Co-estimation of SOC and SOH for Li-ion battery based on MIEKPF-EKPF fusion algorithm
This paper uses the EKPF algorithm to directly measure the state of charge (SOC) and state of health (SOH) of Li-ion batteries and proposes a combination of multi-innovation-based extended Kalman particle filter (MIEKPF) and extended Kalman particle filter (EKPF) to estimate SOC. Firstly, the EKPF a...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-11-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484723015470 |
| _version_ | 1797378598362939392 |
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| author | Huan Zhou Jing Luo Zinbin Yu |
| author_facet | Huan Zhou Jing Luo Zinbin Yu |
| author_sort | Huan Zhou |
| collection | DOAJ |
| description | This paper uses the EKPF algorithm to directly measure the state of charge (SOC) and state of health (SOH) of Li-ion batteries and proposes a combination of multi-innovation-based extended Kalman particle filter (MIEKPF) and extended Kalman particle filter (EKPF) to estimate SOC. Firstly, the EKPF algorithm is applied to identify parameters and estimate SOH online, and the identification results of resistance and capacitance parameters are as input to compensate for the errors arising from considering the effects of battery aging in estimating SOC, thus improving the model accuracy. Secondly, the proposed fusion of multiple new interest discrimination theories and extended Kalman particle filtering algorithm, which takes into account the influence of past observations on the current value, enables the collaborative estimation of SOC and SOH over the whole Li-ion battery cycle. Finally, the MIEKPF-EKPF algorithm is compared with other existing algorithms to limit the average and maximum errors of SOC to 0.48% and 2%, respectively, during the New European Driving Cycle (NEDC) operating conditions. The simulation results verify the feasibility and accuracy of the proposed method. |
| first_indexed | 2024-03-08T20:10:04Z |
| format | Article |
| id | doaj.art-638c1166a8e94bba85eb3a60e7d666e7 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-03-08T20:10:04Z |
| publishDate | 2023-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-638c1166a8e94bba85eb3a60e7d666e72023-12-23T05:22:12ZengElsevierEnergy Reports2352-48472023-11-011044204428Co-estimation of SOC and SOH for Li-ion battery based on MIEKPF-EKPF fusion algorithmHuan Zhou0Jing Luo1Zinbin Yu2Academy of Electronic and Information Engineering, Jingchu University of Technology, Jingmen, 448000, ChinaAcademy of Electronic and Information Engineering, Jingchu University of Technology, Jingmen, 448000, China; Corresponding author.College of Electrical Engineering, Xinjiang University, Urumqi, 830017, ChinaThis paper uses the EKPF algorithm to directly measure the state of charge (SOC) and state of health (SOH) of Li-ion batteries and proposes a combination of multi-innovation-based extended Kalman particle filter (MIEKPF) and extended Kalman particle filter (EKPF) to estimate SOC. Firstly, the EKPF algorithm is applied to identify parameters and estimate SOH online, and the identification results of resistance and capacitance parameters are as input to compensate for the errors arising from considering the effects of battery aging in estimating SOC, thus improving the model accuracy. Secondly, the proposed fusion of multiple new interest discrimination theories and extended Kalman particle filtering algorithm, which takes into account the influence of past observations on the current value, enables the collaborative estimation of SOC and SOH over the whole Li-ion battery cycle. Finally, the MIEKPF-EKPF algorithm is compared with other existing algorithms to limit the average and maximum errors of SOC to 0.48% and 2%, respectively, during the New European Driving Cycle (NEDC) operating conditions. The simulation results verify the feasibility and accuracy of the proposed method.http://www.sciencedirect.com/science/article/pii/S2352484723015470State of chargeState of healthParticle filteringExtended Kalman filtering |
| spellingShingle | Huan Zhou Jing Luo Zinbin Yu Co-estimation of SOC and SOH for Li-ion battery based on MIEKPF-EKPF fusion algorithm Energy Reports State of charge State of health Particle filtering Extended Kalman filtering |
| title | Co-estimation of SOC and SOH for Li-ion battery based on MIEKPF-EKPF fusion algorithm |
| title_full | Co-estimation of SOC and SOH for Li-ion battery based on MIEKPF-EKPF fusion algorithm |
| title_fullStr | Co-estimation of SOC and SOH for Li-ion battery based on MIEKPF-EKPF fusion algorithm |
| title_full_unstemmed | Co-estimation of SOC and SOH for Li-ion battery based on MIEKPF-EKPF fusion algorithm |
| title_short | Co-estimation of SOC and SOH for Li-ion battery based on MIEKPF-EKPF fusion algorithm |
| title_sort | co estimation of soc and soh for li ion battery based on miekpf ekpf fusion algorithm |
| topic | State of charge State of health Particle filtering Extended Kalman filtering |
| url | http://www.sciencedirect.com/science/article/pii/S2352484723015470 |
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