This paper presents a nonmyopic energy management strategy (EMS) for controlling multiple energy flow in fuel cell hybrid vehicles. The control problem is solved by convex programing under a partially observable Markov decision process-based framework. We propose an average-reward approximator to estimate a long-term average cost instead of using a model to predict future power demand. Thus, the dependence between the system closed-loop performance and the model accuracy for predicting the future power demand is decoupled in the energy management design for fuel cell hybrid vehicles. The energy management scheme consists of a real-time self-learning system, an average-reward filter based on the Markov chain Monte Carlo sampling, and an action selector system through the rollout algorithm with a convex programing-based policy. The performance evaluation of the EMS is conducted via simulation studies using the data obtained from real-world driving experiments and its performance is compared with three benchmark schemes.

中文翻译：

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基于局部可观马尔可夫决策过程的燃料电池混合动力汽车能量管理

本文提出了一种非近视能量管理策略（EMS），用于控制燃料电池混合动力汽车中的多种能量流。在部分可观察到的基于马尔可夫决策过程的框架下，通过凸编程解决了控制问题。我们提出了一种平均回报近似器来估算长期平均成本，而不是使用模型来预测未来的电力需求。因此，在燃料电池混合动力汽车的能量管理设计中，系统闭环性能与模型精度之间的依赖关系可以用来预测未来的电力需求。能源管理方案包括一个实时自学习系统，一个基于马尔可夫链蒙特卡洛采样的平均奖励过滤器以及一个采用基于凸编程的策略的推出算法的动作选择器系统。