Accurate monitoring of the state of health (SOH) of lithium-ion batteries is vital for electric vehicles. However, it faces considerable challenges owing to the uncertainty of operating conditions and limited computational power available on board. An efficient method is introduced for estimating the SOH of a battery under multi-dynamic operating conditions using a fused-model approach. First, this method is based on the electrochemical theory and a simplified electrochemical model is established to characterize the physicochemical reactions in the battery. Second, the areas of voltage and state of charge errors under different aging cycles are adopted as the aging features, whereas the model parameters remained constant. Integrating these features with those that responded to the dynamic operating conditions resulted in more stable fused features under multi-dynamic operating conditions. Finally, the multiple fused features are used as inputs to a support vector regression model, enabling precise SOH estimation under diverse dynamic operating conditions. This framework is highly generalizable without the need to update the model parameters or use complex algorithms, and the computational effort is sufficiently small to achieve accurate estimation results, with a root mean square error and mean absolute error consistently within 1%.

Manufacturing defects in lithium-ion batteries are a major cause of thermal runaway, with copper foreign matter being one of the most common defects on battery production lines. Such defects can induce internal short circuits (ISCs) that may trigger thermal runaway, posing significant safety risks. The occurrence of ISCs in copper defect batteries is closely associated with the charging stages during formation and cycling processes. However, the abnormal characteristics during these processes are not yet fully understood, and existing methods for detecting copper matter in batteries primarily rely on offline self-discharge measurements. In this study, a detailed analysis of abnormal current and voltage characteristics in copper defect batteries during formation and cycling is conducted, a multi-stage defect detection method is proposed. The proposed method achieves detection rates of 84.2% in the formation stage, 84.2% in the single-cycle stage, and 68.4% in the multi-cycle stage. Using this multi-stage detection method, all copper defect batteries, including those prone to sudden ISCs, are successfully identified. Furthermore, the proposed method requires no complex calculations or additional equipment and relies only on standard current and voltage data collected during formation and cycling. This provides an efficient and practical solution for detecting copper foreign matter defects in lithium-ion batteries, thereby enhancing overall battery safety.

Supercapacitors are widely used in transportation and renewable energy fields due to their high power density, stable cycling performance, and rapid charge–discharge capabilities. To ensure efficient applications of supercapacitors, accurately predicting their degradation trajectories and remaining useful life (RUL) is crucial. For this purpose, a physics-informed neural network (PINN) model is developed using Long Short-Term Memory (LSTM) as the base architecture. Physical equations are embedded into the loss function to ensure consistency with domain knowledge, allowing the loss function to incorporate both physical and data-driven components. The balance between these two loss components is dynamically determined through Bayesian optimization, to enhance the model's accuracy further. Validation results show a root mean square error (RMSE) of 3 ​mF (the rated capacity is 1 F) in the degradation trajectory prediction and a RMSE of 269 cycles (the average cycle life is 5180 cycles) for the RUL. Ablation experiments were conducted to validate the effectiveness of integrating physical information into the LSTM framework. Results demonstrate that the proposed model outperforms both the data-driven LSTM method and the empirical equation-based method that the PINN model can reduce the RMSE by 85% and 87.5% for degradation trajectory prediction, and 86.5% and 94.6% for RUL prediction, respectively. In addition, a comparison with advanced models demonstrates that our model reduces the requirement significantly on training data while maintaining comparable prediction accuracy, which favors scenarios where data is scarce.

Lithium plating directly affects the fast-charging ability and safety of electric vehicles. However, existing lithium plating detection methods cannot meet the industry's needs for timeliness, quantification, and robustness, which seriously restricts the development of electric vehicles and emission reduction. This article provides suggestions for the future development of lithium plating detection methods in different periods of time to support the revolution of the next-generation electric vehicle batteries.

Lithium-ion batteries are currently the most widely used energy storage devices due to their superior energy density, long lifespan, and high efficiency. However, the manufacturing defects, caused by production flaws and raw material impurities can accelerate battery degradation. In extreme cases, these defects may result in severe safety incidents, such as thermal runaway. Metal foreign matter is one of the main types of manufacturing defects, frequently causing internal short circuits in lithium-ion batteries. Among these, copper particles are the most common contaminants.

