In the present research, the energy flow of electric vehicle batteries under different environmental temperatures was experimentally examined in a climate chamber. The energy flow characteristics, energy loss conditions, and the critical components’ operating conditions and working efficiency under different environmental temperatures were comparatively analyzed. The test results show that the environmental temperature has a profound impact on an electric vehicle’s performance and the critical components’ working conditions. The driving mileage of the tested vehicle at −7 °C, 23 °C, and 35 °C was found to be 162.89 km, 256.09 km, and 198.69 km, respectively. The environmental temperature does not have much effect on the loss of the motor and motor control unit under driving conditions, and the proportion of those at different temperatures is in all cases about 18%. The battery-recycled energy at 23 °C under braking conditions is much higher than that at −7 °C and 35 °C, leading to a longer driving range. The power battery pack thermal transfer loss at −7 °C is much greater than that at 23 °C and 35 °C due to the low charging and discharging efficiency and the high energy consumption required to warm up the battery at a low environmental temperature. The compressor energy consumption accounts for a large proportion in both braking and driving conditions at 35 °C, and the proportions are 15.25% and 12.41%, respectively. The battery state-of-charge drops the fastest at −7 °C, followed by 35 °C, due to the differences in the power demands of air conditioning, warm air positive temperature coefficient (PTC), and battery PTC in high- and low-temperature environments. The working condition of the front motor under driving conditions at 35 °C is the most severe and leads to the lowest working efficiency.

This study addresses the critical issue of high casualty rates in frontal collisions by proposing structural optimization methods for the energy-absorbing box of lightweight electric vehicles. A small pure electric car was selected as the research object. A finite element model for frontal collision was established in HyperMesh and solved using the LS-DYNA explicit dynamics solver. The parameters such as the acceleration of the B-pillar of the vehicle, the compression distance of the energy absorption box and the energy absorption are analyzed in this study. Energy absorption was used as the primary crashworthiness indicator while ensuring that the peak collision force, compression distance of the energy-absorbing box, and acceleration of the B-pillar complied with safety standards. Results demonstrate that Scheme 2 (featuring reduced wall thickness and a single induced groove) outperformed other designs, increasing energy absorption by 3% and reducing mass by 17% compared to the baseline model. This conclusion can provide a reference basis for the subsequent vehicle collision analysis.

In this study, the sensitivity of operating parameters such as the hydrogen and air excess coefficient, cathode inlet pressure, intake relative humidity, and coolant inlet temperature and their effects on the performance of single proton exchange membrane fuel cells (PEMFCs) are experimentally assessed. The results revealed that the fuel cell node voltage increases as the hydrogen and air excess coefficient increases, and the impact of the hydrogen and air excess coefficient on the fuel cell node voltage gradually increases as the current density increases. However, a higher hydrogen and air excess coefficient is not always better. The node voltage increases as the intake pressure increases. However, it is not that a higher intake pressure is always better, but rather that there is an optimal intake pressure value to achieve the best overall performance of the fuel cell. The node voltage increases as the coolant inlet temperature increases at most fuel cell current densities. However, the optimum fuel cell operating inlet temperature is not necessarily higher, as the coolant inlet temperature may have a strong coupling relationship with other operating conditions that will also affect the fuel cell performance. The fuel cell operating inlet temperature may have a strong coupling relationship with the intake relative humidity, and both of these parameters must be well-matched to achieve better fuel cell performance.

This paper addresses the lateral path tracking problem for autonomous vehicles operating over a wide speed range by proposing a speed-adaptive horizon model predictive control (SAH-MPC) strategy. Unlike conventional fixed-horizon MPC, the proposed approach schedules both the prediction horizon ${\mathrm{N}}_{\mathrm{p}}$ and the control horizon ${\mathrm{N}}_{\mathrm{c}}$ as explicit functions of the longitudinal speed. This design maintains high tracking accuracy at low speeds while enhancing stability and suppressing oscillations at high speeds. The controller is formulated using a bicycle-model representation of the lateral-yaw dynamics, with online linearization, forward-Euler discretization, and a quadratic programming solver enforcing hard constraints on steering angle and its increment, as well as soft stability constraints on sideslip angle, lateral acceleration, and tire slip angles. A co-simulation framework is established in MATLAB/Simulink and CarSim, and the algorithm is evaluated under double lane-change and slalom maneuvers at speeds from 36 to 90 km/h, with a fixed-horizon MPC (${\mathrm{N}}_{\mathrm{p}}=20$, ${\mathrm{N}}_{\mathrm{c}}=5$) as the baseline. Quantitative comparisons show that at 90 km/h, SAH-MPC reduces the peak lateral error by 33% (from 0.42 m to 0.28 m) and lowers the peak yaw-rate oscillation by 18%. Moreover, it helps maintain the sideslip angle within the prescribed ±4° safety bound, whereas the baseline exceeds this limit with a peak of 5.2°. These results demonstrate that SAH-MPC effectively improves the tracking-stability trade-off across a wide speed range without altering the core MPC structure, offering a simple yet practical enhancement for speed-varying autonomous driving.