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1 |
Deep reinforcement learning based energy management for a hybrid electric vehicle
مدیریت انرژی مبتنی بر یادگیری تقویت عمیق برای یک وسیله نقلیه الکتریکی هیبریدی-2020 This research proposes a reinforcement learning-based algorithm and a deep reinforcement learningbased
algorithm for energy management of a series hybrid electric tracked vehicle. Firstly, the powertrain
model of the series hybrid electric tracked vehicle (SHETV) is constructed, then the corresponding
energy management formulation is established. Subsequently, a new variant of reinforcement learning
(RL) method Dyna, namely Dyna-H, is developed by combining the heuristic planning step with the Dyna
agent and is applied to energy management control for SHETV. Its rapidity and optimality are validated
by comparing with DP and conventional Dyna method. Facing the problem of the “curse of dimensionality”
in the reinforcement learning method, a novel deep reinforcement learning algorithm deep Qlearning
(DQL) is designed for energy management control, which uses a new optimization method
(AMSGrad) to update the weights of the neural network. Then the proposed deep reinforcement learning
control system is trained and verified by the realistic driving condition with high-precision, and is
compared with the benchmark method DP and the traditional DQL method. Results show that the
proposed deep reinforcement learning method realizes faster training speed and lower fuel consumption
than traditional DQL policy does, and its fuel economy quite approximates to global optimum.
Furthermore, the adaptability of the proposed method is confirmed in another driving schedule. Keywords: Hybrid electric tracked vehicle | Energy management | Dyna-H | Deep reinforcement learning | AMSGrad optimizer |
مقاله انگلیسی |
2 |
Deep reinforcement learning based energy management for a hybrid electric vehicle
مدیریت انرژی مبتنی بر یادگیری تقویت عمیق برای یک وسیله نقلیه برقی هیبریدی-2020 This research proposes a reinforcement learning-based algorithm and a deep reinforcement learningbased
algorithm for energy management of a series hybrid electric tracked vehicle. Firstly, the powertrain
model of the series hybrid electric tracked vehicle (SHETV) is constructed, then the corresponding
energy management formulation is established. Subsequently, a new variant of reinforcement learning
(RL) method Dyna, namely Dyna-H, is developed by combining the heuristic planning step with the Dyna
agent and is applied to energy management control for SHETV. Its rapidity and optimality are validated
by comparing with DP and conventional Dyna method. Facing the problem of the “curse of dimensionality”
in the reinforcement learning method, a novel deep reinforcement learning algorithm deep Qlearning
(DQL) is designed for energy management control, which uses a new optimization method
(AMSGrad) to update the weights of the neural network. Then the proposed deep reinforcement learning
control system is trained and verified by the realistic driving condition with high-precision, and is
compared with the benchmark method DP and the traditional DQL method. Results show that the
proposed deep reinforcement learning method realizes faster training speed and lower fuel consumption
than traditional DQL policy does, and its fuel economy quite approximates to global optimum.
Furthermore, the adaptability of the proposed method is confirmed in another driving schedule.. Keywords: Hybrid electric tracked vehicle | Energy management | Dyna-H | Deep reinforcement learning | AMSGrad optimizer |
مقاله انگلیسی |