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    • Commuting-pattern-oriented Optimal Sizing of Electric Vehicle Powertrain Based on Stochastic Optimization

    Xingyu Zhou,Chao Sun,Fengchun Sun,Chuntao Zhang

    Journal of Power Sources（2022）SCI 2区

    Beijing Inst Technol

    Cited 2|Views14
    Abstract
    Owing to the dynamic randomness of traffic flow, driving patterns stochastically change from time to time and person to person, which inevitably triggers significant variability in the energy efficiency of electric vehicle (EV) powertrains. For optimizing the expected energy efficiency of the EV powertrain in probable driving operations while reducing the variability in energy efficiency, this paper engineers an integrated stochastic optimization (SO) method for the design scheme and control strategy of EV powertrains. To evaluate the candidate design scheme and identify the superior design scheme in random driving operations, an instantaneous optimal control and a Monte Carlo simulation-aided iterative searching process are developed and utilized as critical components of the SO. According to simulation validation, by optimizing the energy consumption in extreme operations, the SO improves the expectation of the energy efficiency of the EV powertrain by 26.4% and reduces the variability in energy efficiency by 90.4%. Moreover, the proposed SO has no side-effect on the energy consumption in typical/frequent driving operations. Even operated in the driving cycle from which the deterministic optimization (DO) result is obtained, the increase in energy consumption of the SO result is less than 5% compared with the energy consumption of the DO result.
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    Key words
    Electric vehicle,Stochastic optimization,Powertrain sizing,Energy management strategy,Traffic dynamics
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    【要点】：本文提出了一种基于随机优化的电动汽车动力系统设计方案与控制策略，旨在提高电动汽车动力系统的预期能源效率并减少能源效率的波动性。
    

    【方法】：作者采用了一种集成的随机优化（SO）方法，该方法结合了设计方案和控制策略，利用瞬时最优控制和蒙特卡洛模拟辅助的迭代搜索过程来评估候选设计方案。
    

    【实验】：通过模拟验证，所提出的SO方法在极端操作中优化能源消耗，使得电动汽车动力系统的能源效率预期提高了26.4%，效率的波动性降低了90.4%，且该方法对典型/频繁驾驶操作的能源消耗无副作用。在确定性优化（DO）结果得到的驾驶循环中，SO结果的能源消耗增加不超过DO结果的5%。使用的数据集名称未在摘要中提及。