基于非参数模型与粒子滤波的锂电池SOH估计

健康状态(state of health, SOH)是电池管理系统的重要参考依据，准确的SOH估计对保证电池安全稳定运行具有重大意义，其中提取可靠有效的健康特征描述电池老化状态以及构建精确稳定的估计模型是目前面临的主要问题。为了提高SOH估计精度，提出了一种基于模糊熵和粒子滤波(particle filter, PF)的锂离子电池SOH估计方法。首先，通过分析电池老化过程中的放电电压数据，提取模糊熵值作为电池的老化特征；其次，基于代谢灰色模型(metabolic grey model, MGM)和时间卷积网络(temporal convolutional network, TCN)构建描述锂电池老化特征的非参数状态空间模型；最后，通过PF实现锂电池SOH的闭环估计。此外，利用NASA锂电池数据集对所提出的SOH估计方法进行了验证，并与该领域其他方法进行对比实验。结果表明，所提方法最大估计误差在5%左右，相比于同类方法其估计精度提升了约50%,且在不同训练周期数条件下表现出较好的鲁棒性，验证了所提方法的可行性与优越性。

SOH estimation of lithium-ion battery based on non-parametric model and particle filter

The state of health (SOH) is an important index for battery management system, and accurate SOH estimation is of great significance for ensuring safe and stable operation of battery. Extracting reliable and effective health features to describe the aging state of battery and constructing accurate and stable estimation model are the main problems we face at present. In order to improve the accuracy of SOH estimation, a fuzzy entropy and particle filter (PF) based SOH estimation method for lithium-ion battery is proposed. Firstly, the fuzzy entropy value is extracted as the aging characteristic of the battery by analyzing the discharge voltage data during the aging process. Secondly, a non-parametric state-space model to describe the aging characteristics of lithium-ion battery is constructed based on the metabolic grey model (MGM) and the temporal convolutional network (TCN). Finally, the closed-loop SOH estimation of lithium-ion battery is realized by PF. In addition, the proposed SOH estimation method is validated using the NASA lithium-ion battery datasets and compared with other methods in the field. The results show that the maximum estimation error of the proposed method is about 5%, the estimation accuracy is improved by about 50% compared with similar methods, and the proposed method exhibits good robustness under different training cycles, which verifies the feasibility and superiority of the proposed method.