论文标题
锂离子电池的基于学习的基于学习的快速充电控制策略
Reinforcement Learning-based Fast Charging Control Strategy for Li-ion Batteries
论文作者
论文摘要
锂离子电池社区面临的最关键的挑战之一是搜索最短时间充电而不会不可逆地损害细胞。根据电池模型，这可能落在解决大规模的非线性最佳控制问题上。在这种情况下，文献中已经提出了几种基于模型的技术。但是，这种策略的有效性受模型的复杂性和不确定性受到很大的限制。此外，很难跟踪与衰老有关的参数并重新调整基于模型的控制策略。为了克服这些限制，在本文中，我们提出了一种依赖于无模型的增强学习框架的安全限制的快速充电策略。特别是，我们专注于基于政策梯度的参与者 - 批判算法，即深层确定性政策梯度（DDPG），以处理连续的动作和集合。当减少的电化学模型被视为真实植物时，在模拟中评估了该提案的有效性。最后，考虑到国家减少的考虑，强调了拟议策略的在线适应性，以应对环境参数的变化。
One of the most crucial challenges faced by the Li-ion battery community concerns the search for the minimum time charging without irreversibly damaging the cells. This can fall into solving large-scale nonlinear optimal control problems according to a battery model. Within this context, several model-based techniques have been proposed in the literature.However, the effectiveness of such strategies is significantly limited by model complexity and uncertainty. Additionally, it is difficult to track parameters related to aging and re-tune the model-based control policy. With the aim of overcoming these limitations, in this paper we propose a fast-charging strategy subject to safety constraints which relies on a model-free reinforcement learning framework. In particular, we focus on the policy gradient-based actor-critic algorithm, i.e., deep deterministic policy gradient (DDPG), in order to deal with continuous sets of actions and sets. The validity of the proposal is assessed in simulation when a reduced electrochemical model is considered as the real plant. Finally, the online adaptability of the proposed strategy in response to variations of the environment parameters is highlighted with consideration of state reduction.
