A Novel Method of SOC Estimation for Electric Vehicle Based on Adaptive Particle Filter
Aimed at improving SOC estimation accuracy, speed and robust of battery on electric vehicle, SOC estimation method based on adaptive particle filter is proposed. 1-order RC and lag model, 2-order RC and lag model, 3-order RC and lag model are built. Particle Swarm algorithm is used to search optimal...
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| Veröffentlicht in: | Automatic control and computer sciences 2020-09, Vol.54 (5), p.412-422 |
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| container_title | Automatic control and computer sciences |
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| creator | Jiabao Tao Zhu, Dunyao Sun, Chuan Chu, Duanfeng Ma, Yulin Li, Haibo Li, Yicheng Xu, Tingxuan |
| description | Aimed at improving SOC estimation accuracy, speed and robust of battery on electric vehicle, SOC estimation method based on adaptive particle filter is proposed. 1-order RC and lag model, 2-order RC and lag model, 3-order RC and lag model are built. Particle Swarm algorithm is used to search optimal parameters. Considering calculation and model accuracy, 1-order lag model is chosen. Traditional particle filter principle is analyzed. State estimation is a substitute to observation equation, and observation estimation is gotten. Observation noise variance is adjusted adaptively through observation error. Verification by simulation, convergence speed and robust of adaptive particle filter are superior to traditional algorithm when SOC original error is large. Besides, SOC estimation accuracy and stability is superior to traditional algorithm obviously. |
| doi_str_mv | 10.3103/S0146411620050089 |
| format | Article |
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Control Comp. Sci</addtitle><description>Aimed at improving SOC estimation accuracy, speed and robust of battery on electric vehicle, SOC estimation method based on adaptive particle filter is proposed. 1-order RC and lag model, 2-order RC and lag model, 3-order RC and lag model are built. Particle Swarm algorithm is used to search optimal parameters. Considering calculation and model accuracy, 1-order lag model is chosen. Traditional particle filter principle is analyzed. State estimation is a substitute to observation equation, and observation estimation is gotten. Observation noise variance is adjusted adaptively through observation error. Verification by simulation, convergence speed and robust of adaptive particle filter are superior to traditional algorithm when SOC original error is large. 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Control Comp. Sci</stitle><date>2020-09-01</date><risdate>2020</risdate><volume>54</volume><issue>5</issue><spage>412</spage><epage>422</epage><pages>412-422</pages><issn>0146-4116</issn><eissn>1558-108X</eissn><abstract>Aimed at improving SOC estimation accuracy, speed and robust of battery on electric vehicle, SOC estimation method based on adaptive particle filter is proposed. 1-order RC and lag model, 2-order RC and lag model, 3-order RC and lag model are built. Particle Swarm algorithm is used to search optimal parameters. Considering calculation and model accuracy, 1-order lag model is chosen. Traditional particle filter principle is analyzed. State estimation is a substitute to observation equation, and observation estimation is gotten. Observation noise variance is adjusted adaptively through observation error. Verification by simulation, convergence speed and robust of adaptive particle filter are superior to traditional algorithm when SOC original error is large. 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| ispartof | Automatic control and computer sciences, 2020-09, Vol.54 (5), p.412-422 |
| issn | 0146-4116 1558-108X |
| language | eng |
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| subjects | Accuracy Adaptive filters Algorithms Computer Science Control Structures and Microprogramming Electric vehicles Model accuracy Robustness State estimation |
| title | A Novel Method of SOC Estimation for Electric Vehicle Based on Adaptive Particle Filter |
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