For electric vehicles, accurate real-time estimation of the road friction coefficient is critical for maintaining stability, as the millisecond-level response of electric motors and the integration of regenerative braking demand higher perception fidelity than traditional internal combustion vehicles. This paper proposes a methodological framework for road friction estimation specifically designed for intelligent E-Chassis based on micro-signal features of intelligent tires and deep learning. An intelligent tire system, integrated with tri-axial accelerometers and strain gauges, was installed on the front-left wheel of a test vehicle to capture raw dynamic signals during transitions from cement to snow-covered surfaces across a velocity gradient of 10–50 km/h. The Savitzky–Golay convolutional smoothing algorithm was applied to reconstruct the high-frequency raw signals, enabling the extraction of a five-dimensional feature vector comprising vehicle velocity, peak strain, contact patch width, peak-to-peak acceleration, and signal standard deviation. The study revealed a natural filtering effect originating from the porous elastic properties of snow, resulting in a 60–70% reduction in signal standard deviation compared to cement, accompanied by a cliff-like feature collapse at the moment of snow entry. A BP neural network model with a 5-7-1 architecture achieved an identification accuracy of 96.2% on the test set, facilitating a rapid real-time prediction of the friction coefficient transitioning from 0.75 to 0.23. Unlike traditional methods, the proposed approach does not rely on high slip ratios and can complete identification within the first physical rotation cycle. This provides a robust physical criterion for the torque vectoring and regenerative braking stability of intelligent electric vehicles in extreme environments.

Sodium-ion batteries (SIBs) have attracted growing attention as an alternative to lithium-ion technologies for electric mobility and stationary energy-storage applications, owing to the wide availability of sodium resources, cost advantages, and comparatively favorable safety characteristics. Accurate state-of-health (SOH) estimation is essential for safe and reliable SIB deployment, yet existing data-driven methods still suffer from limited accuracy and interpretability, as well as a lack of dedicated aging datasets. This study proposes an explainable SOH estimation methodology based on a long short-term memory (LSTM) network combined with model-agnostic KernelSHAP analysis. Thirteen health indicators (HIs) are extracted from charge/discharge data and post-charge relaxation segments, and the most relevant indicators are selected via Pearson correlation screening as model inputs. Built on these HIs, an LSTM-based multi-step framework is developed to take HI sequences as input and forecast the SOH trajectory over the subsequent 20 cycles. Experimental results show that the proposed method achieves high accuracy and robust cross-cell generalization, with mean absolute error (MAE) below 1.0%, root-mean-square error (RMSE) below 1.2% across all cells, and an average RMSE of about 0.75% in the main cross-cell setting. KernelSHAP-based global and temporal analyses further clarify how different HIs and time positions influence SOH estimates, enhancing model transparency and physical interpretability.

To address the difficulty of balancing trajectory-tracking accuracy and yaw stability for distributed-drive four-axle heavy trucks under high-speed and large-curvature cornering conditions, this paper proposes a hierarchical cooperative control strategy. The upper layer employs Sliding Mode Control (SMC) to achieve precise trajectory tracking, while the lower layer integrates a sliding-mode-based Direct Yaw Moment Control (DYC) and a differential braking allocation strategy to enhance vehicle stability. TruckSim–Simulink co-simulation results demonstrate that, under large-curvature scenarios such as S-shaped paths, sharp lane changes, and single-lane transitions, the proposed strategy outperforms the conventional SMC method. Specifically, the maximum lateral deviation is reduced by 19.23–23.02%, the peak heading angle error decreases from 5.3° to 3.5°, the maximum yaw rate drops from 12.6°/s to 4.6°/s (a 63.49% reduction), and the peak sideslip angle at the vehicle’s center of mass converges from 4.6° to 3.8° (a 17.39% decrease). The results indicate that the proposed strategy achieves coordinated optimization of trajectory tracking and yaw stability under high-speed, large-curvature cornering conditions, providing both theoretical support and engineering value for high-dynamic control of distributed-drive heavy trucks.

The road surface adhesion coefficient is a key parameter affecting the dynamic control and driving safety of vehicles. Addressing the limitations of vehicle dynamics-based estimation methods under low-excitation conditions, this paper proposes a vision-perception-based CNN–Mamba hybrid regression network (CMHR-Net) for visual road friction potential estimation. By extracting road surface texture features from camera images, the proposed method predicts representative friction levels associated with different road conditions, providing prior information for vehicle active safety and control systems. First, ResNet18 is used to extract local texture features from road surface images. Second, a Mamba State Space Model is introduced to model long-distance dependencies between features, thereby enhancing global representation capabilities. Finally, the predicted adhesion coefficient value is output through a regression layer. Simultaneously, an adhesion coefficient mapping dataset based on road surface semantic attributes is constructed for model training and validation. Experimental results show that under various road surface conditions, including wet asphalt, waterlogged asphalt, waterlogged concrete, ice and snow, and joints, the proposed method significantly reduces the MAE (Mean Absolute Error) compared to the traditional CNN model. Specifically, under low adhesion conditions (μ ≈ 0.20), the error is reduced by approximately 56.9%. Furthermore, in complex variable conditions, the model significantly outperforms the traditional CNN model in both maximum error (Max Error) and mean absolute percentage error (MAPE). For example, under low adhesion conditions (μ ≈ 0.20), the MAPE decreases from 21.59% to 9.28%, and the maximum error decreases from 0.2025 to 0.0628, demonstrating superior stability and robustness. This method can achieve high-precision feedforward estimation of the adhesion coefficient without relying on vehicle dynamics excitation, providing effective support for feedforward control and active safety systems in vehicles.

With the expansion of electric vehicle (EV) applications into unstructured sandy terrains such as deserts, accurately characterizing tire–sand dynamic interactions is essential for enhancing off-road performance. However, traditional terramechanics models, the discrete element method (DEM), and purely data-driven neural networks all have inherent limitations, failing to balance physical interpretability and computational efficiency. This study proposes a wheel–sand dynamics prediction model that integrates DEM simulation, semi-physical modeling, and deep learning. A DEM tire–sand contact platform is built to acquire longitudinal slip and cornering properties, and a dimensionless semi-physical tire model is derived using frictional constitutive relations and tire theory. A 3-DOF vehicle dynamics model is then established to generate high-fidelity physics-based datasets, and a residual neural network is adopted to avoid performance degradation in deep networks. The model is validated and optimized via real-vehicle sandy terrain tests, with its performance compared against other network structures. The proposed model achieves high prediction accuracy, with engineering-acceptable errors, and outperforms conventional neural networks. The dimensionless framework improves generality, overcoming the weaknesses of traditional and purely data-driven models. This work provides theoretical and statistical support for EV traction control design and tire structure optimization, promoting driving stability and terrain passability in unstructured sandy environments.