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.

Selective Catalytic Reduction (SCR) technology is currently used to effectively reduce NO_x emissions for diesel engines. The present study aims at building a three-dimensional numerical model to evaluate the NO_x conversion efficiency and the NH₃ slip in an SCR system, reducing the time and resources necessary for design and development process. A numerical model of an SCR system has been built to integrate species transport, heat transfer and flow characteristics along with kinetics of the chemical reactions. In order to systematically simulate situations where the concentration of NH₃ at the inlet is not uniform, different NH₃ inlet profiles have been built by using a Multivariate Gaussian Distribution and considering the maximum concentration in different locations. The effect of different geometries and NH₃ distribution profiles on the NO_x conversion efficiency and NH₃ slip has been studied. The behaviour of the system at different inlet temperatures has been explored and the reaction rates in the monolith have been analysed. The study has been extended by studying the effect of different NH₃/NO ratios to provide a more complete comparison between different designs. The numerical model has been found useful to take into account many aftertreatment system parameters during the design of an SCR system, maximize the NO_x conversion efficiency by modifying the NH₃/NO ratio while minimizing the NH₃ slip, providing a comprehensive tool for the optimization of geometrical characteristics of an SCR system.

Considering the rules of Formula Student Racing regarding functional and safety requirements of an electric vehicle’s power system, a reliability control model of accelerator and brake pedal is developed in the Matlab/Simulink platform. Voltage signals of the accelerator pedal are filtered. An amplitude limit module and consistency module check the filtered voltage signals of the accelerator pedal. The voltage signal of the brake pedal is processed to achieve the accurate position of the accelerator pedal and avoid the influence of incorrect signals and mis-operation on safety. The control model need not change the structure of the pedal and increase the sensors. Simulation and experimental results verified that the model can effectively avoid influences of pedal vibration on the voltage signal, identify the faulty signals and cut power output when the accelerator and brake pedal are depressed simultaneously.