Research article Special Issues

A-EMCS for PHEV based on real-time driving cycle prediction and personalized travel characteristics

  • Received: 24 July 2020 Accepted: 08 September 2020 Published: 23 September 2020
  • Energy management plays an important role in improving the fuel economy of plug-in hybrid electric vehicles (PHEV). Therefore, this paper proposes an improved adaptive equivalent consumption minimization strategy (A-ECMS) based on long-term target driving cycle recognition and short-term vehicle speed prediction, and adapt it to personalized travel characteristics. Two main contributions have been made to distinguish our work from exiting research. Firstly, online long-term driving cycle recognition and short-term speed prediction are considered simultaneously to adjust the equivalent factor (EF). Secondly, the dynamic programming (DP) algorithm is applied to the offline energy optimization process of A-ECMS based on typical driving cycles constructed according to personalized travel characteristics. The improved A-ECMS can optimize EF based on mileage, SOC, long-term driving cycle and real-time vehicle speed. In the offline part, typical driving cycles of a specific driver is constructed by analyzing personalized travel characteristics in the historical driving data, and optimal SOC consumption under each typical driving cycle is optimized by DP. In the online part, the SOC reference trajectory is obtained by recognizing the target driving cycle from Intelligent Traffic System, and short-term vehicle speed is predicted by Nonlinear Auto-Regressive (NAR) neural network which both adjust EF together. Simulation results show that compared with CD-CS, the fuel consumption of A-ECMS proposed in the paper is reduced by 8.7%.

    Citation: Yuanbin Yu, Junyu Jiang, Pengyu Wang, Jinke Li. A-EMCS for PHEV based on real-time driving cycle prediction and personalized travel characteristics[J]. Mathematical Biosciences and Engineering, 2020, 17(6): 6310-6341. doi: 10.3934/mbe.2020333

    Related Papers:

  • Energy management plays an important role in improving the fuel economy of plug-in hybrid electric vehicles (PHEV). Therefore, this paper proposes an improved adaptive equivalent consumption minimization strategy (A-ECMS) based on long-term target driving cycle recognition and short-term vehicle speed prediction, and adapt it to personalized travel characteristics. Two main contributions have been made to distinguish our work from exiting research. Firstly, online long-term driving cycle recognition and short-term speed prediction are considered simultaneously to adjust the equivalent factor (EF). Secondly, the dynamic programming (DP) algorithm is applied to the offline energy optimization process of A-ECMS based on typical driving cycles constructed according to personalized travel characteristics. The improved A-ECMS can optimize EF based on mileage, SOC, long-term driving cycle and real-time vehicle speed. In the offline part, typical driving cycles of a specific driver is constructed by analyzing personalized travel characteristics in the historical driving data, and optimal SOC consumption under each typical driving cycle is optimized by DP. In the online part, the SOC reference trajectory is obtained by recognizing the target driving cycle from Intelligent Traffic System, and short-term vehicle speed is predicted by Nonlinear Auto-Regressive (NAR) neural network which both adjust EF together. Simulation results show that compared with CD-CS, the fuel consumption of A-ECMS proposed in the paper is reduced by 8.7%.


