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The success of deep learning in the field of state‐of‐health (SOH) estimation relies on a large amount of battery data and the fact that all data possess the same probability distribution. While in real situations, a model based on one working condition data set may not be valid for another working condition data set due to distribution differences. Therefore, this article proposes a transfer learning method using soft‐dynamic time warping (soft‐DTW) as the statistical feature in the feature transfer method, called soft‐DTW domain adaptation network (SDDAN). By combining the prediction error with the time‐series gap in the model training process, the feature transformation can make the obtained prediction results more similar to the source domain results, which can help us to obtain better prediction results in the target domain. Experimental results show that SDDAN can effectively predict the SOH of Li‐ion batteries and significantly improve the performance of feature learning and knowledge transfer.

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Accurate estimation of the state of charge (SOC) is critical for battery management systems. A backpropagation neural network (BPNN) based on a modified fuzzy Sunday algorithm is proposed to improve the accuracy of SOC predictions of lithium-ion batteries (LIBs). The road condition information relating to the data is obtained using the fuzzy Sunday algorithm, and the acquired feature information is used to estimate SOC using BPNN based on the Levenberg–Marquardt (L–M) training process. The change from exact character matching to fuzzy number matching is an improvement to the Sunday algorithm. The quantification of the road condition is innovatively integrated into the neural network. At present, this kind of feature is new to the estimation process, and our experiment proved that the effect is good. To quickly estimate the SOC under different driving conditions, the same network was used to predict the data of different road conditions. In addition, a strategy is proposed for SOC estimation under unknown road conditions, which improves the estimation accuracy. Studies have shown that the model used in the experiment is more accurate than other machine learning models. This model assures prediction accuracy, reliability, and timeliness.