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Dynamic time prediction for electric vehicle charging based on charging pattern recognition

Frontiers of Information Technology & Electronic Engineering 2023年第2期24卷 299-313页

作者： Chunxi LI Yingying FU Xiangke CUI Quanbo GE Logistics Engineering College Shanghai Maritime UniversityShanghai200135China School of Economics and Management Beijing Jiaotong UniversityBeijing102603China School of Automation Nanjing University of Information Science and TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science and TechnologyNanjing210044China Jiangsu Key Laboratory of Big Data Analysis Technology(B-DAT) Nanjing University of Information Science and TechnologyNanjing210044China

Overcharging is an important safety issue in the charging process of electric vehicle power batteries,and can easily lead to accelerated battery aging and serious safety accidents.It is necessary to accurately predict the vehicle’s charging time to effectively prevent the battery from overcharging.Due to the complex structure of the battery pack and various charging modes,the traditional charging time prediction method often encounters modeling difficulties and low accuracy.In response to the above problems,data drivers and machine learning theories are applied.On the basis of fully considering the different electric vehicle battery management system(BMS)charging modes,a charging time prediction method with charging mode recognition is proposed.First,an intelligent algorithm based on dynamic weighted density peak clustering(DWDPC)and random forest fusion is proposed to classify vehicle charging modes.Then,on the basis of an improved simplified particle swarm optimization(ISPSO)algorithm,a high-performance charging time prediction method is constructed by fully integrating long short-term memory(LSTM)and a strong tracking filter.Finally,the data run by the actual engineering system are verified for the proposed charging time prediction algorithm.Experimental results show that the new method can effectively distinguish the charging modes of different vehicles,identify the charging characteristics of different electric vehicles,and achieve high prediction accuracy.

关键词： charging mode charging time Random forest Long short-term memory(LSTM) Simplified particle swarm optimization(SPSO)

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Multi-stage constant current charging strategy considering SOC intervals and voltage thresholds

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Global Energy Interconnection 2022年第2期5卷 143-153页

作者： Yu Zhang Si Xu Tiezhou Wu Hubei University of Technology Hubei Key Laboratory of Solar Energy Efficient Utilization and Energy Storage Operation ControlHubeiWuhan 430068P.R.China

Conventional multi-stage constant current charging strategies often use higher multiples of current to charge the battery in pursuit of shorter charging times.However,this leads to an increase in battery temperature,while shortening the charging time.This in turn affects the safety of the charging process.Furthermore,the higher charging currents are not ideal for shortening the charging time in the later stages of charging.To solve the aforementioned problems,in this study,a multi-stage constant current charging strategy is presented.This strategy can shorten the battery charging time by using the increase in battery temperature during the charging process as a constraint,using a genetic algorithm to calculate the charging current value,and investigating the phased approach to charging.Finally,the charging strategy is experimentally validated at different ambient temperatures and different initial SOCs.The experimental results show that the charging strategy proposed in this paper not only reduces the amount of calculations,but also reduces the temperature rise by up to 46.4%and charging time by up to 4.2%under different operating conditions.

关键词： charging time Battery temperature rise Genetic algorithm Multi-stage constant current charging

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维普期刊数据库

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