{"title":"An adaptive co-state design method for PMP-based energy management of plug-in hybrid electric vehicles based on fuzzy logical control","authors":"","doi":"10.1016/j.est.2024.114118","DOIUrl":null,"url":null,"abstract":"<div><div>The determination of the optimal co-state in Pontryagin's minimum principle-based (PMP-based) energy management strategy (EMS) in real-time remains a significant challenge. This paper proposes a fuzzy logic-based approach to tackle this problem. Firstly, an offline optimization method based on the multi-island genetic algorithm (MIGA) is proposed to calculate the optimal co-state of the PMP-based EMS for a plug-in hybrid electric vehicle (PHEV) based on the provided driving cycles. Secondly, a comprehensive evaluation of the influence on the optimal co-state is conducted based on the vehicle's velocity and load, utilizing real-life and representative driving scenarios. Subsequently, a fuzzy logic-based controller is formulated for online modification of the co-state, with inputs including vehicle velocity, load, and acceleration. Finally, the proposed method is evaluated against benchmarks including dynamic programming (DP), charge-depleting and charge-sustaining (CD-CS), and PMP-constant solutions using nine actual driving cycles. The findings demonstrate that the controller with the fuzzy logic method displays significant adaptability to diverse driving cycles. The proposed PMP-adaptive strategy exhibits significant improvement compared to CD-CS, with energy-saving effectiveness approaching DP solutions. In addition, the computational efficiency of the PMP-adaptive is superior to that of the CD-CS, which presents a valuable advantage for real-time applications.</div></div>","PeriodicalId":15942,"journal":{"name":"Journal of energy storage","volume":null,"pages":null},"PeriodicalIF":8.9000,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of energy storage","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352152X24037046","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0
Abstract
The determination of the optimal co-state in Pontryagin's minimum principle-based (PMP-based) energy management strategy (EMS) in real-time remains a significant challenge. This paper proposes a fuzzy logic-based approach to tackle this problem. Firstly, an offline optimization method based on the multi-island genetic algorithm (MIGA) is proposed to calculate the optimal co-state of the PMP-based EMS for a plug-in hybrid electric vehicle (PHEV) based on the provided driving cycles. Secondly, a comprehensive evaluation of the influence on the optimal co-state is conducted based on the vehicle's velocity and load, utilizing real-life and representative driving scenarios. Subsequently, a fuzzy logic-based controller is formulated for online modification of the co-state, with inputs including vehicle velocity, load, and acceleration. Finally, the proposed method is evaluated against benchmarks including dynamic programming (DP), charge-depleting and charge-sustaining (CD-CS), and PMP-constant solutions using nine actual driving cycles. The findings demonstrate that the controller with the fuzzy logic method displays significant adaptability to diverse driving cycles. The proposed PMP-adaptive strategy exhibits significant improvement compared to CD-CS, with energy-saving effectiveness approaching DP solutions. In addition, the computational efficiency of the PMP-adaptive is superior to that of the CD-CS, which presents a valuable advantage for real-time applications.
期刊介绍:
Journal of energy storage focusses on all aspects of energy storage, in particular systems integration, electric grid integration, modelling and analysis, novel energy storage technologies, sizing and management strategies, business models for operation of storage systems and energy storage developments worldwide.