This paper addresses the safety risks posed by manufacturing defects in lithium-ion batteries, analyzes their classification and associated hazards, and reviews the research on metal foreign matter defects, with a focus on copper particle contamination. Furthermore, we summarize the detection methods to identify defective batteries and propose future research directions to address metal foreign matter defects.

Electric vehicles and battery energy storage are effective technical paths to achieve carbon neutrality, and lithium-ion batteries (LiBs) are very critical energy storage devices, which is of great significance to the goal. However, the battery’s characteristics of instant degradation seriously affect its long life and high safety applications. The aging mechanisms of LiBs are complex and multifaceted, strongly influenced by numerous interacting factors. Currently, the degree of capacity fading is commonly used to describe the aging of the battery, and the ratio of the maximum available capacity to the rated capacity of the battery is defined as the state of health (SOH). However, the aging or health of the battery should be multifaceted. To realize the multi-dimensional comprehensive evaluation of battery health status, a novel SOH estimation method driven by multidimensional aging characteristics is proposed through the improved single-particle model. The parameter identification and sensitivity analysis of the model were carried out during the whole cycle of life in a wide temperature environment. Nine aging characteristic parameters were obtained to describe the SOH. Combined with aging mechanisms, the current health status was evaluated from four aspects: capacity level, lithium-ion diffusion, electrochemical reaction, and power capacity. The proposed method can more comprehensively evaluate the aging characteristics of batteries, and the SOH estimation error is within 2%.

Battery capacity assessment is a crucial research direction in the field of lithium-ion battery applications. In the previous research, a novel data-driven state of health (SOH) estimation method based on the voltage relaxation curve at full charging is developed. The experimental results have shown the evidence of the superiority of accurate battery SOH estimation based on physical features derived from equivalent circuit models (ECMs). However, the earlier research has limitations in estimating battery capacity with a diversity of battery charging states of charge. This study represents an extension of the previous work, aiming to investigate the feasibility of this technology for battery degradation evaluation under various charging states so that the application capability in practice is enhanced. In this study, six ECM features are extracted from 10-min voltage relaxation data across varying charging states to characterize the battery degradation evolution. Gaussian process regression (GPR) is employed to learn the relationship between the physical features and battery SOH. Experimental results under 10 different state of charge (SOC) ranges show that the developed methodology predicts accurate battery SOH, with a root mean square error being 0.9%.

To improve the safety of electric vehicles and battery energy storage systems, early prediction of thermal runaway (TR) is of great significance. This work proposes a novel method for early warning and short-term prediction of the TR. To give warning of TR long time in advance, a variety of battery models are established to extract key features, such as Pauta feature and Shannon entropy of voltage deviation, and then local outlier factor algorithm is used for feature fusion to detect abnormal cells. For the short-term prediction, the predefined threshold and variation rates are used. By measuring the real-time signals, such as voltage and temperature, their variation rates are calculated, based on which TR can be predicted exactly. The real data including TR from an electric vehicle are used to verify the method that it can give a warning on TR long time before it happens up to 74 days. This is remarkable for providing replacement recommendations for abnormal cells. It can also predict the occurrence of TR 33 seconds in advance to ensure the safe use of batteries.

With the wide application of the LFP lithium-ion batteries, more attention is paid to the battery life and future aging behaviors as the safety and performance of the battery are guaranteed by accurate battery aging monitoring. In recent years, long-term aging trajectory prediction of the lithium-ion battery is always a challenge due to its complex nonlinear aging behaviors especially the aging behaviors in the two aging stages are quite different when the battery experiences the two-stage aging process under fast-charging conditions. Thus, it is harder to achieve accurate long-term aging trajectory prediction of the LFP lithium-ion batteries on the condition of the two-stage aging process. To address it, a novel transfer learning strategy combined with the cycle life prediction technology is presented in this paper. Specifically, a new cycle life prediction method is proposed based on feature extraction and deep learning technology and achieves accurate cycle life prediction. The transfer learning is started by developing a base aging model offline to learn the information of the two-stage aging process. Then, taking the predicted cycle life as its prior information, the Bayesian model migration technology is employed to predict the aging trajectory accurately, and the uncertainty of the aging trajectory is quantified. Two batches of the battery datasets are used for performance evaluation and comparison with two benchmarks. It is novel to combine the cycle life prediction and transfer learning technique to achieve accurate two-stage aging trajectory prediction with only a few data available (first 30%).