The state of health (SOH) of lithium-ion batteries (LIBs) is a critical parameter of the battery management system. Because of the complex internal electrochemical properties of LIBs and uncertain external working environment, it is difficult to achieve an accurate SOH determination. In this paper, we have proposed a novel SOH estimation method by using a prior knowledge-based neural network (PKNN) and the Markov chain for a single LIB. First, we extract multiple features to capture the battery aging process. Due to its effective fitting ability for complex nonlinear problems, the neural network with a prior knowledge-based optimization strategy is adopted for the battery SOH prediction. The Markov chain, with the advantageous prediction performance for the long-term system, is established to modify the PKNN estimation results based on the prediction error. Experimental results show that the maximum estimation error of the SOH is reduced to less than 1.7% by adopting the proposed method. By comparing with the group method of data handling and the back-propagation neural network in conjunction with the Levenberg–Marquardt algorithm, the proposed estimation method obtains the highest SOH accuracy.

中文翻译：

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基于先验知识的神经网络和马尔可夫链的锂离子电池健康状态估计方法

锂离子电池 (LIB) 的健康状态 (SOH) 是电池管理系统的关键参数。由于LIBs复杂的内部电化学性质和不确定的外部工作环境，很难实现准确的SOH测定。在本文中，我们通过使用基于先验知识的神经网络 (PKNN) 和单个 LIB 的马尔可夫链，提出了一种新的 SOH 估计方法。首先，我们提取多个特征来捕捉电池老化过程。由于其对复杂非线性问题的有效拟合能力，神经网络采用基于先验知识的优化策略进行电池SOH预测。马尔可夫链，对长期系统具有优越的预测性能，建立基于预测误差修改PKNN估计结果。实验结果表明，采用所提出的方法将SOH的最大估计误差降低到1.7%以下。通过与数据处理的分组方法和反向传播神经网络结合Levenberg-Marquardt算法进行比较，所提出的估计方法获得了最高的SOH精度。