Jiuchun Jiang

To address the issue of thermal runaway in ternary lithium-ion batteries under overcharging conditions, this paper establishes a multi-physics simulation model based on electrochemical–thermal coupling theory to systematically investigate the thermal behavior and runaway mechanisms of the battery. A P2D electrochemical model and the Bernardi heat generation model were combined to construct an electrochemical–thermal coupling model suitable for overcharging conditions. Simulation results indicate that under normal charging conditions, the battery temperature rise is small and uniformly distributed; however, under overcharging conditions, side reactions significantly intensify, leading to a rapid increase in heat generation. The battery temperature exhibits a distinct inflection point and rises rapidly, displaying typical thermal runaway characteristics. Charging rate and ambient temperature have a significant impact on the thermal runaway process; both high charging rates and high ambient temperatures accelerate heat accumulation and reduce battery thermal safety. The study demonstrates that the established model effectively reveals the evolution of thermal runaway in overcharged ternary lithium-ion batteries, providing a theoretical basis for battery thermal management design and safety early warning systems.

Electrochemical energy storage serves as a foundational technology in contemporary electrical energy storage systems, with its operational safety and stability being crucial to socio-economic development. The estimation of the state of health (SOH) of energy storage batteries is an essential component for ensuring system safety warnings and lifecycle management. To address the challenges of redundant health feature dimensions, insufficient correlation of influencing factors, and limited prediction accuracy in existing SOH estimation methods, in this paper, a novel state of health estimation framework is introduced, leveraging an Improved Gray Wolf Optimization (IGWO) algorithm to optimize the parameters of a Support Vector Machine (SVM). This model achieves precise prediction of battery health states by extracting multidimensional health features, including the differential temperature, incremental capacity, time interval of equal charge voltage difference (DT-IC-TIECVD) and implementing the improved gray wolf optimization algorithm with support vector machine algorithm (IGWO-SVM). Validated on the Oxford battery aging dataset, the proposed model achieves mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination (R²) values of 0.43%, 0.55%, and 0.99, respectively. These results confirm the high accuracy and feasibility of the proposed method, while also providing a novel technical pathway for the health management of energy storage batteries.

Accurate estimation of the State of Charge (SOC) of lithium-ion batteries is one of the core functions of a battery management system and is of great significance for ensuring the safe operation of electric vehicles and optimizing energy utilization. However, due to the strong nonlinearity, time-varying characteristics, and interference from complex operating conditions within the battery, high-precision SOC estimation faces severe challenges. To address the problems that a single data-driven method lacks physical constraints and a single model-driven method struggles to characterize complex nonlinearities, this paper proposes a series-connected LSTM-UKF fusion estimation method. This method first utilizes a Long Short-Term Memory network to learn the dynamic characteristics of the battery from historical voltage and current data, capturing the long-term dependencies of SOC changes to achieve an initial prediction. Subsequently, using this predicted value as the observation input, an Unscented Kalman Filter based on a second-order RC equivalent circuit model is introduced for optimal state correction, effectively suppressing model uncertainty and measurement noise. Simulation validation under various dynamic conditions, such as constant current discharge and FUDS, shows that compared to single LSTM or UKF algorithms, the proposed fusion method has significant advantages in estimation accuracy, convergence speed, and robustness. Its root mean square error is reduced to 0.0031, and it maintains stable estimation performance under different operating conditions. This study provides an effective data-model fusion solution for high-precision SOC estimation of lithium-ion batteries under complex operating conditions.

