Hybrid energy storage system supplies a feasible solution to battery peak current reduction by introducing supercapacitor as auxiliary energy source. Energy management control strategy is a key technology for guaranteeing performance. In this paper, we describe a predictive energy management strategy for battery and supercapacitor hybrid energy storage systems of pure electric vehicles. To utilize the supercapacitor reasonably, Markov chain model is proposed to predict the future load power during a driving cycle. The predictive results are subsequently used by power distribution strategy, which is designed using a low-pass filter and a fuzzy logic controller. The strategy model is developed under MATLAB/Simulink software environment. To validate the performance of the proposed control strategy, a comparison test is implemented based on a 72 V rated voltage hybrid energy storage system experimental platform. The results indicate that the battery peak currents by proposed predictive control strategy are reduced by 26.32%, 28.21% and 27.12% under the UDDS, SC03 and NEDC three driving cycles respectively.

中文翻译：

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纯电动汽车电池和超级电容混合储能系统预测能量管理策略的实现

混合储能系统通过引入超级电容器作为辅助能源，为降低电池峰值电流提供了可行的解决方案。能源管理控制策略是保证性能的关键技术。在本文中，我们描述了纯电动汽车电池和超级电容器混合储能系统的预测能量管理策略。为了合理利用超级电容器，提出马尔可夫链模型来预测未来行驶周期的负载功率。预测结果随后由配电策略使用，该策略使用低通滤波器和模糊逻辑控制器设计。策略模型是在MATLAB/Simulink软件环境下开发的。为了验证所提出的控制策略的性能，基于72V额定电压混合储能系统实验平台进行对比试验。结果表明，在UDDS、SC03和NEDC三个工况下，所提出的预测控制策略的电池峰值电流分别降低了26.32%、28.21%和27.12%。