publications

(9 citation statements)

References 32 publications

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“…However, as a common practice, an off-line global optimal control approach such as DP can be used to benchmark the optimality of the solutions provided by a real-time capable controller such as A-ECMS. 50,51 To this end, Table 3 reports ERTs in the twelve retained Marrakesh race scenarios for both DP and A-ECMS. The percentages of the difference in ERTs between A-ECMS and DP are illustrated as well.…”

Section: Resultsmentioning

confidence: 99%

Optimal adaptive race strategy for a Formula-E car

Anselma

2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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Appropriately managing battery state-of-charge and temperature while ensuring minimized lap time represents a crucial issue in Formula-E competitions. An open research question might relate to simultaneously guarantee near-optimality in the race strategy solution, computational light-weighting, and effective adaptability with respect to varying and unpredictable race conditions. In this paper, a novel near-optimal real-time capable Formula-E race controller is introduced that takes inspiration from the adaptive equivalent consumption minimization strategy (A-ECMS) approach. A reduced-order Formula-E car plant model is detailed first. The optimal Formula-E race problem subsequently discussed involves controlling at each lap the depletable battery energy, the thermal management mode, and the race mode in order to minimize the overall race time. Moreover, avoiding excessively depleting the battery energy and overheating the battery are considered as constraints for the race optimization problem. Dynamic programing (DP) is implemented first to obtain the global optimal Formula-E race strategy solution in an off-line control approach. The proposed real-time capable A-ECMS based race controller finds then detailed illustration. The flexibility of the introduced A-ECMS Formula-E race controller is guaranteed by optimally calibrating the related equivalence factors to adapt to the current vehicle states (i.e. battery state-of-charge, battery temperature, and lap number). Simulation results for the Marrakesh e-prix considering different race scenarios in terms of battery initial temperature and Safety car entry demonstrate that the estimated race time achieved by the A-ECMS race controller is always near-optimal being 1.7% higher at most compared with the corresponding global optimal benchmark provided by DP.

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Section: Resultsmentioning

confidence: 99%

Optimal adaptive race strategy for a Formula-E car

Anselma

2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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“…The starting point and key points of travel trajectory are the key information of trajectory prediction model [29]. In order to avoid the interference of third-party mapping software on the trajectory prediction model, the starting point of the trajectory is firstly clustered, and the key points of the historical trajectory clustered into the same class are mined, so as to complete the cluster analysis of the historical travel trajectory.…”

Section: Mileage Prediction Based On Historical Datamentioning

confidence: 99%

Research on Energy Management Method of Plug-In Hybrid Electric Vehicle Based on Travel Characteristic Prediction

Wang

Sun

2021

Energies

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In the research on energy management methods of plug-in hybrid electric vehicles, it is expected that a future trend will be to optimize energy management using the information provided by the global positioning system (GPS) and intelligent transportation system (ITS), which is relatively scarce in current research. This study proposes a PHEV energy management method based on travel characteristic prediction. Firstly, this study processes the historical travel data of a certain driver obtained by GPS and ITS and uses the established Markov trajectory prediction model based on key points to predict the trajectory and mileage. Then, on the basis of characteristics analysis of historical travel data, while considering traffic information to form a target cycle, the driving cycles are classified and identified based on traffic information predictions. Then, according to the reasonable SOC allocation range of the four typical cycles, the planning algorithm of the SOC reference trajectory is determined and verified. Finally, based on the previous work, an A-ECMS energy management method based on travel characteristic prediction is established. By comparing different energy management methods, the developed energy management method based on travel characteristic prediction can reasonably utilize power batteries. The fuel saving is about 8.95% higher than the rule-based energy management method, which can effectively improve the whole vehicle’s fuel economy and optimization ability.

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“…Concerning reviews, it is noticeable, that most of the works concentrate on PHEVs, where authors [23][24][25] where the main has been on energy management and intelligent transportation systems, where this topic includes the review [22,26] when it is combined with EVs.…”

Section: State Of the Artmentioning

confidence: 99%