In order to improve the tracking adaptability of autonomous vehicles under different vehicle speeds and road curvature, this paper develops a weight adaptive model prediction control system (AMPC) based on PSO-BP neural network, which consists of a dynamics-based model prediction controller (MPC) and an optimal weight adaptive regulator. Based on the application of MPC to achieve high-precision tracking control, the optimal weight under different operating conditions obtained by automated simulation is used to train the PSO-BP neural network offline to achieve online adjustment of MPC weight. The validation results of the Prescan-Carsim-Simulink joint simulation platform show that the adaptive control system has better tracking adaptation capability compared with the original classical MPC control. The control strategy was also verified on an autonomous vehicle test platform, and the test results showed that the adaptive control strategy improved tracking accuracy while meeting the vehicle’s requirements for real-time control and lateral stability.

The compound accumulator is an energy storage device composed of one large accumulator and one small accumulator. Compared with traditional single-accumulator hydraulic hybrid vehicles, hydraulic hybrid vehicles based on compound accumulator can switch the working timing of large and small accumulators according to the characteristics of different working conditions, more braking energy can be recovered on the premise of providing enough braking strength. This article is aimed at hydraulic hybrid vehicles based on compound accumulator. First, the key components of the hydraulic system were matched, then according to the engine fuel consumption curve, the engine’s high-efficiency work area is divided, and formulated a rule-based energy management strategy based on the principle of adjusting the engine to work in a high-efficiency area. The internal pressure of the compound accumulator is selected as the state variable, the ratio of the torque of the hydraulic pump/motor to the required torque of the vehicle is the control variable, and the optimal fuel economy of the entire cycle is the objective function, and the mathematical model is established using the dynamic programming algorithm, and compiled a program in MATLAB to obtain the optimal torque distribution relationship between the engine and the hydraulic pump/motor and the optimal working timing of the large and small accumulators. Simulation results show that compared with the traditional hydraulic hybrid vehicle with single accumulator based on initial rules and the hydraulic hybrid vehicle with compound accumulator based on initial rules, the hybrid vehicle with compound accumulator based on improved rules can save fuel by 11.84[Formula: see text] and 5.96[Formula: see text] respectively. The fuel saving potential of the compound accumulator structure and the effectiveness of the improved rule control strategy based on dynamic programming algorithm are verified.