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Bias-Compensated State of Charge and State of Health Joint Estimation for Lithium Iron Phosphate Batteries

Baozhao Yi, Xinhao Du, Jiawei Zhang, Xiaogang Wu, Qiuhao Hu, Weiran Jiang, Xiaosong Hu, Ziyou Song

Abstract

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%.

Bias-Compensated State of Charge and State of Health Joint Estimation for Lithium Iron Phosphate Batteries

Abstract

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%.
Paper Structure (11 sections, 29 equations, 7 figures, 3 tables)

This paper contains 11 sections, 29 equations, 7 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Battery system modeling
  3. Battery system description
  4. The analysis of battery system dynamics
  5. The Bias-compensated algorithm for joint estimation of SOC and SOH
  6. The effect of voltage measurement bias on SOC estimation
  7. The framework of the bias-compensated algorithm
  8. Experimental results
  9. Experimental setup
  10. Results and discussion
  11. Conclusions

Figures (7)

  • Figure 1: The first-order equivalent circuit model.
  • Figure 2: The framework of the bias-compensated algorithm.
  • Figure 3: A piece of experimental data used for the estimation process.
  • Figure 4: Estimated results of SOC after adding voltage bias of 10mV. (a) Cell 1 at $\rm 25^\circ C$. (b) Cell 2 at $\rm 25^\circ C$. (c) Cell 1 at $\rm 5^\circ C$. (d) Cell 2 at $\rm 5^\circ C$.
  • Figure 5: Estimated errors of SOC and capacity in different stages after adding 10mV bias. (a) Cell 1 at $\rm 25^\circ C$. (b) Cell 2 at $\rm 25^\circ C$. (c) Cell 1 at $\rm 5^\circ C$. (d) Cell 2 at $\rm 5^\circ C$.
  • ...and 2 more figures