Electric vehicles (EVs) in severe cold regions face the real demand for fast charging under low temperatures, but low-temperature environments with high C-rate fast charging can lead to severe lithium plating of the anode material, resulting in rapid degradation of the lithium-ion battery (LIB). In this paper, by constructing an electrode–thermal model coupling solid electrolyte interphase (SEI) growth and lithium plating, the competition among different factors of capacity degradation under various ambient temperatures and C-rates are systematically analyzed. In addition, the most important cause of rapid degradation of LIBs under low temperatures are investigated, which reveal the change pattern of lithium plating with temperature and C-rate. The threshold value and kinetic law of lithium plating are determined, and a method of lithium-free control under high C-rate is proposed. Finally, by studying the average aging rate of LIBs, the reasons for the abnormal attenuation of cycle life at lower C-rates are ascertained. Through the chromaticity diagram of the expected life of LIBs under various conditions, the optimal fast strategy is explored, and its practical application in EVs is also discussed. This study can provide a useful reference for the development of high-performance and high-safety battery management systems to achieve fine management.

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 battery health conditions is a crucial challenge for development of battery management systems due to the degradation of cathode and anode materials. In this paper, a fusion of deep learning model and feature analysis methods is employed to approach accurate estimation for state of health (SOH) and remaining useful life (RUL). The differential thermal voltammetry (DTV) signal analysis is executed to pre-process the datasets from Oxford University. A deep learning model is constructed with LSTM network as the core, combined with Bayesian optimization and dropout technique. This work shows that the deep learning model could approach the SOH and RUL early estimation with the mean absolute error of RUL maintained around 0.5%. It is potential that this deep learning model, combined with DTV signal analysis methods, could approach early prediction and estimation of battery SOH and RUL, contributing to the development of the next-generation high-energy-density and highly safety commercial batteries.

With rapid development of clean energy vehicles, the health diagnosis and prognosis of lithium batteries remain challenging for practical applications. Accurate state-of-health (SOH) and remaining useful life (RUL) estimation provides crucial information for improving the safety, reliability and longevity of batteries. In this paper, a fusion of deep-learning model and feature signal analysis methods are proposed to realize accurate and fast estimation of the health conditions for battery cathodes. Specifically, the long short-term memory (LSTM) network and differential thermal voltammetry (DTV) are utilized to verify our fusion method. Firstly, the DTV feature signal analysis is executed based on battery charging and discharging data, based on which useful feature variables are extracted with Pearson correlation analysis. Next, the deep-learning model is constructed and trained with the LSTM as the core based on timeseries datasets constructed with features. Finally, the validation and error analysis of proposed model are provided, showing a max mean absolute error of 0.6%. The proposed method enables highly accurate models for SOH and RUL estimation that can be potentially deployed on cloud-end for offline battery degradation tracking.

The accurate prediction of lithium-ion battery capacity is crucial for the safe and efficient operation of battery systems. Although data-driven approaches have demonstrated effectiveness in lifetime prediction, the acquisition of lifecycle data for long-life lithium batteries remains a significant challenge, limiting prediction accuracy. Additionally, the varying degradation trends under different operating conditions further hinder the generalizability of existing methods. To address these challenges, we propose a Multi-feature Transfer Learning Framework (MF-TLF) for predicting battery capacity in small-sample scenarios across diverse operating conditions (different temperatures and C-rates). First, we introduce a multi-feature analysis method to extract comprehensive features that characterize battery aging. Second, we develop a transfer learning-based data-driven framework, which leverages pre-trained models trained on large datasets to achieve a strong prediction performance in data-scarce scenarios. Finally, the proposed method is validated using both experimental and open-access datasets. When trained on a small sample dataset, the predicted RMSE error consistently stays within 0.05 Ah. The experimental results highlight the effectiveness of MF-TLF in achieving high prediction accuracy, even with limited data.