Electric vehicles (EVs) are considered to relieve energy crisis, and environmental problems due to their high efficiency, and low emissions, and energy management strategies (EMSs) have been extensively studied to improve the performance of hybrid energy storage systems (HESSs) for EVs. To effectively reduce HESS energy loss, and extend battery life, this paper proposes a predictive EMS (PEMS) for the battery/supercapacitor HESSs. First, the pattern sequence-based velocity predictor is presented to accurately predict the future short-term velocity profile. Second, the PEMS is proposed by formulating an HESS power split optimization problem, where the HESS energy loss, and the battery capacity loss are considered. Third, an improved chaotic particle swarm optimization algorithm is presented to solve the formulated optimization problem. Simulation results demonstrate that, compared with the benchmark, the proposed PEMS can effectively reduce the HESS energy loss, and extend the battery lifetime at the same time.

中文翻译：

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基于速度预测的电动汽车新型预测性能量管理策略

电动汽车 (EV) 因其高效、低排放而被认为可以缓解能源危机和环境问题，并且能源管理策略 (EMS) 已被广泛研究以提高电动汽车的混合储能系统 (HESS) 的性能. 为了有效减少HESS能量损失，延长电池寿命，本文提出了一种用于电池/超级电容器HESS的预测EMS（PEMS）。首先，提出了基于模式序列的速度预测器来准确预测未来的短期速度剖面。其次，PEMS 是通过制定 HESS 功率分配优化问题提出的，其中考虑了 HESS 能量损失和电池容量损失。第三，提出了一种改进的混沌粒子群优化算法来解决公式化的优化问题。