  The hybrid electric system has good potential for unmanned tracked vehicles
due to its excellent power and economy. Due to unmanned tracked vehicles have
no traditional driving devices, and the driving cycle is uncertain, it brings
new challenges to conventional energy management strategies. This paper
proposes a novel energy management strategy for unmanned tracked vehicles based
on local speed planning. The contributions are threefold. Firstly, a local
speed planning algorithm is adopted for the input of driving cycle prediction
to avoid the dependence of traditional vehicles on driver's operation.
Secondly, a prediction model based on Convolutional Neural Networks and Long
Short-Term Memory (CNN-LSTM) is proposed, which is used to process both the
planned and the historical velocity series to improve the prediction accuracy.
Finally, based on the prediction results, the model predictive control
algorithm is used to realize the real-time optimization of energy management.
The validity of the method is verified by simulation using collected data from
actual field experiments of our unmanned tracked vehicle. Compared with
multi-step neural networks, the prediction model based on CNN-LSTM improves the
prediction accuracy by 20%. Compared with the traditional regular energy
management strategy, the energy management strategy based on model predictive
control reduces fuel consumption by 7%.
