Adaptive Energy Management Strategy for a Hybrid Vehicle Using Energetic Macroscopic Representation

Mansour, Charbel; Salloum, N.; Francis, S. W.; Baroud, W.

doi:10.1109/vppc.2016.7791606

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…According to the optimization objectives, the optimization control strategy can be divided into instantaneous and global optimization control strategies. Mansour C [4] et al proposed a simple adaptive rule strategy based on short-term driving pattern recognition and dynamic programming global optimization program. Mingming Gao et al [5] proposed an extended-range electric bus energy management strategy based on a convex optimization algorithm, which can be better applied to the REEB energy management system to meet the requirements of the power system.…”

Section: Introductionmentioning

confidence: 99%

Research on Energy Management Strategy of a Hybrid Commercial Vehicle Based on Deep Reinforcement Learning

Xi,

Ma,

Wang

et al. 2023

WEVJ

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Given the influence of the randomness of driving conditions on the energy management strategy of vehicles, deep reinforcement learning considering driving conditions prediction was proposed. A working condition prediction model based on the BP neural network was established, and the correction coefficient of vehicle demand torque was determined according to the working condition prediction results. An energy management strategy and deep reinforcement learning were integrated to build an energy management strategy with deep reinforcement learning based on driving condition prediction. Simulation experiments were conducted according to the actual collected working condition data. The experimental results show that the energy management strategy, i.e., deep reinforcement learning considering working condition prediction, has faster convergence speed and more vital self-learning ability, and the equivalent fuel consumption per 100 km under different driving conditions is 6.411 L/100 km, 6.327 L/100 km, and 6.388 L/100 km, respectively. Compared with the unimproved strategy, the fuel economy can be improved by 3.18%, 3.08%, and 2.83%. The research shows that the energy management strategy, the deep reinforcement learning based on driving condition prediction, is effective and adaptive.

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Section: Introductionmentioning

confidence: 99%

Research on Energy Management Strategy of a Hybrid Commercial Vehicle Based on Deep Reinforcement Learning

Xi,

Ma,

Wang

et al. 2023

WEVJ

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“…Plug-in HEVs (PHEVs), as a combination of BEVs and HEVs, can fully merge both advantages of a certain all-electric range (AER) and superior fuel efficiency improvement [4]. The key technologies regarding PHEVs includes proper design of powertrain components associated with the power allocation amongst the main components, referred to as energy management strategies (EMSs) [5].…”

mentioning

confidence: 99%

An adaptive equivalent consumption minimization strategy for plug-in hybrid electric vehicles based on traffic information

Lei

Qin

Hou

et al. 2020

Energy

110

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Energy management strategies play an important role in performance optimization of plug-in hybrid electric vehicles (PHEVs), and can be further improved by incorporating external traffic information. Motivated by this, an adaptive equivalent consumption minimization strategy considering traffic information is proposed in this study to facilitate the effective energy management of PHEVs. First, the initial equivalent factors in terms of different initial state of charge (SOC) and driving distance are searched by genetic algorithm. Then, the simplified dynamic programming is leveraged to determine the optimal SOC trajectory according to the traffic information with fast calculation speed. The fuzzy controller is employed to regulate the equivalent factor dynamically, thus enabling effective tracking of the reference SOC trajectory. A hardware-in-the-loop simulation platform based on the virtual scene is developed to validate the performance of controller. Simulation and experimental results highlight that the proposed strategy can lead to less fuel consumption, compared to traditional equivalent consumption minimization strategy, thereby proving its feasibility.

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Rule-Based Online Energy Management Strategy for Power-Split Plug-In Hybrid Electric Vehicles

Chen

Guo

et al. 2018

2018 IEEE Vehicle Power and Propulsion Conference (VPPC)

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Adaptive Energy Management Strategy for a Hybrid Vehicle Using Energetic Macroscopic Representation

Cited by 5 publications

References 10 publications

Research on Energy Management Strategy of a Hybrid Commercial Vehicle Based on Deep Reinforcement Learning

Research on Energy Management Strategy of a Hybrid Commercial Vehicle Based on Deep Reinforcement Learning

An adaptive equivalent consumption minimization strategy for plug-in hybrid electric vehicles based on traffic information

Rule-Based Online Energy Management Strategy for Power-Split Plug-In Hybrid Electric Vehicles

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