This paper reviews recent advancements in predicting the temperature of lithium-ion batteries in electric vehicles. As environmental and energy concerns grow, the development of new energy vehicles, particularly electric vehicles, has become a significant trend. Lithium-ion batteries, as the core component of electric vehicles, have their performance and safety significantly impacted by temperature. This paper begins by introducing the fundamental components and operating principles of lithium-ion batteries, followed by an analysis of how temperature affects battery performance and safety. Next, the methods for measuring and predicting battery temperature are categorized and discussed, including model-based methods, data-driven methods, and hybrid approaches that combine both. Finally, the paper summarizes the application of temperature prediction in a BMS and provides an outlook on future research directions.

Fast and accurate detections of state-of-health (SoH) are urgently required by various industrial sectors to facilitate reuse and recycling of Li-ion batteries. However, existing SoH identification methods rarely reconcile the monitoring speed and accuracy, which results in redundant carbon footprints and electronic wastes. To address this critical issue, an ultrafast and accurate SoH monitoring is proposed in this paper. The proposed variable forgetting factor Recursive Least Squares (VFFRLS) method is designed based on a least square method with forgetting factors to determine the electrical parameters of a simple electrical model. The established electrical model can be easily implemented using cost-effective micro-controller units (MCUs). The weighting factors of the objective function is automatically determined based on the Genetic Algorithm (GA). Experimental results validate the superior performance of the proposed strategy over conventional methods in detecting the SoH of one new and three aged batteries. For the new battery, the relative error of the estimated SoH is almost 0%. For the aged batteries, the relative error of the estimated SoH is less than 2.78%.

Accurate temperature estimation models for lithium (Li)‐ion batteries are critical for timely identification of and response to thermal runaway effects to ensure battery safety. In this paper, a hybrid data‐driven approach incorporating thermoelectric equivalent model (TEM) is proposed to predict the temperature of Li‐ion batteries under different state of health (SOH) based on measured data. The proposed TEM model consists of an electrical equivalent circuit model (EECM) and a thermal equivalent circuit modeling (TECM). The electrical model is a second‐order RC equivalent circuit model, and the thermal model is a first‐order thermal model, which interacts with parameters such as state of charge (SOC) and internal resistance to improve the accuracy of the model. In order to solve the problem that the model part is susceptible to measurement errors, a data‐driven model using Kalman filter (KF) combined bidirectional gated recursive unit (BiGRU) and Transformer is proposed to ensure high accuracy in predicting the temperature. The output of the TEM is used as the input to the data‐driven part to obtain the implied relationship between the temperature and parameters. The experimental results confirm the high accuracy of the hybrid model in estimating the battery temperature. The maximum temperature prediction error of the Li‐ion battery was 0.3423°C with a predicted root mean square error (RMSE) of 0.1266 under different SOH conditions.

Permanent magnet synchronous motors (PMSMs) have been widely used in new-energy vehicles and industrial servo systems. However, demagnetization faults (DMFs) can lead to severe issues, including torque ripple and magnetic field distortion. This paper proposes an intelligent diagnostic approach for DMFs based on stator tooth flux (STF). A mathematical model of STF is formulated, and the magnetic flux change is measured using multiple sets of anti-series-connected detection coils (DCs). By combining finite element simulation with signal processing technology, we establish a comprehensive diagnostic system covering fault feature extraction, fault location identification, and severity assessment is established. The proposed method employs wavelet transform (WT) to extract time-frequency features of voltage signals and combines it with a convolutional neural network (CNN) to form the WT-CNN intelligent diagnosis model. Based on the extracted voltage signal features, the method achieves intelligent identification and visual localization of DMFs. Simulation results show that the proposed method achieves an accuracy above 80% for fault location identification (defined as sample-level multi-label classification accuracy across 12 PMs) and above 85% for demagnetization severity estimation (defined as classification accuracy across 9 severity degrees from 10% to 90%). These results provide an effective technical foundation for motor condition monitoring and fault early warning in simulation environments.