Xian Yanhua, Li Mingyang, Huang Jiayin
Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips, School of Electronic and Information Engineering, Guangxi Normal University, Guilin, China.
Key Laboratory of Nonlinear Circuits and Optical Communications (Guangxi Normal University), Education Department of Guangxi Zhuang Autonomous Region, Guilin, China.
PLoS One. 2025 May 23;20(5):e0324868. doi: 10.1371/journal.pone.0324868. eCollection 2025.
Existing state of health (SOH) estimation methods for lithium-ion batteries predominantly extract health features (HF) from constant current (CC) and constant voltage (CV) charging phases. Nevertheless, CC charging phase feature extraction is susceptible to the randomness of the initial charging stage. By contrast, data during the constant voltage (CV) charging stage are preserved intact. The complexity and noise interference of battery data make it difficult to accurately extract health features, and it is necessary to develop effective methods to process the data and extract representative features. In response to this issue, this paper proposes an SOH estimation method for extracting HF at the end of the CV charging stage and optimizes the Backpropagation Neural Network (BPNN). Firstly, the current curve during the CV charging stage was transformed into the differential current curve (dQ/dI curve), from which two HFs were extracted. Secondly, addressing the issue of weight and threshold initialization in BPNN, the Coati Optimization Algorithm (COA) was employed to optimize the network (COA-BPNN). Finally, validation was conducted using two publicly available datasets. The experimental results demonstrate that the proposed method exhibits high accuracy in estimating the SOH of batteries under various environmental temperatures and charging rate conditions. Compared with the traditional BPNN method, the COA-BPNN method reduces the maximum root mean square error and average absolute error of the estimated results to 0.22% and 0.16%, respectively.
现有的锂离子电池健康状态（SOH）估计方法主要从恒流（CC）和恒压（CV）充电阶段提取健康特征（HF）。然而，CC充电阶段的特征提取易受初始充电阶段随机性的影响。相比之下，恒压（CV）充电阶段的数据则完整保留。电池数据的复杂性和噪声干扰使得准确提取健康特征变得困难，因此有必要开发有效的数据处理方法并提取具有代表性的特征。针对这一问题，本文提出了一种在CV充电阶段结束时提取HF的SOH估计方法，并对反向传播神经网络（BPNN）进行了优化。首先，将CV充电阶段的电流曲线转换为微分电流曲线（dQ/dI曲线），从中提取两个HF。其次，针对BPNN中的权重和阈值初始化问题，采用浣熊优化算法（COA）对网络进行优化（COA-BPNN）。最后，使用两个公开可用的数据集进行验证。实验结果表明，该方法在各种环境温度和充电速率条件下对电池SOH的估计具有较高的准确性。与传统的BPNN方法相比，COA-BPNN方法将估计结果的最大均方根误差和平均绝对误差分别降低到了0.22%和0.16%。
