基于 Munchausen-PER 算法优化的混合动力履带车辆能量管理策略

To optimize the fuel economy of the series hybrid tracked vehicle and reduce the offline training time of neural network,an energy management strategy (EMS) based on double-deep Q_learning network (DDQN) algorithm with Munchausen gradient optimization and prioritized experience replay (Munchausen-PER-DDQN) is proposed. The required power is calculated by a vehicle model which involves the engine-generator set,the battery pack and drive motor,and then the peoposed strategy is used to optimally control the throttle opening of engine based on power demand. The Munchausen gradient optimization algorithm adds log-policy to the reward to ease the learning of sub-optimal actions,and the prioritized experience replay algorithm assigns higher selection possibility to certain experience for those who have more influence on the training of the algorithm, Tthe energy management strategy based on Munchausen-PER-DDQN algorithm shows a better performance of fuel economy and training time of neural network. The simulated result shows that, compared with TD3-PER algorithm, the Munchausen-PER-DDQN algorithm achieves 35. 3% improvement in neural network training time and 4. 6% improvement in the fuel economy.