基于轨迹规划与 CNN-LSTM 预测的履带式混合动力无人平台能量管理优化

As one of the key technologies of hybrid electric vehicles, energy management is critical to the entire efficiency and fuel economy. As the driving cycle of unmanned tracked vehicles is uncertain, conventional energy management strategies must deal with new challenges. To improve the prediction accuracy, a prediction model based on Convolutional Neural Networks and Long Short-Term Memory (CNN-LSTM) is proposed for processing both planned and historical velocity series. An optimal forward dynamic programming algorithm is proposed to solve the optimal control problem of energy management. Based on the prediction results, a model predictive control algorithm is adopted to realize real-time optimization of energy management. The effectiveness of the method is proved by using collected data from actual field experiments of unmanned tracked vehicles. Compared with multi-step neural networks, the prediction model based on CNN-LSTM improves prediction accuracy by 3%. The energy management strategy based on model predictive control reduces fuel consumption by 3.9% compared to the traditional regular energy management strategy.