scholarly journals A Novel State of Charge Approach of Lithium Ion Battery Using Least Squares Support Vector Machine

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 195398-195410
Author(s):  
Jiabo Li ◽  
Min Ye ◽  
Wei Meng ◽  
Xinxin Xu ◽  
Shengjie Jiao
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
State Of Charge ◽  
Support Vector

scholarly journals On-Line Estimation Method of Lithium-Ion Battery Health Status Based on PSO-SVM

2021 ◽  
Vol 9 ◽  
Author(s):  
Ran Li ◽  
Wenrui Li ◽  
Haonian Zhang ◽  
Yongqin Zhou ◽  
Weilong Tian
Keyword(s):  
Support Vector Machine ◽  
Estimation Method ◽  
Particle Swarm ◽  
Lithium Ion ◽  
State Of Charge ◽  
Joint Estimation ◽  
Support Vector ◽  
Particle Swarm Algorithm ◽  
Battery Management System ◽  
Safe Driving

Battery management system (BMS) refers to a critical electronic control unit in the power battery system of electric vehicles. It is capable of detecting and estimating battery status online, especially estimating state of charge (SOC) and state of health (SOH) accurately. Safe driving and battery life optimization are of high significance. As indicated from recent literature reports, most relevant studies on battery health estimation are offline estimation, and several problems emerged (e.g., long time-consuming, considerable calculation and unable to estimate online). Given this, the present study proposes an online estimation method of lithium-ion health based on particle swarm support vector machine algorithm. By exploiting the data of National Aeronautics and Space Administration (NASA) battery samples, this study explores the changing law of battery state of charge under different battery health. In addition, particle swarm algorithm is adopted to optimize the kernel function of the support vector machine for the joint estimation of battery SOC and SOH. As indicated from the tests (e.g., Dynamic Stress Test), it exhibits good adaptability and feasibility. This study also provides a certain reference for the application of BMS system in electric vehicle battery online detection and state estimation.

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