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XI Li-he, ZHANG Xin, GENG Cong, XUE Qi-cheng. Energy management strategy optimization of extended-range electric vehicle based on dynamic programming[J]. Journal of Traffic and Transportation Engineering, 2018, 18(3): 148-156. doi: 10.19818/j.cnki.1671-1637.2018.03.015
Citation: XI Li-he, ZHANG Xin, GENG Cong, XUE Qi-cheng. Energy management strategy optimization of extended-range electric vehicle based on dynamic programming[J]. Journal of Traffic and Transportation Engineering, 2018, 18(3): 148-156. doi: 10.19818/j.cnki.1671-1637.2018.03.015
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Energy management strategy optimization of extended-range electric vehicle based on dynamic programming

doi: 10.19818/j.cnki.1671-1637.2018.03.015

  • School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Bei j ing 100044, China

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    Abstract
  • A modified dynamic programming algorithm was proposed.A future reachable states array was determined based on the constraints.The transfer costs among discretized states were calculated to guarantee the solving accuracy and reduce the off-line calculation burden.An energy management strategy for an extended-range electric vehicle was designed using a modified dynamic programming algorithm.Based on the energy management problem features, a dynamic system model was constructed, a system state equation for solving global optimization problems was determined, the battery state of charge (SOC) was selected as a state variable, and the extender output power was selected as a control variable.During the iterative calculation process, the cost of engine fuel and the battery energy were added in the objective function.Different driving-distance simulation cycles were constructed based on the Beijing arterial road cycle toobtain the optimal distribution result of required motor power.The control rules of extender start-stop corresponding to the battery SOC and required motor power were extracted, the distributed regulation between extender power split ratio and required power was fitted using the least square method, and the energy management strategy based on the optimal rules was established.Simulation result indicates that for the 100 km driving distance simulation cycle, the calculation time of the modified dynamic programming algorithm is 7 239 s, and the calculation efficiency improves by 78.2% compared to the classic dynamic programming algorithm.The optimal rule-based energy-management strategy has a similar control performance with the modified dynamic programming algorithm.The SOC errors of the two control strategies are within 2.5%.Compared to the charging deplete/charging sustain control strategy, the optimal rule-based control strategy improves the economy performance by approximately 5.4% and the fuel economy by approximately 7.9%.

     

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