Health prognosis via feature optimization and convolutional neural network for lithium-ion batteries

With the rapid expansion of the electric vehicle market, the demand for lithium-ion batteries (LIBs) is exploding. The state of health (SOH) of LIBs is receiving more widespread attention, which is the key parameter for battery health management. This paper proposes a SOH estimation method for LIBs via feature optimization and convolutional neural network (CNN) to reduce the information redundancy of existing multiple features, aimed at leveraging multiple sources of features while optimizing their combination to minimize redundancy. Firstly, multiple features are extracted from different perspectives, including electrical, thermodynamic, and electrochemical properties, to comprehensively characterize the aging of batteries. Secondly, we construct a SOH estimator based on principal component analysis (PCA) with CNN (PCA-CNN). Finally, the dimension of features is optimized with a simulated annealing algorithm (SA) under the mean-variance objective function. Moreover, Comparative experiments are conducted on the Oxford dataset for validation. The results demonstrate the effectiveness of the proposed multi-feature description method in terms of accuracy and smoothness. Compared to traditional CNN methods and fixed-dimension PCA-CNN, this estimation approach significantly improves performance, showing more than 20% and 30% increases in the key metrics of MAE and RMSE, respectively. This study successfully optimized feature combinations to reduce redundancy within the feature set while enhancing the accuracy of SOH estimation.