The accurate estimation of the battery state of health (SOH) is crucial for the dependability and safety of battery management systems (BMS). The generality of existing SOH estimation methods is limited as they tend to primarily consider information from single-source features. Therefore, a novel method for integrating multi-feature collaborative analysis with deep learning-based approaches is proposed in this research. First, several battery degradation features are obtained through differential thermal voltammetry (DTV) analysis, singular value decomposition (SVD), incremental capacity analysis (ICA), and terminal voltage characteristic (TVC) analysis. The features highly related to SOH are selected as inputs for the deep learning model based on the results of a Pearson correlation analysis. The SOH estimation is achieved by developing a deep learning framework cored by long short-term memory (LSTM) neural network (NN), which integrates multi-source features as an input. A suggested method is validated using NASA and Oxford Battery Degradation datasets. The results demonstrate that the presented model provides great SOH estimation accuracy and generality, where the maximum root mean square error (RMSE) is less than 1%. Based on a cloud computing platform, the proposed method can be applied to provide a real-time prediction of battery health, with the potential to enhance battery full lifespan management.

The accurate estimation of the state of charge (SOC) plays a crucial role in ensuring the range of electric vehicles (EVs) and the reliability of the EVs battery. However, due to the dynamic working conditions in the implementation of EVs and the limitation of the onboard BMS computational force, it is challenging to achieve a reliable, high-accuracy and real-time online battery SOC estimation under diverse working scenarios. Therefore, this study proposes an end-cloud collaboration approach of lithium-ion batteries online estimate SOC. On the cloud-side, a deep learning model constructed based on CNN-LSTM is deployed, and on the end-side, the coulomb counting method and Kalman’s filter are deployed. The estimation results at both sides are fused through the Kalman filtering algorithm, realizing high-accuracy and real-time online estimation of SOC. The proposed approach is evaluated with three dynamic driving profiles and the results demonstrate the proposed approach has high accuracy under different temperatures and initial errors, where the root means square error (RMSE) is lower than 1.5% and the maximum error is lower than 5%. Furthermore, this method could achieve high-accuracy and real-time SOC online estimation under the cyber hierarchy and interactional network (CHAIN) framework and can be extended to multi-state collaborative online estimation.