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Progress and summary of reinforcement learning on energy management of MPS-EV

arXiv 2211.04001 ↗First posted 8 Nov 2022cs.LG eess.SP physics.app-phcited by 0

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Abstract (arXiv)

The high emission and low energy efficiency caused by internal combustion engines (ICE) have become unacceptable under environmental regulations and the energy crisis. As a promising alternative solution, multi-power source electric vehicles (MPS-EVs) introduce different clean energy systems to improve powertrain efficiency. The energy management strategy (EMS) is a critical technology for MPS-EVs to maximize efficiency, fuel economy, and range. Reinforcement learning (RL) has become an effective methodology for the development of EMS. RL has received continuous attention and research, but there is still a lack of systematic analysis of the design elements of RL-based EMS. To this end, this paper presents an in-depth analysis of the current research on RL-based EMS (RL-EMS) and summarizes the design elements of RL-based EMS. This paper first summarizes the previous applications of RL in EMS from five aspects: algorithm, perception scheme, decision scheme, reward function, and innovative training method. The contribution of advanced algorithms to the training effect is shown, the perception and control schemes in the literature are analyzed in detail, different reward function settings are classified, and innovative training methods with their roles are elaborated. Finally, by comparing the development routes of RL and RL-EMS, this paper identifies the gap between advanced RL solutions and existing RL-EMS. Finally, this paper suggests potential development directions for implementing advanced artificial intelligence (AI) solutions in EMS.

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References · 25

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  3. 2015Continuous control with deep reinforcement learningcited by 3,407
  4. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  5. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  6. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  7. 2016Concrete Problems in AI Safetycited by 1,642
  8. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,240
  9. 2018IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architecturescited by 883
  10. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 785
  11. 2017Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithmcited by 734
  12. 2018Distributed Prioritized Experience Replaycited by 269
13 more
  1. 2019Google Research Football: A Novel Reinforcement Learning Environmentcited by 260
  2. 2016Continuous Deep Q-Learning with Model-based Accelerationcited by 157
  3. 2017Trial without Error: Towards Safe Reinforcement Learning via Human Interventioncited by 103
  4. 2021Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domaincited by 58
  5. 2022Safe Reinforcement Learning for Legged Locomotioncited by 35
  6. 2020Learning to Locomote: Understanding How Environment Design Matters for Deep Reinforcement Learningcited by 27
  7. 2021Smart Online Charging Algorithm for Electric Vehicles via Customized Actor-Critic Learningcited by 9
  8. 2019Skynet: A Top Deep RL Agent in the Inaugural Pommerman Team Competitioncited by 7
  9. 2020Transfer Deep Reinforcement Learning-enabled Energy Management Strategy for Hybrid Tracked Vehiclecited by 3
  10. 2020Learning Time Reduction Using Warm Start Methods for a Reinforcement Learning Based Supervisory Control in Hybrid Electric Vehicle Applicationscited by 3
  11. 2020Adaptive Energy Management for Real Driving Conditions via Transfer Reinforcement Learningcited by 2
  12. 2020Data-Driven Transferred Energy Management Strategy for Hybrid Electric Vehicles via Deep Reinforcement Learningcited by 2
  13. 2020Transferred Energy Management Strategies for Hybrid Electric Vehicles Based on Driving Conditions Recognitioncited by 1

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