Deep Reinforcement Learning Based on Driver Experience Embedding for Energy Management Strategies in Hybrid Electric Vehicles

Hu, Dong; Zhang, Yuanyuan

doi:10.1002/ente.202200123

Cited by 15 publications

(3 citation statements)

References 41 publications

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“…In recent years, deep learning algorithms have shown noteworthy contributions in various signal processing applications because of their faster conversions, high accuracy, reliability, and effectiveness [6], [7], [12]. In the future, various deep learning-based systems can be employed for driving and vehicle condition data augmentation to create the synthetic data for the simulation using available limited datasets [8], [9], [13].…”

Section: ░ 4 Simulation Results and Discussionmentioning

confidence: 99%

Grey Wolf Optimization Based Energy Management Strategy for Hybrid Electrical Vehicles

Gadge

Pahariya

2022

IJEER

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Electric vehicles (EVs) are seen as a necessary component of transportation's future growth. However, the performance of batteries related to power density and energy density restricts the adoption of electric vehicles. To make the transition from a conventional car to a pure electric vehicle (PEV), a Hybrid Electric Vehicle's (HEV) Energy Management System (EMS) is crucial. The HEVs are often powered with hybrid electrical sources, therefore it is important to select the optimal power source to improve the HEV performance, minimize the fuel cost and minimize hydrocarbon and nitrogen oxides emission. This paper presents the Grey Wolf Optimization (GWO) algorithm for the control of the power sources in the HEVs based on power requirement and economy. The proposed GWO-based EMS provides optimized switching of the power sources and economical and pollution free control of HEV.

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Section: ░ 4 Simulation Results and Discussionmentioning

confidence: 99%

Grey Wolf Optimization Based Energy Management Strategy for Hybrid Electrical Vehicles

Gadge

Pahariya

2022

IJEER

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“…Reinforcement learning (RL) has been applied to energy management in PHEVs, and the results have been promising. [ 18 ] In ref. [19], a Q ‐learning‐based EMS for PHEVs was proposed, which makes decisions using a look‐up Q table.…”

Section: Introductionmentioning

confidence: 99%

Plug‐In Hybrid Electric Vehicle Energy Management with Clutch Engagement Control via Continuous‐Discrete Reinforcement Learning

Gong,

Xu,

Lin

2024

Energy Tech

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Energy management strategy (EMS) is a key technology for plug‐in hybrid electric vehicles (PHEVs). The energy management of certain series–parallel PHEVs involves the control of continuous variables, such as engine torque, and discrete variables, such as clutch engagement/disengagement. Herein, a control‐oriented model is established for a series–parallel plug‐in hybrid system with clutch engagement control from the perspective of mixed‐integer programming. Subsequently, an EMS based on continuous‐discrete reinforcement learning (CDRL), which enables simultaneous output of continuous and discrete variables, is designed. During training, state‐of‐charge (SOC) randomization is introduced to ensure that the hybrid system exhibits optimal energy‐saving performance in both high and low SOC. Finally, the effectiveness of the proposed CDRL strategy is verified by comparing EMS based on charge‐depleting charge‐sustaining (CD‐CS) with rule‐based clutch engagement control and dynamic programming (DP). In the simulation results, it is shown that, under a high SOC, the CDRL strategy proposed in this article can improve energy efficiency by 8.3% compared to CD‐CS, and the energy consumption is just 6.6% higher than the global optimum based on DP, while under a low SOC, the numbers are 4.1% and 3.9%, respectively.

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“…As model-free EMSs, DRL-based EMS was capable to adapt to dynamic driving conditions. [20][21][22] Compared to other machine learning-based EMSs, reinforcement learning algorithms optimize strategy functions for the expected cumulative return rather than only considering the current immediate payoff, therefore allowing for better global optimality. In addition, the DRL algorithm still shows great potential when applied to large-scale data processing.…”

Section: Introductionmentioning

confidence: 99%

Energy Management Strategy Based on Deep Reinforcement Learning and Speed Prediction for Power‐Split Hybrid Electric Vehicle with Multidimensional Continuous Control

et al. 2023

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An efficient energy management strategy (EMS) is significant to improve the economy of hybrid electric vehicles (HEVs). Herein, a power‐split HEV model is built and validated against test results, and then the EMS is proposed for this model based on vehicle speed prediction and deep reinforcement learning (DRL) algorithms. The rule‐based local controller and global optimal empirical knowledge are introduced to enhance the convergence speed. It is shown in the results that the twin delayed deep deterministic policy gradient algorithm (TD3) achieves more satisfactory performance on converge speed and energy efficiency. The networks of the DRL algorithm with continuous control update more robustly during iterations, in contrast to the discrete ones. Although the power‐split HEV with lower control dimension can reduce the learning burden for DRL EMS; however, the multidimensional control space shows greater optimization potential. As a result, the equivalent fuel consumption of TD3‐based EMS with multidimensional continuous control differences from the global optimal algorithm only by 4.92%. Herein, it is demonstrated in the results that long short‐term memory recurrent neural network (LSTM RNN) performs better for vehicle speed prediction than classical RNN and BP neural network, and the predictive vehicle speed feature helps improve fuel economy by 0.55%.

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Deep Reinforcement Learning Based on Driver Experience Embedding for Energy Management Strategies in Hybrid Electric Vehicles

Cited by 15 publications

References 41 publications

Grey Wolf Optimization Based Energy Management Strategy for Hybrid Electrical Vehicles

Grey Wolf Optimization Based Energy Management Strategy for Hybrid Electrical Vehicles

Plug‐In Hybrid Electric Vehicle Energy Management with Clutch Engagement Control via Continuous‐Discrete Reinforcement Learning

Energy Management Strategy Based on Deep Reinforcement Learning and Speed Prediction for Power‐Split Hybrid Electric Vehicle with Multidimensional Continuous Control

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