Lithium-ion power battery pack life, capacity and safety depend primarily on consistency between battery cells. However, inconsistencies between battery cells are inevitable due to the inherent variability in production processes and operational environments. In parallel circuits, battery management systems can usually only monitor the total module current and terminal voltage, which results in limitations that lead to inter-unit inconsistency, reducing overall safety and energy efficiency. The conventional method of battery sorting involves analyzing static parameters such as capacity, internal resistance and voltage to ensure static consistency between cells. Nonetheless, cell-to-cell variations are more pronounced during dynamic and complex operations. The direct integration of static and dynamic features may result in data scale discrepancies and redundant information. Thus, the present study proposes a two-stage multi-parameter clustering method based on static and dynamic features. Initially, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) was applied to sort abnormal batteries and identify the number of subsequent clusters, using discharge capacity, internal resistance and open-circuit voltage (OCV) as inputs. Then, a Principal Component Analysis (PCA) was used to downscale and extract features from the discharge voltage profile. The principal component data were used as inputs to the Self-Organizing Map (SOM) clustering algorithm, which uses its self-organized and unsupervised learning characteristics to mine more dynamic time-series features and complete the final clustering and sorting. Finally, the effectiveness of the two-stage sorting method in parallel circuits was verified by determining clustering evaluation indexes, as well as the cycle life and discharge curves of batteries reassembled in parallel.

This study investigates the temperature increase characteristics of lithium-ion batteries under various states of health (SOHs) and proposes an aging assessment method based on temperature increase. The analysis of experimental data reveals that temperature increase behavior varies across different locations on the battery, particularly at the center of its large surface area, as the battery ages. This study demonstrates that monitoring surface temperature increase can serve as an effective method for rapid SOH assessment and provides insights into performance degradation and electrode aging. Furthermore, this approach enables the preliminary diagnosis of battery health by tracking temperature changes, without requiring complex equipment, making it highly practical and suitable for widespread application. In addition, this study discusses the potential impact of lithium plating on temperature increase characteristics and its implications for battery safety.

Yaw stability is essential for vehicle lateral control and is strongly influenced by the nonlinear dynamics of tire-road interaction. Tire Lateral Stiffness (TLS), a key parameter in this process, varies with tire properties and road conditions. Accurate TLS estimation is crucial for autonomous driving safety, especially during aggressive maneuvers or on low-friction surfaces. This paper proposes a novel TLS identification framework using modified Gradient Descent Methods (GDM) inspired by deep learning. A theoretical link is established between Recursive Least Squares (RLS) and GDM, revealing that RLS is a special case of GDM with adaptive learning rates. Based on this, improved RLS variants are developed for real-time use. Simulations compare different GDM and RLS algorithms, showing effective TLS tracking under varying conditions. Adaptive methods like Adam, which adjust gradients and learning rates, achieve improved performance, with Relative Steady-State Error (RSSE) below 5% and t10 (response time to reach 10% error band) under 3 s. These results offer practical guidance for estimator selection in safety-critical autonomous vehicle systems.