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%).

Lithium-ion batteries (LiB) are widely used in electric vehicles (EVs) and battery energy storage systems, and accurate state estimation relying on the relationship between battery Open-Circuit-Voltage (OCV) and State-of-Charge (SOC) is the basis for their safe and efficient applications. To avoid the time-consuming lab test needed for obtaining OCV-SOC curves, this study proposes a data-driven universal method by using operation data collected onboard about the variation of OCV with ampere-hour (Ah). To guarantee high reliability, a series of constraints have been implemented. To verify the effectiveness of this method, the constructed OCV-SOC curves are used to estimate battery SOC and State-of-Health (SOH), which are compared with data from both lab tests and EV manufacturers. Results show that a higher accuracy can be achieved in the estimation of both SOC and SOH, for which the maximum deviations are less than 3.0% and 2.9% respectively.

The safety of lithium-ion batteries in electric vehicles (EVs) is attracting more attention. To ensure battery safety, early detection is necessary of a soft short circuit (SC) which may evolve into severe SC faults, leading to fire or thermal runaway. This paper proposes a soft SC fault diagnosis method based on the extended Kalman filter (EKF) for on-board applications in EVs. In the proposed method, the EKF is used to estimate the state of charge (SOC) of the faulty cell by adjusting a gain matrix based on real-time measured voltages. The SOC difference between the estimated SOC and the calculated SOC through coulomb counting for the faulty cell is employed to detect soft SC faults, and the soft SC resistance values are further identified to indicate the degree of fault severity. Soft SC experiments are developed to investigate the characteristics of a series-connected battery pack under different working conditions when one battery cell in the pack is short-circuited with different resistance values. The experimental data are acquired to validate the proposed soft SC fault diagnosis method. The results show that the proposed method is effective and robust in quickly detecting a soft SC fault and accurately estimating soft SC resistance.