Abstract:

To address the insufficient energy management efficiency of composite energy storage hybrid systems in engineering vehicles under complex operating conditions, a global optimization strategy based on dynamic programming (DP) was proposed. This strategy constructed an optimal power allocation control model using load demand torque, the state of charge (SOC) of hydraulic accumulator, and battery SOC as state variables, and solved the control sequence through inverse recursive-forward optimization. The feasibility of applying the DP strategy in practical engineering was further validated through hardware-in-the-loop testing. The results show that compared to rule-based (RB) and adaptive neural fuzzy inference system (ANFIS) strategies, the DP strategy increases the proportion of engine operation within the high-efficiency zone by 38.28% and 30.27%, respectively. It reduces the comprehensive fuel consumption by 15.17% and 11.23%, and the battery SOC fluctuation by 34.02% and 23.97%, respectively. The average SOC of the brake energy recovery accumulator are increased by 29.46% and 23.51%, and the average battery SOC are improved by 11.57% and 8.62%, respectively. These results demonstrate that the DP strategy effectively enhances engine efficiency, maintains stable operation within the high-efficiency zone, and achieves overall system energy optimization. The DP strategy provides both theoretical justification and practical solutions for energy-saving control in construction machinery.

Key words: engineering vehicles, composite energy storage hybrid power system, dynamic programming (DP), energy management, hardware-in-the-loop (HIL) testing