Lithium-ion batteries are widely applied in transportation, communication, and other fields. Nevertheless, during prolonged cycling operation, internal electrochemical reactions inevitably lead to the degradation of the state-of-health (SOH). To ensure the reliability and safety of lithium-ion batteries, accurate SOH estimation is of critical importance. Nevertheless, under practical operating conditions, obtaining fully recorded charge–discharge data is often impractical. Motivated by the practical charging behaviors of lithium-ion batteries, this paper proposes a practical SOH estimation method based on incremental capacity analysis, dynamic time warping (DTW), and gradient-boosting regression trees (GBRTs). Three health indicators—interval incremental capacity features, local capacity–voltage curve similarity, and segmented voltage curve similarity—are extracted. The proposed method requires only 0.13 V and 0.07 V voltage windows on the Oxford and CALCE datasets. The effectiveness of the proposed model is verified across both public datasets and laboratory test data. Experimental results demonstrate RMSE values of approximately 2.5% and 2.0%, respectively. Compared with mainstream SOH estimation algorithms, the proposed approach delivers comparable accuracy while achieving training time reductions of up to 57.6% and 91.9% relative to GPR and SVM, making it suitable for real-time battery management systems.

Lithium iron phosphate (LiFePO₄, LFP) batteries dominate grid-scale energy storage, yet their cycle life is capped by its capacity fade issues. Conventional failure workflows suffer from redundant tests, high cost, and long turnaround time because the underlying mechanisms remain unclear. Herein, multi-scale characterization coupled with electrochemical tests have been quantitatively established to reveal four synergistic fade modes of LFP: active-Li loss, FePO₄ secondary-phase formation, SEI rupture, and particle fracture. A two-tier “screen–validate” protocol is proposed to accurately and efficiently disclose its mechanism. In the screening tier, capacity, cyclic voltammetry, electrochemical impedance spectroscopy, low-magnification scanning electron microscopy, and snapshot X-ray diffraction (XRD) rapidly flag the most probable failure cause. The validation tier then deploys mechanism-matched in situ/ex situ tools (operando XRD, TEM, XPS, ToF-SIMS, etc.) to build a comprehensive evidence chain of dynamic structural evolution, materials loss tracking, and quantitative proof. The streamlined workflow preserves scientific rigor and reproducibility while cutting analysis time and cost, offering a closed-loop route for fast failure diagnosis and targeted optimization of next-generation LFP batteries.

State-of-energy (SOE) estimation helps to enhance the safety of battery operation and predict vehicle range. However, the voltage plateau of the LiFePO4 (LFP) battery presents a significant challenge for SOE estimation. Therefore, this paper introduces a significantly varying mechanical force feature to tackle the flat voltage curve in the mid-SOE region. A fusion model that integrates a bidirectional long short-term memory (BiLSTM) network, particle swarm optimization (PSO), and Kalman filter (KF) algorithm is proposed for SOE estimation. The BiLSTM is applied to fully capture the temporal dependencies from inputs to output over both local and long cycles. Subsequently, PSO is employed to optimize the parameters of KF, which is utilized to smooth the results of the BiLSTM network, thereby achieving highly accurate SOE estimation. Experimental results across different operating conditions and temperatures reveal that the introduction of mechanical force significantly improves SOE estimation accuracy. Compared to models using only traditional electrical and thermal features, the model with the introduction of mechanical force achieves average improvements of 67.06%, 66.38%, and 66.46% for the root mean square error (RMSE), maximum absolute error (MAXE), and mean absolute error (MAE), respectively. Moreover, the generalizability and robustness of the proposed method are further confirmed by the comparison of different models and preload forces.

Impedance spectroscopy can characterize a variety of states within a battery and shows great potential for application in safety warning and condition assessment, but there is a lack of an effective online monitoring method. Therefore, this article proposes a method for online measurement of impedance spectroscopy based on harmonic injection of equalization currents, which superimposes different frequency sinusoidal perturbation signals on the original equalization currents through process reconstruction and collaborative control, and realizes real-time impedance spectroscopy monitoring at the level of single cell without additional hardware. In order to verify the validity of the method proposed in this article, a hardware experimental platform was constructed. First, a four-switch buck-boost (FSBB) converter is used to perform energy equalization of a six-cell Li-ion battery pack under different working conditions, and the experimental results show that the Li-ion battery pack can be equalized by the active equalization circuit under the states of resting, charging, and discharging in about 60 min, 75 min, and 100 min, respectively. Then, the impedance spectroscopy in the frequency range of 0.2 Hz–800 Hz is achieved through the collaborative control of active equalization and impedance spectroscopy. Finally, in order to verify the reliability of the measured data, the measured impedance spectroscopy data were evaluated by the Kramers–Kronig (K-K) transforms, and the data measured at the initial stage were compared and analyzed with the data measured by a professional electrochemical workstation, which verified the validity of the method proposed in this article.

