Adaptive Smooth Variable Structure Filter Strategy for State Estimation of Electric Vehicle Batteries
Battery Management Systems (BMSs) are used to manage the utilization of batteries and their operation in Electric and Hybrid Vehicles. It is imperative for efficient and safe operation of batteries to be able to accurately estimate the State of Charge (SoC), State of Health (SoH) and State of Power...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/24/8560 |
| _version_ | 1797505004061327360 |
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| author | Sara Rahimifard Saeid Habibi Gillian Goward Jimi Tjong |
| author_facet | Sara Rahimifard Saeid Habibi Gillian Goward Jimi Tjong |
| author_sort | Sara Rahimifard |
| collection | DOAJ |
| description | Battery Management Systems (BMSs) are used to manage the utilization of batteries and their operation in Electric and Hybrid Vehicles. It is imperative for efficient and safe operation of batteries to be able to accurately estimate the State of Charge (SoC), State of Health (SoH) and State of Power (SoP). The SoC and SoH estimation must remain robust and accurate despite aging and in presence of noise, uncertainties and sensor biases. This paper introduces a robust adaptive filter referred to as the Adaptive Smooth Variable Structure Filter with a time-varying Boundary Layer (ASVSF-VBL) for the estimation of the SoC and SoH in electrified vehicles. The internal model of the filter is a third-order equivalent circuit model (ECM) and its state vector is augmented to enable estimation of the internal resistance and current bias. It is shown that system and measurement noise covariance adaptation for the SVSF-VBL approach improves the performance in state estimation of a battery. The estimated internal resistance is then utilized to improve determination of the battery’s SoH. The effectiveness of the proposed method is validated using experimental data from tests on Lithium Polymer automotive batteries. The results indicate that the SoC estimation error can remain within less than <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>2</mn><mo>%</mo></mrow></semantics></math></inline-formula> over the full operating range of SoC along with an accurate estimation of SoH. |
| first_indexed | 2024-03-10T04:12:29Z |
| format | Article |
| id | doaj.art-c334947b08e84ed9bdf15358647fa94d |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T04:12:29Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-c334947b08e84ed9bdf15358647fa94d2023-11-23T08:09:00ZengMDPI AGEnergies1996-10732021-12-011424856010.3390/en14248560Adaptive Smooth Variable Structure Filter Strategy for State Estimation of Electric Vehicle BatteriesSara Rahimifard0Saeid Habibi1Gillian Goward2Jimi Tjong3Department of Mechanical Engineering, McMaster University, Hamilton, ON L8S 4L8, CanadaDepartment of Mechanical Engineering, McMaster University, Hamilton, ON L8S 4L8, CanadaDepartment of Chemistry and Chemical Biology, McMaster University, Hamilton, ON L8S 4L8, CanadaDepartment of Mechanical Engineering, McMaster University, Hamilton, ON L8S 4L8, CanadaBattery Management Systems (BMSs) are used to manage the utilization of batteries and their operation in Electric and Hybrid Vehicles. It is imperative for efficient and safe operation of batteries to be able to accurately estimate the State of Charge (SoC), State of Health (SoH) and State of Power (SoP). The SoC and SoH estimation must remain robust and accurate despite aging and in presence of noise, uncertainties and sensor biases. This paper introduces a robust adaptive filter referred to as the Adaptive Smooth Variable Structure Filter with a time-varying Boundary Layer (ASVSF-VBL) for the estimation of the SoC and SoH in electrified vehicles. The internal model of the filter is a third-order equivalent circuit model (ECM) and its state vector is augmented to enable estimation of the internal resistance and current bias. It is shown that system and measurement noise covariance adaptation for the SVSF-VBL approach improves the performance in state estimation of a battery. The estimated internal resistance is then utilized to improve determination of the battery’s SoH. The effectiveness of the proposed method is validated using experimental data from tests on Lithium Polymer automotive batteries. The results indicate that the SoC estimation error can remain within less than <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>2</mn><mo>%</mo></mrow></semantics></math></inline-formula> over the full operating range of SoC along with an accurate estimation of SoH.https://www.mdpi.com/1996-1073/14/24/8560electric vehiclelithium-ion batterybattery management systemadaptive smooth variable structure filterstate of chargestate of health |
| spellingShingle | Sara Rahimifard Saeid Habibi Gillian Goward Jimi Tjong Adaptive Smooth Variable Structure Filter Strategy for State Estimation of Electric Vehicle Batteries Energies electric vehicle lithium-ion battery battery management system adaptive smooth variable structure filter state of charge state of health |
| title | Adaptive Smooth Variable Structure Filter Strategy for State Estimation of Electric Vehicle Batteries |
| title_full | Adaptive Smooth Variable Structure Filter Strategy for State Estimation of Electric Vehicle Batteries |
| title_fullStr | Adaptive Smooth Variable Structure Filter Strategy for State Estimation of Electric Vehicle Batteries |
| title_full_unstemmed | Adaptive Smooth Variable Structure Filter Strategy for State Estimation of Electric Vehicle Batteries |
| title_short | Adaptive Smooth Variable Structure Filter Strategy for State Estimation of Electric Vehicle Batteries |
| title_sort | adaptive smooth variable structure filter strategy for state estimation of electric vehicle batteries |
| topic | electric vehicle lithium-ion battery battery management system adaptive smooth variable structure filter state of charge state of health |
| url | https://www.mdpi.com/1996-1073/14/24/8560 |
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