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The hybrid electric system has good potential for unmanned tracked vehiclesdue to its excellent power and economy. Due to unmanned tracked vehicles haveno traditional driving devices, and the driving cycle is uncertain, it bringsnew challenges to conventional energy management strategies. This paperproposes a novel energy management strategy for unmanned tracked vehicles basedon local speed planning. The contributions are threefold. Firstly, a localspeed planning algorithm is adopted for the input of driving cycle predictionto avoid the dependence of traditional vehicles on driver's operation.Secondly, a prediction model based on Convolutional Neural Networks and LongShort-Term Memory (CNN-LSTM) is proposed, which is used to process both theplanned and the historical velocity series to improve the prediction accuracy.Finally, based on the prediction results, the model predictive controlalgorithm is used to realize the real-time optimization of energy management.The validity of the method is verified by simulation using collected data fromactual field experiments of our unmanned tracked vehicle. Compared withmulti-step neural networks, the prediction model based on CNN-LSTM improves theprediction accuracy by 20%. Compared with the traditional regular energymanagement strategy, the energy management strategy based on model predictivecontrol reduces fuel consumption by 7%.

Energy Management Strategy for Unmanned Tracked Vehicles Based on Local Speed Planning