An Energy Management Strategy Based on Fuzzy Logic for Hybrid Energy Storage System in Electric Vehicles

Shen, Yongpeng; Li, Yuanfeng; Tang, Yaohua; Sun, Jianbin; Zhao, Jun; Yang, Xiaoliang

doi:10.1002/tee.23487

Cited by 17 publications

(6 citation statements)

References 26 publications

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“…Some studies have used scenario clustering methods to improve computational efficiency while preserving the characteristics of PV-Load [16], [17]. Scenario generation methods such as k-means clustering algorithm [18], fuzzy C-means clustering algorithm [19], and hierarchical clustering [20] have been widely applied. However, these methods measure similarity using Euclidean distance when dealing with high-dimensional time series data, and the clustering scenarios need to be determined in advance, resulting in lower clustering quality.…”

Section: Introductionmentioning

confidence: 99%

Optimal Configuration of ESS in Distribution Network Considering Generation of PV-Load Time Series Scenarios

Xiao,

Shi,

et al. 2024

IEEE Access

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With the accelerating application demand for new energy-based power systems, a energy storage system (ESS) optimization configuration method is proposed to fully consider the load demand and the uncertainty of photovoltaic (PV) output and improve the adaptability of the ESS configuration scale. Firstly, the Gaussian kernel function of the kernel method is used to map the temporal variation characteristics of the PV output scenario to establish a comprehensive evaluation index scenario for the uncertainty and temporal correlation of PV-Load. The iterative self-organizing data analysis algorithm (ISODATA) is used to optimize clustering of the comprehensive evaluation index scenario to generate PV-Load typical scenarios. A distribution network ESS configuration model is constructed with the objectives of minimizing voltage fluctuation indicator, line loss rate, and minimizing ESS investment cost. To solve the high-dimensional multi-objective functions in the model, an non-dominated sorting genetic algorithm-Ⅲ (NSGA-Ⅲ) was employed, and the optimal solution is selected by using the entropy weight method (EWM). Finally, the proposed method and model are analyzed and verified by case simulation, and the results show that the proposed model and method can comprehensively consider the actual operating characteristics of PV and load, and can effectively formulate the ESS configuration scale and operation strategy in the distribution network.

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Section: Introductionmentioning

confidence: 99%

Optimal Configuration of ESS in Distribution Network Considering Generation of PV-Load Time Series Scenarios

Xiao,

Shi,

et al. 2024

IEEE Access

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“…The EMS can be divided into rule‐based control, model‐based optimization, and learning‐based methods. The rule‐based control strategy controls the power demand of each power source in different driving modes [3–5]. This approach has better real‐time performance but does not involve direct optimal control due to usually using heuristics or engineering intuition with rules.…”

Section: Introductionmentioning

confidence: 99%

Energy Management Strategy Combining Double Deep Q‐Networks and Demand Torque Prediction for Connected Hybrid Electric Vehicles

Pang,

Zhang,

Jiao

et al. 2023

IEEJ Transactions Elec Engng

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This paper proposes an energy management strategy (EMS) based on double‐deep Q‐Networks (DDQN) with demand torque prediction (DTP) to optimize the fuel consumption of hybrid electric vehicles (HEVs) by online utilizing vehicle‐to‐vehicle (V2V) information. The main framework of the EMS is designed as DDQN, combining Q‐learning with the deep neural network to realize real‐time training and online optimization of the torque distribution between the internal combustion engine and the electric motor, and given that the model training easily falls into local minima and overestimation, two different Q‐networks are designed to decouple action selection and target evaluation. Meanwhile, BP‐network predicts future demand torque using the ego vehicle speed, front vehicle speed, and distance between the two vehicles to reduce the burden of model training due to the introduction of traffic information. The predicted demand torque is introduced into the DDQN‐based EMS as states, together with the distance traveled in the driving cycle and the current information on HEV. The effectiveness and adaptability to actual driving cycles of the proposed strategy are verified by comparison with DDQN‐based EMS without DTP and rule‐based EMS. The results show that the fuel consumption using DDQN‐based EMS without DTP is reduced by 4.1% compared with rule‐based EMS. The introduction of demand torque prediction resulted in a further 3.9% reduction in fuel consumption. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

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“…Shen et al [ 25 ] have suggested fuzzy logic control (FLC) for the EMS of HESS on EV. The degradation of the battery issues was solved by the utilization of battery and ultracapacitor (UC).…”

Section: Introductionmentioning

confidence: 99%

Energy Management in Plugin Hybrid Electric Vehicles with Hybrid Energy Storage System Using Hybrid Approach

Ramasamy

Chandramohan

Devadasu³

2022

Energy Tech

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Herein, an optimal control approach for the energy management of hybrid energy storage system (HESS) like battery, supercapacitor (SC), and integrated charging unit in plugin hybrid electric vehicle (PHEV) is proposed. The proposed approach is the combination of both the jellyfish search optimizer (JS) and gradient boosting decision tree algorithm (GBDT) and it is termed as JS‐GBDT approach. The high energy density battery and high power density SC are combined for satisfying the demand of vehicle. To balance the charging, an uncontrolled rectifier with DC to DC buck converter, and to guarantee smooth transition of energy, two bidirectional DC–DC buck–boost converters are utilized. To meet the load requirements, GBDT approach predicts and integrates the total power supply and the charging level of the power source. The output voltage regulation, reference generation, and smooth tracking of current are performed by the energy management of PHEV using JS approach. The proposed methodology is executed on MATLAB/Simulink working platform. The performance of the HESS is evaluated by utilizing the comparison analysis with existing systems. From the analysis, the proposed approach provides less stress for primary and secondary sources and improves the charging unit performance and enhances the battery life.

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An Energy Management Strategy Based on Fuzzy Logic for Hybrid Energy Storage System in Electric Vehicles

Cited by 17 publications

References 26 publications

Optimal Configuration of ESS in Distribution Network Considering Generation of PV-Load Time Series Scenarios

Optimal Configuration of ESS in Distribution Network Considering Generation of PV-Load Time Series Scenarios

Energy Management Strategy Combining Double Deep Q‐Networks and Demand Torque Prediction for Connected Hybrid Electric Vehicles

Energy Management in Plugin Hybrid Electric Vehicles with Hybrid Energy Storage System Using Hybrid Approach

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