Aiming to address the complexity and challenges of predicting pure electric vehicle (EV) sales, this paper integrates a time series model, support vector machine and combined model to forecast EV sales in China. Firstly, a seasonal autoregressive integrated moving average (SARIMA) model was constructed using historical EV sales data, and the model was trained on sales statistics to obtain forecasting results. Secondly, variables that were highly correlated with sales were analyzed using gray relational analysis (GRA) and utilized as input parameters for the support vector regression (SVR) model, which was constructed to optimize sales predictions for EVs. Finally, a combined model incorporating different algorithms was verified against market sales data to explore the optimal sales prediction approach. The results indicate that the SARIMA-GRA-SVR model with the squared prediction error and inverse method achieved the best predictive performance, with MAPE, MAE and RMSE values of 12%, 1.45 and 2.08, respectively. This empirical study validates the effectiveness and superiority of the SARIMA-GRA-SVR model in forecasting EV sales.

To address the critical challenge of high energy consumption in single-source electric vehicles, this study proposes a hybrid energy storage system (HESS)-integrated energy management strategy (EMS). Firstly, the car-following and HESS models are constructed. Secondly, a multi-objective optimization framework balancing adaptive cruise control (ACC) optimal tracking quality and energy economy is developed, where the fast, non-dominated sorting genetic algorithm (NSGA-II) resolves dynamic power demands. Thirdly, the third-order Haar wavelet enables online rolling decomposition of power profiles. The high-frequency transient power is matched by a supercapacitor, while the low-frequency steady-state power is utilized as an input variable to the optimization controller. Then, a fuzzy logic controller dynamically optimizes HESS’s energy distribution based on state-of-charge (SOC) and load conditions. Finally, the cruise simulation model has been constructed utilizing the MATLAB/Simulink platform. Comparative analysis under the Urban Dynamometer Driving Schedule (UDDS) demonstrates a 3.71% reduction in the total power demand of the ego vehicle compared to the front vehicle. Compared to single-source configurations, the HESS ensures smoother SOC dynamics in lithium-ion batteries. After employing the third-order Haar wavelet for online rolling decomposition of the demand power, the high-frequency transient power matched by the lithium-ion battery is substantially reduced. Comparative analysis of three control strategies demonstrates that the wavelet-fuzzy logic approach exhibits superior comprehensive performance. Consequently, the proposed strategy effectively mitigates high-frequency transient peak charge/discharge currents in the lithium-ion battery and the energy consumption of the entire vehicle. This study provides a novel solution for energy storage systems in hybrid energy storage electric vehicles (HESEV) under ACC scenarios.

In response to the challenges posed by the low energy utilization of single-power pure electric vehicles and the limited lifespan of power batteries, this study focuses on the development of a compound power system. This study constructs a composite power system, analyzes the coupling characteristics of multiple systems, and investigates the energy management and optimal control mechanisms. Firstly, a power transmission scheme is designed for a hybrid electric vehicle. Then, a multi-state model is established to assess the electric vehicle’s performance under complex working conditions and explore how these conditions impact system coupling. Next, load power is redistributed using the Haar wavelet theory. The super capacitor is employed to stabilize chaotic and transient components in the required power, with low-frequency components serving as input variables for the controller. Further, power distribution is determined through the application of fuzzy logic theory. Input parameters include the system’s power requirements, power battery status, and super capacitor state of charge. The result is the output of a composite power supply distribution factor. To fully exploit the composite power supply’s potential and optimize the overall system performance, a global optimization control strategy using the dynamic programming algorithm is explored. The optimization objective is to minimize power loss within the composite power system, and the optimal control is calculated through interpolation using the interp function. Finally, a comparative simulation experiment is conducted under UDDS cycle conditions. The results show that the composite power system improved the battery discharge efficiency and reduced the number of discharge cycles and discharge current of the power battery. Under the cyclic working condition of 1369 s, the state of charge of the power battery in the hybrid power system decreases from 0.9 to 0.69, representing a 12.5% increase compared to the single power system. The peak current of the power battery in the hybrid power system decreases by approximately 20 A compared with that in the single power system. Based on dynamic programming optimization, the state of charge of the power battery decreases from 0.9 to 0.724. Compared with that of the single power system, the power consumption of the proposed system increases by 25%, that of the hybrid power fuzzy control system increases by 14.2%, and that of the vehicle decreases by 14.7% after dynamic programming optimization. The multimode energy shunt relationship is solved through efficient and reasonable energy management and optimization strategies. The performance and advantages of the composite energy storage system are fully utilized. This approach provides a new idea for the energy storage scheme of new energy vehicles.

