Fuel cell hybrid electric vehicles have attracted a large amount of attention in recent years owing to their advantages of zero emissions, high efficiency and low noise. To improve the fuel economy and system durability of vehicles, this paper proposes an energy management strategy optimization method for fuel cell hybrid electric vehicles based on dynamic programming. Rule-based and dynamic-programming-based strategies are developed based on building a fuel cell/battery hybrid system model. The rule-based strategy is improved with a power distribution scheme of dynamic programming strategy to improve the fuel economy of the vehicle. Furthermore, a limit on the rate of change of the output power of the fuel cell system is added to the rule-based strategy to avoid large load changes to improve the durability of the fuel cell. The simulation results show that the equivalent 100 km hydrogen consumption of the strategy based on the dynamic programming optimization rules is reduced by 6.46% compared with that before the improvement, and by limiting the rate of change of the output power of the fuel cell system, the times of large load changes are reduced. Therefore, the strategy based on the dynamic programming optimization rules effectively improves the fuel economy and system durability of vehicles.

The accurate estimation of the state of charge (SOC) and state of health (SOH) is of great significance to energy management and safety in electric vehicles. To achieve a good trade-off between real-time capability and estimation accuracy, a collaborative estimation algorithm for SOC and SOH is presented based on the Thevenin equivalent circuit model, which combines the recursive least squares method with a forgetting factor and the extended Kalman filter. First, the parameter identification accuracy is studied under a dynamic stress test (DST) and the federal urban driving schedule (FUDS) test at different ambient temperatures (0 °C, 25 °C, and 45 °C). Secondly, the FUDS test is used to verify the SOC estimation accuracy. Thirdly, two batteries with different aging degrees are used to validate the proposed SOH estimation algorithm. Subsequently, the accuracy of the SOC estimation algorithm is studied, considering the influence of updating the SOH. The proposed SOC estimation algorithm can achieve good performance at different ambient temperatures (0 °C, 25 °C, and 45 °C), with a maximum error of less than 2.3%. The maximum error for the SOH is less than 4.3% for two aged batteries at 25 °C, and it can be reduced to 1.4% after optimization. Furthermore, calibrating the capacity as the SOH changes can effectively improve the SOC estimation accuracy over the whole battery life.

Lane changing present a significant challenge for autonomous vehicles, as they must maintain safe driving and optimize time efficiency. This process is strongly affected by traffic environment and driver characteristics. This paper proposed a lane changing control method based on Stackelberg game theory, integrating lane changing decision and trajectory planning while comprehensively considering the driver’s characteristics and the traffic environment. Firstly, considering the common characteristics of lane changing decision and trajectory planning, the two stages are integrated using the leader-follower game theory, enhancing the accuracy of lane changing decisions. Secondly, the cooperative game theory model is employed to design an adaptive weight adjustment strategy for the trajectory tracking controller. The weight coefficients for vehicle stability and path tracking accuracy are dynamically adjusted within the model predictive control method to adapt to the vehicle’s stability state. Simulation results indicate a 24% improvement in decision-making accuracy with the proposed leader-follower game decision method over the rule-based lane changing model. The average relative error in lateral displacement, comparing the vehicle’s actual trajectory to the planned one, is reduced by 6%. Additionally, the variable-weight trajectory tracking control enhances overall tracking performance by over 30% in scenarios involving high speeds and low adhesion. These findings verify the proposed vehicle lane changing method notably improves lane changing safety, stability, and precision.

For multi-axle distributed drive (MADD) vehicles, the complexity of the longitudinal dynamics control system increases with the number of driven wheels, which presents a huge challenge to control the multi-motor drive vehicle with more than four wheels. To reduce the control system complexity, this paper proposes a coordinated slip control algorithm using the hierarchical linear quadratic regulator (HLQR) scheme for a 12 × 12 MADD vehicle. The 12-wheel driving system is decoupled based on the wheel load and simplified to a double local subsystem. First, the 12 × 12 MADD vehicle dynamics model is established. Then, the optimal slip ratio is obtained on the basis of the road friction coefficient estimation through a fuzzy control algorithm when the wheel slips. Afterwards, the wheel slip ratio is controlled based on the HLQR program for anti-slip regulation. Furthermore, the driving torque control allocation based on quadratic programming (QR) is coordinated with the anti-slip control. Simulink results show that the proposed coordinated slip control based on HLQR can improve slip control accuracy by more than 30% and greatly reduce the calculation load. The torque control allocation is also limited by the slip control results to ensure wheel dynamic stability and smoothly satisfy the driver’s demand.