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    [1] M. F. M. Sabri, K. A. Danapalasingam, M. F. Rahmat, A review on hybrid electric vehicles architecture and energy management strategies, Renew. Sustain. Energy Rev., 53 (2016), 1433-1442. doi: 10.1016/j.rser.2015.09.036
    [2] H. Sö lek, K. Müderrisoğlu, C. Armutlu, M. Yılmaz, Development of fuzzy logic based energy management control algorithm for a Plug-in HEV with fixed routed, 2019 International Aegean Conference on Electrical Machines and Power Electronics (ACEMP).
    [3] R. Du, X. Hu, S. Xie, Battery aging- and temperature-aware predictive energy management for hybrid electric vehicles, J. Power Sources, 473 (2020), 228568. doi: 10.1016/j.jpowsour.2020.228568
    [4] F. Xu, X. Jiao, Y. Wang, Y. Jing, Battery-lifetime-conscious energy management strategy based on SP-SDP for commuter plug-in hybrid electric vehicles, IEEE J. Trans. Electr. Electron. Eng., 13 (2018), 472-479. doi: 10.1002/tee.22590
    [5] J. Gao, J. Zhao, S. Yang, J. Xi, Control strategy of Plug-in hybrid electric bus based on driver intention, J. Mechan. Eng., 52 (2016), 107-114.
    [6] N. Sulaiman, M. A. Hannan, A. Mohamed, E. H. Majlana, W. R. Wan Daud, A review on energy management system for fuel cell hybrid electric vehicle: Issues and challenges, Renew. Sustain. Energy Rev., 52 (2015), 802-814.
    [7] M. Sivertsson, L. Eriksson, Design and evaluation of energy management using map-based ECMS for the PHEV benchmark, Oil Gas Sci. Technol., 70 (2015), 159-178. doi: 10.2516/ogst/2014022
    [8] F. Zhang, X. Hu, R. Langaric, D. Cao, Energy management strategies of connected HEVs and PHEVs: Recent progress and outlook, Prog. Energy Combust. Sci., 73 (2019), 235-256. doi: 10.1016/j.pecs.2019.04.002
    [9] Z. Chen, N. Guo, J. Shen, R. Xiao, P. Dong, A hierarchical energy management strategy for power-split plug-in hybrid electric vehicles considering velocity prediction, IEEE Access, 6 (2018), 3261-3274.
    [10] W. Zhou, L. Yang, Y. S. Cai, T. X. Ying, Dynamic programming for new energy vehicles based on their work modes Part I: Electric vehicles and hybrid electric vehicles, J. Power Sources, 406 (2018), 15166.
    [11] Y. Zhou, A. Ravey, M. C. Péra, A survey on driving prediction techniques for predictive energy management of plug-in hybrid electric vehicles, J. Power Sources, 412 (2019), 480-495. doi: 10.1016/j.jpowsour.2018.11.085
    [12] L. Li, C. Yang, Y. Zhang, A rule-based energy management strategy for Plug-in Hybrid Electric vehicle energy management strategy of Plug-in hybrid electric bus for city-bus route, IEEE Trans. Veh. Technol., 64 (2015), 2792-2803. doi: 10.1109/TVT.2014.2352357
    [13] A. Rezaei, J. B. Burl, B. Zhou, Estimation of the ECMS equivalent factor bounds for hybrid electric vehicles, IEEE Transact. Control Systems Technol., 26 (2018), 2198-2205. doi: 10.1109/TCST.2017.2740836
    [14] H. Guo, G. Wei, F. Wang, C. Wang, S. Du, Self-learning enhanced energy management for Plug-in Hybrid electric bus with a target preview based SOC plan method, IEEE Access, 7 (2019), 103153-103166. doi: 10.1109/ACCESS.2019.2931509
    [15] R. Lian, J. Peng, Y. Wu, H. Tan, H. Zhang, Rule-interposing deep reinforcement learning based energy management strategy for power-split hybrid electric vehicle, Energy, 197 (2020), 117297. doi: 10.1016/j.energy.2020.117297
    [16] G. Liu, J. Zhang, An energy management of plug-in hybrid electric vehicles based on driver behavior and road information, J. Intell. Fuzzy Systems, 33 (2017), 3009-3020. doi: 10.3233/JIFS-169352
    [17] C. Sun, S. J. Moura, X. Hu, J. K. Hedrick, F. Sun, Dynamic traffic feedback data enabled energy management in plug-in hybrid electric vehicles, IEEE Transact. Control Systems Technol., 23 (2015), 1075-1086. doi: 10.1109/TCST.2014.2361294
    [18] J. Shin, M. Sunwoo, Vehicle speed prediction using a Markov chain with speed constraints, IEEE Transact. Intell. Transport. Systems, 20 (2019), 3201-3211. doi: 10.1109/TITS.2018.2877785
