IMPROVED ALGORITHM BASED ON AEKF FOR STATE OF CHARGE ESTIMATION OF LITHIUM-ION BATTERY

다국어 초록 (Multilingual Abstract)

State of charge (SOC) is one of the most significant parameters in the battery management system (BMS).
Accurate estimate of the SOC can prevent the battery overcharge and over-discharge, which can effectively increase the life of the Lithium-ion battery and improve the safety of electric vehicle. In this paper, an improved second-order equivalent model is established. The improved model distinguishes the direction of charge and discharge for the resistance and capacitance parameters. To improve the estimation accuracy of SOC, this paper proposes an improved Adaptive Extended Kalman Filter by introducing an iterative method into the AEKF algorithm. The improved algorithm mainly uses the measured voltage data to adjust the covariance matrix P multiple times in one calculation step to reduce the error in the linearization process. The dynamic stress test (DST) and urban dynamometer driving schedule (UDDS) are applied to verify the validity and accuracy of the improved algorithm. The experimental results show that the algorithm proposed in this paper has faster convergence and more accurate compared with AEKF algorithms.

참고문헌 (Reference)

1 Li, L. L., "The open-circuit voltage characteristic and state of charge estimation for lithium-ion batteries based on an improved estimation algorithm" 48 (48): 1712-1730, 2020

2 Li, B., "State-of-charge estimation for lithium-ion battery using the Gauss-Hermite particle filter technique" 10 (10): 014105-, 2018

3 Yuan, S. F., "State of charge estimation using the extended kalman filter for battery management systems based on the ARX battery model" 6 (6): 444-470, 2013

4 Kim, M., "Reliable online parameter identification of li-ion batteries in battery management systems using the condition number of the error covariance matrix" 8 : 189106-189114, 2020

5 Dang, X., "Open-circuit voltage-based state of charge estimation of lithium-ion battery using dual neural network fusion battery model" 188 : 356-366, 2016

6 Cheng, C., "Neural network-based direct adaptive robust control of unknown MIMO nonlinear systems using state observer" 34 (34): 1-14, 2020

7 Chen, X. K, "Modeling and state of charge estimation of lithium-ion battery" 3 (3): 202-211, 2015

8 Xu, Z., "LiFePO4 battery state of charge estimation based on the improved Thevenin equivalent circuit model and Kalman filtering" 8 (8): 024103-, 2016

9 Zheng, Y., "Investigating the error sources of the online state of charge estimation methods for lithium-ion batteries in electric vehicles" 377 : 161-188, 2018

10 Xiong, R., "Evaluation on state of charge estimation of batteries with adaptive extended kalman filter by experiment approach" 62 (62): 108-117, 2013

11 Li, B, "Estimation algorithm research for lithium battery SOC in electric vehicles based on adaptive unscented Kalman filter" 31 (31): 8171-8183, 2019

12 Chaoui, H., "Aging prediction and state of charge estimation of a LiFePO4 battery using input time-delayed neural networks" 146 : 189-197, 2017

13 Liu, Y, "Adaptive sigma Kalman filter method for state-of-charge estimation based on the optimized battery model" 9 (9): 044101-, 2017

14 Nejad, S., "A systematic review of lumped-parameter equivalent circuit models for real-time estimation of lithium-ion battery states" 316 : 183-196, 2016

15 Esfandyari, M. J., "A hybrid model predictive and fuzzy logic based control method for state of power estimation of series-connected Lithium-ion batteries in HEVs" 24 : 100758-, 2019

16 Wang, Y, "A framework for state-ofcharge and remaining discharge time prediction using unscented particle filter" 260 : 114324-, 2020

17 Wang, Y., "A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems" 131 : 110015-, 2020