Abstract：To address the limitations of insufficient feature characterization and hyperparameter sensitivity in existing state-of-health (SOH) prediction methods for lithium-ion batteries, this paper proposes a novel SOH prediction framework integrating a graph attention network (GAT), a conditional generative adversarial network (CGAN), improved beluga whale optimization (IBWO), and random forest regression (RFR). Firstly, using lithium-ion batteries from the publicly available NASA and MIT battery datasets, voltage, current, temperature, and actual SOH data are collected. Subsequently, the GAT method is employed to adaptively learn aging trends from the voltage, current, and temperature data, extracting health features (HFs) that effectively reflect battery degradation. Then, optimal HF is screened through Pearson correlation coefficient analysis. Secondly, to address the insufficient nonlinear correlation between the HFextracted by GAT and SOH, this study proposes a feature enhancement framework based on a CGAN. The generator generates feature vectors consistent with the original SOH distribution via adversarial learning, while the discriminator dynamically identifies feature sources using a convolutional architecture. Through iterative adversarial training, the correlation between generated features and degradation patterns progressively approximates the real SOH distribution. Thus, precise tracking of complex battery degradation trajectoriesis achievable. Then, the traditional beluga whale optimization (BWO) algorithm is improved by introducing three optimization strategies: Circle chaotic mapping, quasi-oppositional learning, and dynamic weight adjustment. To validate the correctness and effectiveness of the IBWO algorithm, unimodal and multimodal functions from the CEC2005 test suite are selected for performance comparisons with IBWO, traditional BWO, the whale optimization algorithm (WOA), and particle swarm optimization (PSO). The results show that the proposed IBWO algorithm achieves superior convergence speed, optimization accuracy, and stability in the search for optimal solutions. Subsequently, the IBWO algorithm is employed to optimize the hyperparameters of RFR, establishing the SOH prediction model. Finally, module-level comparative experiments demonstrate that the GAT-CGAN-IBWO-RFR model effectively keeps the SOH prediction error (PE) within [-0.005, 0.005], indicating significant error suppression. Increasing the training dataset size improves SOH prediction accuracy, with mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) consistently remaining below 0.6%. Furthermore, cross-study comparisons with existing models confirm the GAT-CGAN-IBWO-RFR model's superior performance in SOH prediction. The proposed model's strong generalization capability and high- precision SOH prediction performance are verified.
范兴明, 黄文喜, 张鑫, 诸葛致. 基于特征工程与改进随机森林回归的锂离子电池SOH预测[J]. 电工技术学报, 2026, 41(12): 4286-4297.
Fan Xingming, Huang Wenxi, Zhang Xin, Zhu Gezhi. A Hybrid State of Health Estimation Framework for Lithium-Ion Batteries Integrating Feature Engineering and Optimized Random Forest Regression. Transactions of China Electrotechnical Society, 2026, 41(12): 4286-4297.
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2026, Vol. 41 