    [19] D. Hodgson, B. C. Mecrow, S. M. Gadoue, H. J. Slater, P. G. Barrass, D. Glaouris, Effect of vehicle mass changes on the accuracy of Kalman filter estimation of electric vehicle speed, IET Electr.l Systems Transport., 3 (2013), 67-78. doi: 10.1049/iet-est.2012.0027
    [20] Z. Lei, D. Qin, L. Hou, J. Peng, Y. Liu, An adaptive equivalent consumption minimization strategy for Plug-in hybrid electric vehicles based on traffic information, Energy, 190 (2020).
    [21] P. Wang, J. Li, Y. Yu, X. Xiong, S. Zhao, W. Shen, Energy management of plug-in hybrid electric vehicle based on trip characteristic prediction, Proc. IMechE Part D J. Autom. Eng., 234 (2020), 2239-2259. doi: 10.1177/0954407020904464
    [22] H. Y. Tong, Development of a driving cycle for a supercapacitor electric bus route in Hong Kong, Sustain. Cities Soc., 48 (2019), 101588. doi: 10.1016/j.scs.2019.101588
    [23] J. Zhang, Z. Wang, P. Liu, Z. Zhang, X. Li, C. Qu, Driving cycles construction for electric vehicles considering road environment: A case study in Beijing, Appl. Energy, 253 (2019), 113514. doi: 10.1016/j.apenergy.2019.113514
    [24] P. Seers, G. Nachin, M. Glaus, Development of two driving cycles for utility vehicles, Transport. Res. Part D Transport Environ., 41 (2015), 377-385. doi: 10.1016/j.trd.2015.10.013
    [25] S. Zhan, D. Qin, Y. Zeng, Energy management strategy of HEV based on driving cycle recognition using genetic optimized K-means clustering algorithm, China J. Highway Transport, 29 (2016), 130-152.
    [26] H. Yu, F. Tseng, R. Mcgee, Driving pattern identification for EV range estimation, IEEE Intern. Electr. Vehicle Conference 2012, pp. 1-7.
    [27] L. Xie, J. Tao, Q. Zhang, H. Zhou, CNN and KPCA-based automated feature extraction for real time driving pattern recognition, IEEE Access, 7 (2019), 123765-123775. doi: 10.1109/ACCESS.2019.2938768
    [28] Y.Zhou, A. Ravey, M. C. Pera, A survey on driving prediction techniques for predictive energy management of plug-in hybrid electric vehicles, J. Power Sources, 412 (2019), 480-495. doi: 10.1016/j.jpowsour.2018.11.085
    [29] J. Lian, S. Liu, L. Li, X. Liu, Y. Zhou, F. Yang, et al. A mixed logical dynamical-model predictive control (MLD-MPC) energy management control strategy for Plug-in hybrid electric vehicles (PHEVs), Energies, 10 (2017), 74. doi: 10.3390/en10010074
    [30] F. Shi, Y. Wang, J. Chen, J. Wang, Y. Hao, Y. He, Short-term vehicle speed prediction by time series neural network in high altitude areas, IOP Conference Series Earth Environ. Science, 304 (2019), 032072. doi: 10.1088/1755-1315/304/3/032072
    [31] K. W. Wang, C. Deng, J. P. Li, Y. Y. Zhang, X. Y. Li, M. C. Wu, Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network, Epidem. Infect., 145(2017), 1118-1129.
    [32] C. Musardo, G. Rizzoni, Y. Guezennec, B. Staccia. A-ECMS: An adaptive algorithm for hybrid electric vehicle energy management. European J. Control, 11 (2005), 509-524.
    [33] C. Yang, S. Du, L. Li, S. You, Y. Yang, Y. Zhao, Adaptive real-time optimal energy management strategy based on equivalent factors optimization for plug-in hybrid electric vehicle, Appl. Energy, 2017,883-896.
    [34] C. Sun, F. Sun, H. He, Investigating adaptive-ECMS with velocity forecast ability for hybrid electric vehicles, Appl. Energy, 185 (2017), 1644-1653. doi: 10.1016/j.apenergy.2016.02.026
    [35] T. Amir, V. Mahyar, A. Nasser, M. John, A comparative analysis of route-based energy management systems for Phevs, Asian J. Control, 18 (2016), 29-39. doi: 10.1002/asjc.1191
    [36] Y. Zhang, L. Chu, Z. Fu, Optimal energy management strategy for parallel plug-in hybrid electric vehicle based on driving behavior analysis and real time traffic information prediction, Mechatronics, 46 (2017), 177-192. doi: 10.1016/j.mechatronics.2017.08.008
    [37] Suprihatin, I. T. R. Yanto, N. Irsalinda, T. H. Purwaningsih, A. P. Wibawa, A performance of modified fuzzy C-means (FCM) and chicken swarm optimization (CSO), 3rd international conference on science in information technology (ICSITech), Bandung, 2017: pp. 171-175.
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