Supervisor of Master's Candidates
Name (Simplified Chinese): 吴慕遥
Name (Pinyin): wumuyao
Date of Birth: 1995-12-08
Date of Employment: 2022-12-27
School/Department: 汽车与交通工程学院
Education Level: With Certificate of Graduation for Doctorate Study
Business Address: 安徽省合肥市屯溪路193号合肥工业大学格物楼515
Gender: Male
Degree: Doctoral Degree in Engineering
Professional Title: Lecturer
Status: Employed
Alma Mater: 中国科学技术大学
Supervisor of Master's Candidates
Discipline: Automobile Engineering
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State of health estimation of the lithium-ion power battery based on the principal component analysis-particle swarm optimization-back propagation neural network
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Impact Factor:9.0
DOI number:10.1016/j.energy.2023.129061
Journal:Energy
Key Words:State of health; Lithium-ion power battery; Aging features; Principal component analysis; Particle swarm optimization; Back propagation neural network
Abstract:State of Health (SOH) estimation of the lithium-ion power battery has become the focus of the research and it has important scientific significance for optimizing the battery energy management strategy as well as prolonging the battery life. However, the reaction mechanism of lithium-ion power battery is complex with strong nonlinear and time-varying. Meanwhile, the complex and varied external operating environment and operating conditions increase the uncertainty of the lithium-ion power battery performance decline and further increase the difficulty of SOH estimation. The SOH estimation method of the lithium-ion power battery based on the Principal Component Analysis-Particle Swarm Optimization-Back Propagation Neural Network (PCA-PSO-BPNN) is proposed in this paper. The PCA is adopted to reduce the system input dimension, the PSO is used to optimize the weights of BPNN and the optimized BPNN is applied to estimate SOH accurately. Experimental results on the lithium-ion power battery of the NASA battery aging test data demonstrate the effectiveness of the proposed method and it can reach more excellent SOH estimation results. The Mean Absolute Error is no more than 0.51%, the Root Mean Square Error is no more than 0.65% and the Maximum Absolute Error is no more than 1.86%, respectively.
Note:中科院1区Top
Co-author:Yiming Zhong,Ji Wu,Yuqing Wang
First Author:Muyao Wu
Indexed by:Journal paper
Correspondence Author:Li Wang
Document Code:129061
Discipline:Engineering
Document Type:J
Volume:283
ISSN No.:0360-5442
Translation or Not:no
Date of Publication:2023-09-12
Included Journals:SCI、EI
Links to published journals:https://www.sciencedirect.com/science/article/pii/S0360544223024556
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