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| Research on Energy Management Strategy for Hybrid Electric Bus Based on Deep Reinforcement Learning |
| DOI：10.13949/j.cnki.nrjgc.2021.06.002 |
| Key Words:dual-planetary hybrid powertrain energy management deep reinforcement learning |
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| Abstract:Taking a dual planetary hybrid bus as a sample vehicle, the energy management strategies based on double deep Q-learning (DDQN) and twin delayed deep deterministic policy gradient (TD3) were proposed respectively for the discrete control and continuous control of the control variable diesel engine speed, and the prioritized experience replay was used to optimize the strategy. The energy management characteristics of the sample vehicle under C-WTVC condition were studied by simulation. Results show that compared with dynamic programming (DP), DDQN and TD3 strategies have fast convergence speed and strong adaptive ability. Similar to DP strategy, DDQN and TD3 strategies use pure electric drive at low speed and low torque, and choose hybrid drive at high speed and high torque. With the three strategies, the diesel engine mainly works in the low and middle speed range, and TD3 strategy can continuously control diesel engine speed. The fuel consumption of DDQN and TD3 strategies is 0.1951L/km and 0.1948L/km, respectively. And the fuel economy of the two strategies reaches 93% of that of DP strategy, which proves the effectiveness of DDQN and TD3 strategies. |
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