The driving range of electric vehicles (EVs) is still an important factor restricting their development. Although the rising battery energy density has reached a bottleneck, which is a key constraint, the drive energy management strategy also has a significant effect and can improve the driving range of EVs, since wheel traction torque control can directly optimize the driving energy consumption of EVs. In order to comprehensively analyze the current research status of driving energy management and clarify its development direction, this review focuses on the driving energy management strategy of EVs and systematically summarizes the configurations and power distribution strategies of the dual-motor coupling drive system (DCDS), as well as torque vectoring control strategies of the decentralized drive system. Firstly, driving energy losses are analyzed in detail, which mainly include electric loss, tire slip energy dissipation, and the power of cornering resistance. Secondly, typical configurations of the DCDS are introduced, and the power distribution strategies of the DCDS are comprehensively reviewed. Finally, as an interesting energy-saving technology, energy-saving torque vectoring, generally applied to decentralized drive systems, is reviewed in detail in terms of its energy-saving pathways and control strategies, which are classified as front-and-rear torque vectoring and left-and-right torque vectoring. Research findings indicate that the driving range of EVs can be effectively increased by applying a driving energy management strategy based on several novel multi-power source drive systems. The development of a driving energy management strategy and the required novel drive systems will be a valuable and crucial direction for further energy conservation in EVs.

Identifying motor imagery (MI) electroencephalogram (EEG) is an important way to achieve brain–computer interface (BCI), but its applicability is heavily dependent on the performance of feature extraction procedure. In this paper, a feature extraction method based on generalized maximum fuzzy membership difference entropy (GMFMDE) and discrete wavelet transform (DWT) was proposed for the feature extraction of EEG signals. The influence of different distance calculation methods, embedding dimensions and tolerances were studied to find the best configuration of GMFMDE for the feature extraction of MI–EEG. The gradient boosting decision tree (GBDT) classifier was used to classify the features extracted from GMFMDE and DWT. The average classification accuracy of 93.71% and the maximum classification accuracy of 96.96% were obtained, which proved the effectiveness of the proposed feature extraction method for EEG signal feature extraction.

Power distribution and battery thermal management are important technologies for improving the energy efficiency of plug-in hybrid electric vehicles (PHEVs). In response to the global optimization of integrated energy thermal management strategy (IETMS) for PHEVs, a dynamic programming algorithm based on adaptive grid optimization (AGO–DP) is proposed in this paper to improve optimization performance by reducing the optimization range of SOC and battery temperature, and adaptively adjusting the grid distribution of state variables according to the actual feasible region. The simulation results indicate that through AGO–DP optimization, the reduction ratio of the state feasible region is more than 30% under different driving conditions. Meanwhile, the algorithm can obtain better global optimal driving costs more rapidly and accurately than traditional dynamic programming algorithms (DP). The computation time is reduced by 33.29–84.67%, and the accuracy of the global optimal solution is improved by 0.94–16.85% compared to DP. The optimal control of the engine and air conditioning system is also more efficient and reasonable. Furthermore, AGO–DP is applied to explore IETMS energy-saving potential for PHEVs. It is found that the IETMS energy-saving potential range is 3.68–23.74% under various driving conditions, which increases the energy-saving potential by 0.55–3.26% compared to just doing the energy management.

With the rapid development of renewable energy and the widespread adoption of electric vehicles, thermal runaway (TR) in batteries has become a critical safety concern. Despite various protective technologies, TR remains frequent due to challenges in metal dendrite growth, material stability, and efficient thermal management. Inspired by natural structures and functions, biological principles have been abstracted to guide novel biomimetic approaches. This review focuses on TR mechanisms, summarizes the corresponding biomimetic principles, and discusses their applications in functional design strategies to enhance battery safety and stability. First, the review outlines current TR protection designs from both intrinsic and system safety perspectives, analyzing biomimetic strategies to enhance intrinsic safety by improving the thermal stability of battery components to reduce the risk of TR. Next, it explores the application of biomimetic designs in thermal management and protection mechanisms, including innovations in thermal management and structure. Finally, this review consolidates the findings from the preceding sections on biomimetic designs for TR protection, emphasizing current challenges and potential future directions, to offer new technical insights and guidance for research on thermal safety in batteries.

The NVH (Noise, Vibration, and Harshness) characteristics of micro-motors used in vehicles directly affect the comfort of drivers and passengers. However, various factors influence the motor’s structural parameters, leading to uncertainties in its NVH performance. To improve the motor’s NVH characteristics, we propose a method for optimizing the structural parameters of automotive micro-motors under uncertain conditions. This method uses the motor’s maximum magnetic flux as a constraint and aims to reduce vibration at the commutation frequency. Firstly, we introduce the Pareto ellipsoid parameter method, which converts the uncertainty problem into a deterministic one, enabling the use of traditional optimization methods. To increase efficiency and reduce computational cost, we employed a data-driven method that uses the one-dimensional Inception module as the foundational model, replacing both numerical models and physical experiments. Simultaneously, the module’s underlying architecture was improved, increasing the surrogate model’s accuracy. Additionally, we propose an improved NSGA-III (Non-dominated Sorting Genetic Algorithm III) method that utilizes adaptive reference point updating, dividing the optimization process into exploration and refinement phases based on population matching error. Comparative experiments with traditional models demonstrate that this method enhances the overall quality of the solution set, effectively addresses parameter uncertainties in practical engineering scenarios, and significantly improves the vibration characteristics of the motor.

Magnetic fields generated by electromagnetic radiation sources in electric vehicle cabins may affect passenger exposure and the reliable operation of onboard electronics. Physical measurement campaigns are time-consuming and spatially limited, motivating rapid field-prediction methods. Based on measured frequency-domain data, this study proposes the Multi-band Equivalent-Source Attribution Network (MESA-Net) that uses the S01 (Static background) spectrum to anchor a spatial background branch and learns condition- and frequency-dependent effective source strengths for powered and dynamic operating states. This static-background-conditioned decomposition is introduced because each cabin probe records an aggregate amplitude-only signal rather than an isolated source spectrum. Five predefined cuboid equivalent-source regions are coupled with normalized distance kernels and an optional diagnostic residual through incoherent power-domain superposition. The dataset comprises 12 passenger measurement points, two near-source auxiliary points, five operating conditions, and four independently acquired bands spanning 1 Hz–400 kHz. Under leakage-free nested spatial holdout, the model achieved a mean absolute amplitude error of 3.528 dB. Learning band-dependent attenuation improved balanced multi-band performance relative to fixed attenuation. MESA-Net therefore links broadband signal prediction with model-constrained candidate-source attribution, rather than unique physical source-power recovery, and provides an engineering reference for rapid cabin-field assessment and diagnostic prioritization.