In recent years, the number of safety accidents in new-energy electric vehicles due to lithium-ion battery failures has been increasing, and the lithium-ion battery fault diagnosis technology is particularly important to ensure the safe operation of electric vehicles. This paper proposes a method for lithium-ion battery fault diagnosis based on the historical trajectory of lithium-ion battery remaining discharge capacity in medium and long time scales. The method first utilizes the sparrow search algorithm (SSA) to identify the parameters of the second-order equivalent circuit model of the lithium-ion battery, and then estimates the state of charge (SOC) of the lithium-ion battery using the extended Kalman filter (EKF). The remaining discharge capacity is estimated according to the SOC, and finally the feature vectors are used to diagnose the faults using box plots on the medium and long time scales. Experimental results verify that the root mean squared error (RSME) and mean absolute error (MAE) of the proposed SOC estimation method are 0.0049 and 0.0034, respectively. This method can accurately identify the faulty single cell in a battery pack with low-capacity single cells and promptly detect any abnormalities in the single cell when a micro-short circuit fault occurs.

This study focuses on the safety and reliability issues of lithium-ion batteries, proposing a fault diagnosis strategy that leverages dual-feature extraction from both the time and frequency domains. Additionally, by modifying the traditional autoencoder, the study proposes a feature-guided autoencoder as an unsupervised model for extracting features in the time domain. Initially, wavelet packet decomposition and its energy-denoising treatment are employed to refine fault information within battery voltage signals. Subsequently, the reconstruction error outputted by the Feature-Guided Autoencoder is utilized as the time-domain fault feature, while the cosine similarity of the energy of signals in various frequency bands obtained after wavelet packet decomposition serves as the frequency-domain fault feature. Ultimately, this article selects the Isolation Forest algorithm for two-dimensional outlier detection of time and frequency features. Experimental results demonstrate that the feature-guided autoencoder proposed in this study not only enhances the sensitivity of time-domain fault features compared to traditional autoencoders and their variants but also optimizes issues related to training time and computational load. The effectiveness of the proposed dual-feature fault diagnosis method in both the time and frequency domains is validated through data from two actual vehicles, showing superior early fault detection capability relative to single-feature fault diagnosis methods.

With the rapid development of new energy vehicles, a large number of lithium batteries have been produced, used, and then retired. The full utilization and safe use of the whole life cycle of the batteries have become a hot topic in the research field. Compared to brand-new batteries, retired power batteries exhibit significant inconsistency and safety risks, thus necessitating effective battery equalization and safety monitoring methods. In this article, an active equalization method for cascade utilization lithium battery pack with online measurement of electrochemical impedance spectroscopy is proposed to actively equalize the retired battery pack and alleviate the inconsistency of the battery pack. Besides, the electrochemical impedance spectrum of the single battery is measured online without adding additional hardware circuits so as to realize real-time safety monitoring and solve the safety problem of the battery. Finally, in order to verify the feasibility of the active equalization and electrochemical impedance spectrum monitoring scheme designed in this article, a simulation model is built based on the matlab-Simulink platform. The simulation results show that the six batteries in the proposed scheme model complete the active equalization in about 710 s, 850 s, and 740 s, respectively, in the balance mode, charge mode, and discharge mode, and the electrochemical impedance spectrum in the frequency range of 1–20 KHz can be successfully measured.