In this paper, an online energy management strategy (EMS) for hybrid electric tracked vehicle (HETV) is developed based on deep deterministic policy gradient (DDPG) with time-varying weighting factor to further improve economic performance of HETV and reduce computational burden. The DDPG is applied to model the EMS problem for the target HETV. Especially, a time-varying weighting factor is introduced here to update old network parameters with experience learned from most recent cycle segment. Afterwards, simulation is conducted to verify the effectiveness and adaptability of the proposed method. Results show that DDPG-based EMS with online updating mechanism can achieve nearly 90% fuel economy performance as that of dynamic programming while computational time is greatly reduced. Finally, hardware-in-loop experiment is carried out to evaluate the real-world performance of the proposed method.

Energy management strategy (EMS) is important to ensure energy-saving performance of hybrid electric vehicle (HEV). However, the power coupling property between different power sources, together with stochastic power demand fluctuation poses great challenges for EMS to achieve desirable performance in real-world scenario. This paper presents an uncertainty-aware energy management strategy for HEV. A speed predictor combining convolutional neural network and long short-term memory neural network is proposed to extract temporal features that could reveal speed change mechanism. Then an online self-adaptive transition probability matrix is constructed to estimate the speed prediction uncertainty. Tube model predictive control (tube-MPC) is finally used to solve the optimization control problem in a receding horizon manner. The robust set introduced in the tube-MPC greatly enhances the optimality and robust-ness of the control sequence under the scenario with speed prediction uncertainty. Simulations are conducted to verify the effectiveness of the proposed method. Results show that the speed prediction accuracy is 47.4% and 23.1% higher than exponential decay rate prediction model and autoregressive integrated moving average model respectively. Compared with traditional rule-based and MPC method, the proposed tube-MPC method could achieve 10.7% and 3.0% energy-saving performance improvement in average.

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