Chengwei Ge, Chunling Wu, Zhen Zhang, Kaile Cao, Li Wang, Mingwei Gao, Xiangming He
Accurate state-of-health estimation of lithium-ion batteries under high-temperature conditions (40-50 °C) remains challenging because of accelerated electrochemical degradation and strongly nonlinear aging patterns. This paper presents a hybrid Ridge regression-convolutional bidirectional long short-term memory framework with a dual-level transfer learning strategy. A Ridge regression baseline first captures the global degradation trend, after which a convolutional bidirectional long short-term memory network learns the nonlinear residuals. For cross-battery adaptation, Ridge coefficients are transferred through prior-regularized regression, and the pre-trained network is fine-tuned using limited target-domain data. The method is validated on cycling datasets from three institutions, namely Tsinghua University, the University of Oxford, and Tongji University, covering 15 batteries under temperatures up to 50 °C. Four health-related features are extracted and adaptively denoised using locally weighted scatterplot smoothing. In single-battery extrapolation, the proposed method achieves a root mean square error as low as 0.0009 on cell B6 at 50 °C, outperforming random forest, long short-term memory, bidirectional long short-term memory, and Ridge regression by 91.1%, 88.6%, 87.7%, and 82.0%, respectively. A cross-battery ablation experiment showed that the dual-level transfer learning strategy reduced the root mean square error from approximately 0.009 to 0.0028, whereas increasing network complexity alone yielded only marginal improvement. A further hierarchical ablation showed that jointly adapting the Ridge prior and the residual network achieved a mean RMSE of 0.004325, representing reductions of 9.39%, 4.14%, and 6.92% relative to the no-adaptation, Ridge-only adaptation, and residual-network-only adaptation configurations, respectively.