Dynamic time prediction for electric vehicle charging based on charging pattern recognition
Logistics Engineering College, Shanghai Maritime University, Shanghai 200135, China; School of Economics and Management, Beijing Jiaotong University, Beijing 102603, China; School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China; Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, China; Jiangsu Key Laboratory of Big Data Analysis Technology (B-DAT), Nanjing University of Information Science and Technology, Nanjing 210044, China; less
Received: 2022-05-16
Accepted: 2023-02-27
Available online: 2023-02-27
Abstract
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 to effectively prevent the battery from overcharging. Due to the complex structure of the battery pack and various s, the traditional 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) s, a prediction method with recognition is proposed. First, an intelligent algorithm based on dynamic weighted density peak clustering (DWDPC) and fusion is proposed to classify vehicle s. Then, on the basis of an improved simplified particle swarm optimization (ISPSO) algorithm, a high-performance prediction method is constructed by fully integrating and a strong tracking filter. Finally, the data run by the actual engineering system are verified for the proposed prediction algorithm. Experimental results show that the new method can effectively distinguish the s of different vehicles, identify the charging characteristics of different electric vehicles, and achieve high prediction accuracy.
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