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2025
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| Online Access: | https://doi.org/10.5281/zenodo.17630214 |
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| author | Monika Umesh Kumar Sinha |
| author_facet | Monika Umesh Kumar Sinha |
| contents | <p><span>Due to a variety of internal and external battery impacts, it is now more challenging to accurately estimate the<br>State of Charge (SOC) and State of Health (SOH) of lithium-ion batteries used in Electric Vehicles (EVs). The<br>paper's primary goal is to accurately predict the lithium-ion battery's SOC and SOH. The first-order RC<br>electrical equivalent circuit model is taken into consideration for analysis and modelling in this work. In order<br>to estimate the true parameters and internal states of the battery, the Adaptive Nonlinear Observer (ANO) is<br>developed to transform nonlinear equations into linearized equations. The transfer function is then derived<br>from the linearized equations. The suggested ANO provides superior dynamic results when compared to the<br>Extended Kalman Filter (EKF), and the convergence rate is examined using the linear criterion. The<br>MATLAB/Simulink platform has been used to validate the simulation findings of the suggested ANO.</span> </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17630214 |
| institution | Zenodo |
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| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | A Proposed Adaptive Nonlinear Observer Algorithm for Lithium-Ion Battery SOC and SOH Estimation for Electric Vehicles Monika Umesh Kumar Sinha <p><span>Due to a variety of internal and external battery impacts, it is now more challenging to accurately estimate the<br>State of Charge (SOC) and State of Health (SOH) of lithium-ion batteries used in Electric Vehicles (EVs). The<br>paper's primary goal is to accurately predict the lithium-ion battery's SOC and SOH. The first-order RC<br>electrical equivalent circuit model is taken into consideration for analysis and modelling in this work. In order<br>to estimate the true parameters and internal states of the battery, the Adaptive Nonlinear Observer (ANO) is<br>developed to transform nonlinear equations into linearized equations. The transfer function is then derived<br>from the linearized equations. The suggested ANO provides superior dynamic results when compared to the<br>Extended Kalman Filter (EKF), and the convergence rate is examined using the linear criterion. The<br>MATLAB/Simulink platform has been used to validate the simulation findings of the suggested ANO.</span> </p> |
| title | A Proposed Adaptive Nonlinear Observer Algorithm for Lithium-Ion Battery SOC and SOH Estimation for Electric Vehicles |
| url | https://doi.org/10.5281/zenodo.17630214 |