On-Line Estimation Method of Lithium-Ion Battery Health Status Based on PSO-SVM
| Autor: | Li Ran, Haonian Zhang, Zhou Yongqin, Wenrui Li, Weilong Tian |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Electronic control unit
Battery (electricity) Economics and Econometrics Renewable Energy Sustainability and the Environment State of health Computer science 020209 energy SVM 020208 electrical & electronic engineering SOH Energy Engineering and Power Technology Particle swarm optimization 02 engineering and technology Lithium-ion battery General Works Reliability engineering Support vector machine Fuel Technology State of charge 0202 electrical engineering electronic engineering information engineering Electric-vehicle battery SOC BMS lithium battery |
| Zdroj: | Frontiers in Energy Research, Vol 9 (2021) |
| DOI: | 10.3389/fenrg.2021.693249/full |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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