Abstract：To address the problems of low accuracy and poor robustness of traditional data-driven battery state of health prediction model, this paper develops a time series prediction model fusing both nonlinear and linear branches. The nonlinear time series prediction branch is formed by a one-dimensional convolutional neural network with a multi-size parallel structure in series with a bidirectional gated recurrent neural network, and the linear branch is constructed by an autoregressive model. Two branches in parallel output prediction results through a fully connected layer. The above prediction model has the generalization ability of the nonlinear part and the memory ability of the linear part, and is more sensitive to the change of input amplitude. The whale optimization algorithm is used to effectively search the optimal model hyper-parameters. The effectiveness and superiority of the linear nonlinear fusion prediction model proposed in this paper are verified by comparing existing models and ablation experiments.
崔孝凯, 王庆芝, 刘其朋. 基于数据驱动的锂电池健康状态预测[J]. 复杂系统与复杂性科学, 2024, 21(3): 154-159.
CUI Xiaokai, WANG Qingzhi, LIU Qipeng. A Data-driven Model for Prediction of Lithium Battery State of Health[J]. Complex Systems and Complexity Science, 2024, 21(3): 154-159.
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