Low Temperature, Current Dependent Battery State Estimation Using Interacting Multiple Model Strategy
Lithium-ion battery State of Charge (SoC) estimation for Electric Vehicle (EV) applications must be robust and as accurate as possible to maximize battery utilization and ensure safe operation over a wide range of operating conditions. SoC estimation commonly utilizes filters such as the Extended Ka...
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2021-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/9478786/ |
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doaj-8229aa611d7644d6b18a7424a47b4d742021-07-20T23:00:21ZengIEEEIEEE Access2169-35362021-01-019998769988910.1109/ACCESS.2021.30959389478786Low Temperature, Current Dependent Battery State Estimation Using Interacting Multiple Model StrategyMarvin Messing0https://orcid.org/0000-0002-3844-5852Sara Rahimifard1https://orcid.org/0000-0002-2393-2017Tina Shoa2Saeid Habibi3Department of Mechanical Engineering, McMaster University, Hamilton, ON, CanadaDepartment of Mechanical Engineering, McMaster University, Hamilton, ON, CanadaCadex Electronics Inc., Richmond, BC, CanadaDepartment of Mechanical Engineering, McMaster University, Hamilton, ON, CanadaLithium-ion battery State of Charge (SoC) estimation for Electric Vehicle (EV) applications must be robust and as accurate as possible to maximize battery utilization and ensure safe operation over a wide range of operating conditions. SoC estimation commonly utilizes filters such as the Extended Kalman Filter (EKF) which rely on battery models, usually in the form of Equivalent Circuit Models (ECM). At low temperatures the battery response to current draw becomes increasingly non-linear, resulting in amplified SoC estimation errors. In this study, current dependent SoC estimation at low temperature is proposed using an Interacting Multiple Model (IMM) filter with three ECMs covering a range of C-rates. The IMM is combined with the Smooth Variable Structure Filter (SVSF) to obtain robust SoC estimates within a SoC estimation error of 2%.https://ieeexplore.ieee.org/document/9478786/Lithium-ion batterystate estimationinteracting multiple model filtersmooth variable structure filterlow temperature |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Marvin Messing Sara Rahimifard Tina Shoa Saeid Habibi |
| spellingShingle |
Marvin Messing Sara Rahimifard Tina Shoa Saeid Habibi Low Temperature, Current Dependent Battery State Estimation Using Interacting Multiple Model Strategy IEEE Access Lithium-ion battery state estimation interacting multiple model filter smooth variable structure filter low temperature |
| author_facet |
Marvin Messing Sara Rahimifard Tina Shoa Saeid Habibi |
| author_sort |
Marvin Messing |
| title |
Low Temperature, Current Dependent Battery State Estimation Using Interacting Multiple Model Strategy |
| title_short |
Low Temperature, Current Dependent Battery State Estimation Using Interacting Multiple Model Strategy |
| title_full |
Low Temperature, Current Dependent Battery State Estimation Using Interacting Multiple Model Strategy |
| title_fullStr |
Low Temperature, Current Dependent Battery State Estimation Using Interacting Multiple Model Strategy |
| title_full_unstemmed |
Low Temperature, Current Dependent Battery State Estimation Using Interacting Multiple Model Strategy |
| title_sort |
low temperature, current dependent battery state estimation using interacting multiple model strategy |
| publisher |
IEEE |
| series |
IEEE Access |
| issn |
2169-3536 |
| publishDate |
2021-01-01 |
| description |
Lithium-ion battery State of Charge (SoC) estimation for Electric Vehicle (EV) applications must be robust and as accurate as possible to maximize battery utilization and ensure safe operation over a wide range of operating conditions. SoC estimation commonly utilizes filters such as the Extended Kalman Filter (EKF) which rely on battery models, usually in the form of Equivalent Circuit Models (ECM). At low temperatures the battery response to current draw becomes increasingly non-linear, resulting in amplified SoC estimation errors. In this study, current dependent SoC estimation at low temperature is proposed using an Interacting Multiple Model (IMM) filter with three ECMs covering a range of C-rates. The IMM is combined with the Smooth Variable Structure Filter (SVSF) to obtain robust SoC estimates within a SoC estimation error of 2%. |
| topic |
Lithium-ion battery state estimation interacting multiple model filter smooth variable structure filter low temperature |
| url |
https://ieeexplore.ieee.org/document/9478786/ |
| work_keys_str_mv |
AT marvinmessing lowtemperaturecurrentdependentbatterystateestimationusinginteractingmultiplemodelstrategy AT sararahimifard lowtemperaturecurrentdependentbatterystateestimationusinginteractingmultiplemodelstrategy AT tinashoa lowtemperaturecurrentdependentbatterystateestimationusinginteractingmultiplemodelstrategy AT saeidhabibi lowtemperaturecurrentdependentbatterystateestimationusinginteractingmultiplemodelstrategy |
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