Grey wolf fuzzy optimal energy management for electric vehicles based on driving condition prediction

Pan, Chaofeng; Tao, Yuanxue; Liu, Qian; He, Zhigang; Liang, Jun; Zhou, Weilin; Wang, Limei

doi:10.1016/j.est.2021.103398

Cited by 7 publications

(8 citation statements)

References 7 publications

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“…20 The Grey Wolf optimizer (GWO) algorithm is known for its simplicity in parameter settings, good robustness, fast convergence, and high accuracy in optimization. [21][22][23][24] Therefore, the GWO algorithm is utilized to optimize the initial weights and bias parameters of the LSTM neural network.…”

Section: Gwo-optimized Lstm Recognition Modelmentioning

confidence: 99%

“…The initial weights and parameters of the LSTM neural network have a significant impact on the model's performance and can easily get trapped in local optima, requiring optimization of its parameters 20 . The Grey Wolf optimizer (GWO) algorithm is known for its simplicity in parameter settings, good robustness, fast convergence, and high accuracy in optimization 21‐24 . Therefore, the GWO algorithm is utilized to optimize the initial weights and bias parameters of the LSTM neural network.…”

Section: Gwo‐lstm Working Condition Recognitionmentioning

confidence: 99%

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Adaptive fuzzy energy management strategy for range‐extended electric vehicles integrated with deep learning

Nie,

Chen,

Wei

et al. 2024

Energy Science & Engineering

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A novel framework for adaptive energy management, rooted in deep learning principles, is proposed to minimize fuel consumption in extended‐range electric vehicles amidst intricate driving scenarios. This innovative approach integrates a long short‐term memory (LSTM) network for pattern recognition across three driving patterns and an adaptive fuzzy controller. To mitigate the impact of poor hyperparameter selection on recognition accuracy, Gray Wolf Optimization is employed to optimize the hidden layer nodes, training times, and learning rate of the LSTM. Simultaneously, a genetic algorithm is utilized to optimize the vertex coordinates of the fuzzy control membership function, enabling the adaptive adjustment of parameters in the fuzzy energy management strategy. The condition recognition model accurately identifies the vehicle's driving status and seamlessly transitions to an energy management strategy tailored to the present conditions. This ensures optimal operation, enhancing overall fuel efficiency and performance. The simulation results robustly validate the efficacy of this approach: the GWO‐LSTM network achieves an impressive 97.7% accuracy in recognizing working conditions, surpassing the 88.9% accuracy of the traditional LSTM network. Furthermore, the fuel consumption reduction achieved by the adaptive fuzzy energy management strategy amounts to 11.9% compared with the conventional fuzzy energy management approach. This outcome underscores the tangible enhancement in vehicle fuel economy resulting from the seamless integration of deep learning techniques.

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Section: Gwo-optimized Lstm Recognition Modelmentioning

confidence: 99%

Section: Gwo‐lstm Working Condition Recognitionmentioning

confidence: 99%

Adaptive fuzzy energy management strategy for range‐extended electric vehicles integrated with deep learning

Nie,

Chen,

Wei

et al. 2024

Energy Science & Engineering

View full text Add to dashboard Cite

show abstract

“…[12][13][14] But a fuzzy control strategy with a good control effect is difficult to design directly under various driving conditions. 15 Thus, the fuzzy control is normally combined with other methods to better distribute the power output between different energy sources than a single fuzzy control strategy. A novel adaptive EMS, including the fuzzy-PI controller, the equivalent consumption minimization strategy (ECMS), and the particle swarm optimization (PSO) algorithm, was presented in Lin et al 16 The simulation results showed that the fuel consumption of the vehicle had been reduced by 19.3% than that of the ECMS.…”

Section: Rule-based Energy Management Strategiesmentioning

confidence: 99%

“…Moreover, the Q function is transformed as an iterative form to update the Q matrix, 37 as shown in Equation (15):…”

Section: The Implementation Of the Q-learning Rl Algorithmmentioning

confidence: 99%

“…The fuzzy control, based on fuzzy rule base, is an intelligent control that simulates human control experience and knowledge 12–14 . But a fuzzy control strategy with a good control effect is difficult to design directly under various driving conditions 15 . Thus, the fuzzy control is normally combined with other methods to better distribute the power output between different energy sources than a single fuzzy control strategy.…”

Section: Introductionmentioning

confidence: 99%

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A reinforcement learning‐based energy management strategy for a battery–ultracapacitor electric vehicle considering temperature effects

Wang

Tang

et al. 2023

Circuit Theory & Apps

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SummaryThe design of energy management strategy (EMS) plays a vital role in the power performance and economy of battery–ultracapacitor for electric vehicles. A reinforcement learning (RL)‐based EMS is proposed to obtain an optimal power allocation strategy for battery–ultracapacitor electric vehicle, and its robustness is verified at different temperatures. First of all, the dynamic characteristic experiments of the battery and ultracapacitor were performed at 10°C, 25°C, and 40°C to obtain mechanism characteristics at different temperatures. Secondly, a genetic algorithm is selected to identify the parameters of the battery and ultracapacitor model. Next, the RL‐based strategy takes the minimum energy loss of the hybrid energy storage system as the reward function and solves the optimal policy based on Markov theory. The simulation results show that the economy of the RL‐based strategy correspondingly improved by 3.05%, 3.20%, and 3.15% at different temperatures in comparison with the fuzzy‐based strategy, and the economic gap between the RL‐based strategy and the DP‐based strategy is further narrowed down to 7.30%, 3.88%, and 8.40% at different temperatures, respectively. Finally, the proposed strategy is validated under different driving conditions, which indicate that the RL‐based strategy can effectively reduce energy consumption and has good robustness at different temperatures.

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Fusion Deconvolution for Reliability Analysis of A Flywheel-Battery Hybrid Energy Storage System

Wang

Gao

Stanisławski

et al. 2022

Journal of Energy Storage

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Grey wolf fuzzy optimal energy management for electric vehicles based on driving condition prediction

Cited by 7 publications

References 7 publications

Adaptive fuzzy energy management strategy for range‐extended electric vehicles integrated with deep learning

Adaptive fuzzy energy management strategy for range‐extended electric vehicles integrated with deep learning

A reinforcement learning‐based energy management strategy for a battery–ultracapacitor electric vehicle considering temperature effects

Fusion Deconvolution for Reliability Analysis of A Flywheel-Battery Hybrid Energy Storage System

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