Optimization of fuzzy control energy management strategy for fuel cell vehicle power system using a multi‐islandgenetic algorithm

Zhao, Zhen; Wang, Tie; Li, Meng; Wang, Heng; Wang, Yiquan

doi:10.1002/ese3.835

Cited by 36 publications

(6 citation statements)

References 44 publications

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“…A multi-island genetic algorithm was used to optimize the MFs and demonstrated optimized fuzzy control EMS in simulations of two EMSs under four driving cycles. The simulation results confirmed that the optimized fuzzy control EMS provided smoother and more stable output power from FCS, reducing hydrogen consumption by 8.4%, 1.1%, 5.1%, and 7.6%, respectively, compared to the original fuzzy control EMS [24]. Jibin et al proposed a parameter matching approach for a hybrid energy storage system based on a multi-objective optimization algorithm with a system simulation model in the loop designed by using an NSGA-II-type genetic algorithm.…”

Section: Introductionsupporting

confidence: 54%

Parameter Matching, Optimization, and Classification of Hybrid Electric Emergency Rescue Vehicles Based on Support Vector Machines

et al. 2022

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Based on the requisition for an ideal precise power source for a hybrid electric emergency rescue vehicle (HE-ERV), we present an optimistic parameter matching and optimization schemes for the selection of a HE-ERV. Then, given a set of optimized power source components, they are classified into different types of HE-ERV. In this study, due to the different design objectives of different types of emergency rescue vehicles and the problems of hybrid electric vehicle parameter matching, a multi-island genetic algorithm (MIGA) and non-linear programming quadratic Lagrangian (NLPQL) is proposed for the matched parameters. The vehicle dynamic model is established based on the AVL Cruise simulation platform. The power source performance parameters are matched by theoretical analysis and coupled to the simulation platform. Finally, the optimized matched parameters are classified based on the support vector machines classification model to determine the category of the HE-ERV. The classification results showed that there is an unprecedented level for categorizing several factors of the power source parameters. This research showed that its more logical and reasonable to match HE-ERVs with medium motor/engine power output and battery capacity, as these can attain dynamic performance, extended driving range, and reduced energy consumption.

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Section: Introductionsupporting

confidence: 54%

Parameter Matching, Optimization, and Classification of Hybrid Electric Emergency Rescue Vehicles Based on Support Vector Machines

et al. 2022

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“…The consumption of hydrogen fuel is applied relying on the optimal control theory to regulate the ratio of energy delivered by the integrated PEMFC Battery power system. Zhen Zhao, et al [20], proposed fuzzy control optimization of EMS for FCEV relying on a multi-island genetic algorithm. In 2021, Jiawen Li, et al [21], introduces a Largescale control system of multi-agent deep learning-based management strategy for energy control optimization of PEMFC.…”

Section: Related Workmentioning

confidence: 99%

AI based Metaheuristic Optimization of PEMFC Control System for Robust and Efficient Operation

Aboshosha

Gouda

Awad

et al. 2023

Preprint

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Proton Exchange Membrane Fuel Cells (PEMFC) have several merits over the other fuel cells like low operating temperature, high power density, light weight, and robustness, fitting them in a wide variety of green energy applications. The most important application area of the PEMFC is hydrogen cars (Fuel Cell Electric Vehicles – FCEV), stationary and portable hydrogen power systems. Throughout the presented research work the complete three cycles of the fuel cell control are presented which are the control system, the FCEV control and the AI based metaheuristic optimization. The presented algorithm includes the PI controller – PI-based HHO-PI-based AEO-PI-based GWO-PI-based PSO. An objective statistical comparison among the presented algorithms to select the best one of them according to perfect criteria.

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“…With the ability to adjust the control according to the changing environment, enhance the robustness of the control system, anti-interference, etc. [11][12][13][14][15][16][17]. But its complex calculation will increase the amount of calculation of the control system, affect the stability of the system.…”

Section: Introductionmentioning

confidence: 99%

Research on Multi‐Algorithm Switching Hybrid Control of Permanent Magnet Synchronous Motor for Electric Special Vehicles

Wu,

Liu

2023

IEEJ Transactions Elec Engng

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In order to improve the smoothness of the electric special vehicle (ESV) under each operational condition, a multi‐algorithm switching hybrid control (MASHC) algorithm is proposed for the permanent magnet synchronous motor (PMSM) of ESV. For a steady–state operation state, the transfer function structure of the motor control system is optimized, avoiding its output variables from varying with the integral and differential changes of the input quantity to enhance the stability and response speed of the control system. For transient operation states, it is considered to avoid the torque ripple that can be generated due to the increased computational effort of introducing an intelligent algorithm. The fuzzy proportional (FP) control algorithm that can replicate the transient performance of the fuzzy control algorithm is designed to improve the control accuracy of the control system. In addition, a switching function is designed to blend the two algorithms to improve the overall vehicle operation performance. To verify the effectiveness of the control algorithm, a simulation model and an experimental platform are developed. Both simulation and experimental results show that the proposed control algorithm has the advantages of no overshoot and oscillation in output, which can improve the smoothness of the ESV drive system. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

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Optimization of fuzzy control energy management strategy for fuel cell vehicle power system using a multi‐islandgenetic algorithm

Cited by 36 publications

References 44 publications

Parameter Matching, Optimization, and Classification of Hybrid Electric Emergency Rescue Vehicles Based on Support Vector Machines

Parameter Matching, Optimization, and Classification of Hybrid Electric Emergency Rescue Vehicles Based on Support Vector Machines

AI based Metaheuristic Optimization of PEMFC Control System for Robust and Efficient Operation

Research on Multi‐Algorithm Switching Hybrid Control of Permanent Magnet Synchronous Motor for Electric Special Vehicles

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