Hybrid electric vehicles (HEVs) are overactuated systems in that they include two power sources: a battery pack and an internal combustion engine. This feature of HEVs is exploited in this article to achieve accurate identification of battery parameters/states. By actively injecting currents, the state of charge, state of health, and other battery parameters can be estimated in a specific sequence to improve identification performance when compared to the case where all parameters and states are estimated concurrently using baseline currents. A dynamic programming strategy is developed to provide the benchmark results regarding how to balance the conflicting objectives corresponding to the identification and system efficiency. The tradeoff between different objectives is presented to optimize the current profile so that the richness of the signal can be ensured and the good fuel economy can be achieved. In addition, simulation results show that the root-mean-square error of the estimation can be decreased by up to 100% at a cost of less than a 2% increase in fuel consumption. With the proposed simultaneous identification and control algorithm, the parameters/states of the battery can be monitored to ensure safe and efficient operation of the battery for HEVs.

中文翻译：

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基于动态规划的串联式混合动力汽车主动信号注入同时辨识与控制

混合动力电动汽车 (HEV) 是过驱动系统，因为它们包括两个动力源：电池组和内燃机。本文利用 HEV 的这一特性来实现电池参数/状态的准确识别。通过主动注入电流，与使用基线电流同时估计所有参数和状态的情况相比，可以按特定顺序估计充电状态、健康状态和其他电池参数，以提高识别性能。开发动态编程策略以提供关于如何平衡与识别和系统效率相对应的冲突目标的基准结果。提出了不同目标之间的权衡以优化电流分布，从而确保信号的丰富性并实现良好的燃油经济性。此外，仿真结果表明，估计的均方根误差最多可降低 100%，而油耗增加不到 2%。通过所提出的同步识别和控制算法，可以监控电池的参数/状态，以确保 HEV 电池的安全高效运行。