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International Journal of Frontiers in Engineering Technology, 2021, 3(6); doi: 10.25236/IJFET.2021.030610.

Simulation Research on Battery Energy Management Strategy of Extended Program Based on Cruise


Menggu Jiang, Yixian Su

Corresponding Author:
Menggu Jiang

School of Information, North China University of Technology, Beijing 100144, China


As a new energy vehicle, range-extended electric tractor have the advantages of long driving range and low pollutant emissions. However, at present, most extended battery have problems of insufficient energy management and high consumption.Therefore, it is necessary to study the energy management strategy of the extended-range electric vehicle battery. The additional battery provides power and power for the whole vehicle, realizes on-board charging through the expansion field, provides charging function when parking, and can be connected to the power grid; High voltage power supply safety management function; High voltage battery charger, high voltage power supply device, high voltage circuit current detection function, manual maintenance switch, battery system thermal management system, monitoring system operation status detection function.In order to construct the advanced energy management strategy and carry out the effective simulation analysis, based on the Cruise platform, this paper studies the power system matching and energy management control strategy of the extended battery, and completes the power system matching according to the vehicle dynamic parameters and the basic parameters of the vehicle. In this paper, a Cruise vehicle model of an extended-range extended battery is established on the Cruise professional simulation software platform, and joint simulation experiments are performed based on the energy management strategy model built in Matlab / Simulink. Simulation results show that the control algorithm designed in this paper satisfies the vehicle's driving needs,improves the economy of the vehicle by 6.84%, better controls the change of battery SOC. So as to verify the dynamics and economics of the power system and to evaluate the advantages and disadvantages of using this energy management strategy in different operating conditions, this paper studies the dynamics and economics under different cycling conditions. The vehicle power system matching and energy management strategies designed in this article have better economic characteristics when driving in this cycle.


Cruise Platform, Energy Management Strategy, Extended Electric, Battery SOC, Cruise Vehicle Model, Power System Matching

Cite This Paper

Menggu Jiang, Yixian Su. Simulation Research on Battery Energy Management Strategy of Extended Program Based on Cruise. International Journal of Frontiers in Engineering Technology (2021), Vol. 3, Issue 6: 89-101. https://doi.org/10.25236/IJFET.2021.030610.


[1] X. Lin, L. Mo, Y. Luo, & S. Zhang. (2017). Part-time hybrid energy management strategy for range-extended electric vehicle based on energy prediction. Qiche Gongcheng/automotive Engineering, 39(4), 369-375 and 380.

[2] Stefano De Pinto, Pablo Camocardi, Aldo Sorniotti, Patrick Gruber, Pietro Perlo, & Fabio Viotto. (2017). Torque-fill control and energy management for a four-wheel-drive electric vehicle layout with two-speed transmissions. IEEE Transactions on Industry Applications, 53(1), 447-458.

[3] F. Ding, W. Wang, C. Xiang, W. He, & Y. Qi. (2017). Speed prediction method and energy management strategy for a hybrid electric vehicle based on driving condition classification. Automotive Engineering, 39(11), 1223-1231.

[4] Mohd Hafizal Ishak. (2017). Modelling energy consumption behaviour using 'energy culture' concept for student accommodations in malaysian public universities. Facilities, 35(3), 658-683.

[5] Shi, J. , Huang, W. , Tai, N. , Qiu, P. , & Lu, Y. . (2017). Energy management strategy for microgrids including heat pump air-conditioning and hybrid energy storage systems. Journal of Engineering, 2017(13), 2412-2416.

[6] Jibin YANG. (2018). Multi-objective optimization of energy management strategy for fuel cell tram. Journal of Mechanical Engineering, 54(22), 153.

[7] P. Li, K. Duan, Y. Dong, L. He, & Z. Tan. (2017). Energy management strategy for photovoltaic dc microgrid with distributed hybrid energy storage system. Power System Protection & Control, 45(13), 42-48.

[8] Qinpu WANG, S. You, L. Li, & C. Yang. (2017). Survey on energy management strategy for plug-in hybrid electric vehicles. Journal of Mechanical Engineering, 53(16), 1-19.

[9] Zhihu Hong, Qi Li, Ying Han, Weilin Shang, & Weirong Chen. (2018). An energy management strategy based on dynamic power factor for fuel cell/battery hybrid locomotive. International Journal of Hydrogen Energy,43(6), 3261-3272.

