Bias-Compensated State of Charge and State of Health Joint Estimation for Lithium Iron Phosphate Batteries
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917611120361472 |
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| author | Yi, Baozhao Du, Xinhao Zhang, Jiawei Wu, Xiaogang Hu, Qiuhao Jiang, Weiran Hu, Xiaosong Song, Ziyou |
| author_facet | Yi, Baozhao Du, Xinhao Zhang, Jiawei Wu, Xiaogang Hu, Qiuhao Jiang, Weiran Hu, Xiaosong Song, Ziyou |
| contents | Accurate estimation of the state of charge (SOC) and state of health (SOH) is crucial for the safe and reliable operation of batteries. Voltage measurement bias highly affects state estimation accuracy, especially in Lithium Iron Phosphate (LFP) batteries, which are susceptible due to their flat open-circuit voltage (OCV) curves. This work introduces a bias-compensated algorithm to reliably estimate the SOC and SOH of LFP batteries under the influence of voltage measurement bias. Specifically, SOC and SOH are estimated using the Dual Extended Kalman Filter (DEKF) in the high-slope SOC range, where voltage measurement bias effects are weak. Besides, the voltage measurement biases estimated in the low-slope SOC regions are compensated in the following joint estimation of SOC and SOH to enhance the state estimation accuracy further. Experimental results indicate that the proposed algorithm significantly outperforms the traditional method, which does not consider biases under different temperatures and aging conditions. Additionally, the bias-compensated algorithm can achieve low estimation errors of below 1.5% for SOC and 2% for SOH, even with a 30mV voltage measurement bias. Finally, even if the voltage measurement biases change in operation, the proposed algorithm can remain robust and keep the estimated errors of states around 2%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_08136 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Bias-Compensated State of Charge and State of Health Joint Estimation for Lithium Iron Phosphate Batteries Yi, Baozhao Du, Xinhao Zhang, Jiawei Wu, Xiaogang Hu, Qiuhao Jiang, Weiran Hu, Xiaosong Song, Ziyou Systems and Control Accurate estimation of the state of charge (SOC) and state of health (SOH) is crucial for the safe and reliable operation of batteries. Voltage measurement bias highly affects state estimation accuracy, especially in Lithium Iron Phosphate (LFP) batteries, which are susceptible due to their flat open-circuit voltage (OCV) curves. This work introduces a bias-compensated algorithm to reliably estimate the SOC and SOH of LFP batteries under the influence of voltage measurement bias. Specifically, SOC and SOH are estimated using the Dual Extended Kalman Filter (DEKF) in the high-slope SOC range, where voltage measurement bias effects are weak. Besides, the voltage measurement biases estimated in the low-slope SOC regions are compensated in the following joint estimation of SOC and SOH to enhance the state estimation accuracy further. Experimental results indicate that the proposed algorithm significantly outperforms the traditional method, which does not consider biases under different temperatures and aging conditions. Additionally, the bias-compensated algorithm can achieve low estimation errors of below 1.5% for SOC and 2% for SOH, even with a 30mV voltage measurement bias. Finally, even if the voltage measurement biases change in operation, the proposed algorithm can remain robust and keep the estimated errors of states around 2%. |
| title | Bias-Compensated State of Charge and State of Health Joint Estimation for Lithium Iron Phosphate Batteries |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2401.08136 |