基于BO-TD3混合动力丘陵山地移动作业底盘能源控制策略OA

To optimize the fuel economy and battery SOC maintenance performance of hybrid power mobile operating plat-forms in hilly terrain during plowing conditions,and to address the uncertainty in hyperparameter selection in reinforce-ment learning-based energy management strategies,a control strategy based on Bayesian optimization of the Twin Delayed Deep Deterministic Policy Gradient(BO-TD3)algorithm was proposed.Firstly,the Twin Delayed Deep Deterministic Policy Gradient(TD3)algorithm was used as the core,and Bayesian optimization(BO)was employed to search for the optimal hyperparameters,such as the learning rate and discount factor.The optimized parameters were then applied to the TD3 algorithm,where the plowing condition data was trained to achieve fuel consumption minimization and battery utilization optimization through the cooperative control of the engine and the motor.Simulation results demonstrated that the proposed BO-TD3 strategy significantly outperformed the conventional TD3 and Deep Deterministic Policy Gradient(DDPG)algorithms in terms of fuel economy,with improvements of 2.08%and 10.37%,respectively,thereby enhan-cing the comprehensive energy management efficiency of the vehicle.Further validation of the real-time online control strategy under other operating conditions showed that,compared with TD3,DDPG,and the rule-based Charge-Depleting and Charge-Sustaining(CDCS)strategy,the BO-TD3 strategy achieved 4.75%,7.13%,and 28.71%im-provements in fuel economy,respectively,while maintaining excellent battery SOC performance.This study can provide a novel approach to energy management strategies for hybrid power mobile operating platforms and other agricultural ma-chinery,offering significant application value and broad promotion prospects.