[10] Elkhatib Kamal, & Lounis Adouane. (2018). Intelligent energy management strategy based on artificial neural fuzzy for hybrid vehicle. IEEE Transactions on Intelligent Vehicles, 3(1), 112-125.

[11] Ali Azizivahed, Mostafa Barani, Seyed-Ehsan Razavi, Sahand Ghavidel, & Jiangfeng Zhang. (2018). A new energy storage management strategy in distribution networks utilized by photovoltaic resources. IET Generation Transmission & Distribution, 12(21), 5627-5638.

[12] T. Deng, J. Luo, H. Han, M. Wang, & D. Cheng. (2018). Adaptive energy management strategy based on driving cycle identification for hybrid electric vehicles. Journal of Xian Jiaotong University, 52(1), 77-83.

[13] Mouhcine Mendil, Antonio De Domenico, Vincent Heiries, Raphael Caire, & Nouredine Hadjsaid. (2017). Battery aging-aware energy management of green small cells powered by the smart grid. Eurasip Journal on Wireless Communications & Networking, 2017(1), 127.

[14] Xue, H. , Zhang, R. , & Hu, Y. . (2017). Full-state model predictive energy management optimization for pv-fuel cell-battery hybrid system. Power System Protection & Control,45(11), 49-58.

[15] Tamas G. Molnar, Wubing B. Qin, Tamas Insperger, & Gabor Orosz. (2018). Application of predictor feedback to compensate time delays in connected cruise control. IEEE Transactions on Intelligent Transportation Systems, 19(2), 545-559.

[16] Yan Zhao, Tianzhi Wang, & Hamid Reza Karimi. (2017). Distributed cruise control of high-speed trains. Journal of the Franklin Institute,354(14), 6044-6061.

[17] Goldman, C. V. , & Degani, A. . (2017). A team-oriented framework for human-automation interaction: implication for the design of an advanced cruise control system. Journal of Vacuum Science & Technology A, 56(1), 2354-2358.

[18] Adem F. Idriz, Arya Senna Abdul Rachman, & Simone Baldi. (2017). Integration of auto-steering with adaptive cruise control for improved cornering behavior. Iet Intelligent Transport Systems, 11(10), 667-675.

[19] R. C. Zhao, P. K. Wong, Z. C. Xie, & J. Zhao. (2017). Real-time weighted multi-objective model predictive controller for adaptive cruise control systems. International Journal of Automotive Technology, 18(2), 279-292.

[20] Y. Li, W. Wang, H. Wang, L. Xing, & S. Liu. (2017). Evaluation of the impacts of adaptive cruise control system on improving fuel efficiency of urban road traffic. Journal of Southeast University (English Edition),33(2), 230-235.

[21] Weinan Gao, & Zhong-Ping Jiang. (2017). Nonlinear and adaptive suboptimal control of connected vehicles: a global adaptive dynamic programming approach. Journal of Intelligent & Robotic Systems, 85(3-4), 597-611.

[22] TAKAHIRO HOSHINO, SATOSHI YOSHIDA, & YOSHIO HAMAMATSU. (2018). Modeling and analysis of traffic flow considering automatic gap control. Ieej Transactions on Industry Applications, 101(1), 3-15.

[23] Min ZHU. (2017). Strategy for vehicle adaptive cruise control considering the reaction headway. Journal of Mechanical Engineering, 53(24), 144.

[24] Y. Li, W. Wang, L. Xing, H. Wang, & C.-Y. Dong. (2017). Improving traffic efficiency of highway by integration of adaptive cruise control and variable speed limit control. Journal of Jilin University, 47(5), 1420-1425.

[25] Ehsan Moradi-Pari, Hossein Nourkhiz Mahjoub, Hadi Kazemi, Yaser P. Fallah, & Amin Tahmasbi-Sarvestani. (2017). Utilizing model-based communication and control for cooperative automated vehicle applications. IEEE Transactions on Intelligent Vehicles, 2(1), 38-51.

[26] Longzhe, Q. , Chenglin, L. I. , Zhengyang, F. , Jiawei, L. , & University, N. A. . (2017). Algorithm of works' decision for three arms robot in greenhouse based on control with motion sensing technology. Transactions of the Chinese Society for Agricultural Machinery, 48(3), 14-23.