In low-temperature environments, both the electrochemical and thermodynamic performance of lithium-ion batteries are significantly affected, leading to a substantial decline in overall performance. This deterioration is primarily manifested in the inability of the battery to release its actual capacity effectively, a marked reduction in charge-discharge efficiency, and accelerated capacity degradation, which directly undermines its power output capability under low-temperature conditions. Such performance degradation severely restricts the application of lithium-ion batteries in scenarios requiring high power and extended range, such as EVs. This paper proposes an intelligent low-temperature DC discharge heating optimization strategy based on the PSO algorithm. The strategy aims to simultaneously optimize heating time and minimize capacity loss by employing the PSO algorithm to dynamically optimize discharge currents under varying ambient temperatures. This approach achieves simultaneous optimization of battery heating efficiency and capacity loss. The proposed strategy effectively overcomes the limitation of traditional constant-current discharge methods, which struggle to dynamically adjust current intensity based on real operating conditions. By balancing heating efficiency and capacity degradation, the strategy significantly enhances energy utilization. The performance improvements achieved include a reduction in heating time by 48.71 seconds and an increase in heating rate by more than twofold. During 1,000 cycles of heating, the capacity loss was reduced by 0.10 Ah, effectively extending the battery's service life. This strategy addresses the limitations of traditional heating methods, providing a novel solution for the efficient application of lithium-ion batteries in low-temperature environments.

The objective of this paper is to examine the issues of low accuracy and poor stability in vehicle trajectory tracking under a transverse controller, with a view to investigating the transverse and longitudinal control of vehicle trajectory tracking. Firstly, an MPC transverse controller based on a single-point pre-scan model is constructed. Secondly, a longitudinal controller with PID as the upper layer and vehicle dynamics as the lower layer is constructed. Once more, GA is applied to optimise the parameters of the MPC controller globally offline. Finally, a classical double-shift line trajectory tracking simulation model has been constructed using MATLAB and Carsim software. The results demonstrated that the average deviation of transverse displacement was reduced by 16.76%, and the average transverse angular velocity was reduced by 6.79%, under MPC+PID controllers relative to MPC control. Following the implementation of GA optimization, significant enhancements have been made to the performance of the MPC+PID controllers.

In low-temperature environments, both the electrochemical and thermodynamic performances of lithium-ion batteries are significantly affected, leading to a substantial decline in overall performance. This deterioration is primarily manifested in the inability of the battery to release its actual capacity effectively, a marked reduction in charge–discharge efficiency, and accelerated capacity degradation, directly undermining its power output capability under low-temperature conditions. This performance degradation severely restricts the application of lithium-ion batteries in scenarios requiring high power and extended range, such as EVs. This paper proposes an intelligent low-temperature DC discharge heating optimization strategy based on the PSO algorithm. The strategy aims to simultaneously optimize heating time and minimize capacity loss by employing the PSO algorithm to dynamically optimize discharge currents under varying ambient temperatures. This approach achieves the simultaneous optimization of battery heating efficiency and capacity loss. It effectively overcomes the limitation of traditional constant-current discharge methods, which struggle to dynamically adjust current intensity based on real operating conditions. By balancing heating efficiency and capacity degradation, the model significantly enhances energy utilization. Taking the weighting factor λ = 0.5 as an example, the battery is heated from −30 °C to 0 °C at a 90% initial SOC. Compared to preheating methods that directly use the minimum optimized dynamic current threshold, it reduces heating time by 48.71 s and increases the heating rate by more than twofold. In contrast to preheating methods using the maximum optimized dynamic current threshold, it decreases capacity degradation by 0.10 Ah after 1000 heating cycles. This strategy addresses the limitations of traditional heating methods, providing a novel solution for the efficient application of lithium-ion batteries in low-temperature environments.