Study of SOC Estimation by the Ampere-Hour Integral Method with Capacity Correction Based on LSTM
Source abstract
Abstract
The estimation of the state of charge (SOC) of a battery’s power is one of the key technologies in a battery management system (BMS). As a common SOC estimation method, the traditional ampere-hour integral method regards the actual capacity of the battery, which is constantly changed by the usage conditions and environment, as a constant for calculation, which may cause errors in the results of SOC estimation. Considering the above problems, this paper proposes an improved ampere-hour integral method based on the Long Short-Term Memory (LSTM) network model. The LSTM network model is used to obtain the actual battery capacity variation, replacing the fixed value of battery capacity in the traditional ampere-hour integral method and optimizing the traditional ampere-hour integral method to improve the accuracy of the SOC estimation method. The experimental results show that the errors of the results obtained by the improved ampere-hour integral method for the SOC estimation are all less than 10%, which proves that the proposed design method is feasible and effective.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Battery Technologies Research · IoT-based Smart Home Systems · Electric Vehicles and Infrastructure
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Automobile and Traffic, Shenyang Ligong University, Shenyang 110159, China
School of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, China
Funding
Liaoning Province Basic Research Projects of Higher Education Institutions · the construction plan of scientific research and innovation team of Shenyang Ligong University · Comprehensive reform project of graduate education of Shenyang Ligong University
References (19)
Show source-provided references
1. A brief review on key technologies in the battery management system of electric vehicles10.1007/s11465-018-0516-8 ↗
2. Research on SOC estimation method of ampere-time integral based on capacity correction
3. Yang, Q., Ma, K., Xu, L., Song, L., Li, X., and Li, Y. (2022). A Joint Estimation Method Based on Kalman Filter of Battery State of Charge and State of Health. Coatings, 12.10.3390/coatings12081047 ↗
4. Joint estimation of SOC and SOH for lithium-ion batteries based on deep learning
5. Ahmed, M.S., and Balasingam, B. (2019, January 16–18). A scaling approach for improved open circuit voltage modeling in Li-ion batteries. Proceedings of the 2019 IEEE Electrical Power and Energy Conference (EPEC), Montréal, QC, Canada.10.1109/EPEC47565.2019.9074789 ↗
6. A noise-tolerant model parameterization method for lithium-ion battery management system10.1016/j.apenergy.2020.114932 ↗
7. Jin, G., Li, L., Xu, Y., Hu, M., Fu, C., and Qin, D. (2020). Comparison of SOC Estimation between the Integer-Order Model and Fractional-Order Model Under Different Operating Conditions. Energies, 13.10.3390/en13071785 ↗
8. Hou, E., Xu, Y., Qiao, X., Liu, G., and Wang, Z. (2021). State of power estimation of echelon-use battery based on adaptive dual extended Kalman filter. Energies, 14.10.3390/en14175579 ↗
9. Zhou, W., Ma, X., Wang, H., and Zheng, Y. (2022). SOC Estimation Based on Hysteresis Characteristics of Lithium Iron Phosphate Battery. Machines, 10.10.3390/machines10080658 ↗
10. Qiao, X., Wang, Z., Hou, E., Liu, G., and Cai, Y. (2022). Online Estimation of Open Circuit Voltage Based on Extended Kalman Filter with Self-Evaluation Criterion. Energies, 15.10.3390/en15124373 ↗
11. Research on SOC estimation method for lithium-ion batteries based on ampere-time integral and traceless Kalman filter
12. Research on SOC estimation algorithm of sliding mode observer with joint extended Kalman filter
13. Pham, N.N., Leuchter, J., Pham, K.L., and Dong, Q.H. (2022). Battery Management System for Unmanned Electric Vehicles with CAN BUS and Internet of Things. Vehicles, 4.10.3390/vehicles4030037 ↗
14. Liu, Z., Li, Z., Zhang, J., Su, L., and Ge, H. (2019). Accurate and Efficient Estimation of Lithium-Ion Battery State of Charge with Alternate Adaptive Extended Kalman Filter and Ampere-Hour Counting Methods. Energies, 12.10.3390/en12040757 ↗
15. Jafari, S., Shahbazi, Z., Byun, Y.-C., and Lee, S.-J. (2022). Lithium-Ion Battery Estimation in Online Framework Using Extreme Gradient Boosting Machine Learning Approach. Mathematics, 10.10.3390/math10060888 ↗
16. A comparative study of equivalent circuit models for electric vehicle battery power input
17. Generalised diagnostic framework for rapid battery degradation quantification with deep learning10.1016/j.egyai.2022.100158 ↗
18. Shankar, K., Kumar, S., Dutta, A.K., Alkhayyat, A., Jawad, A.J.M., Abbas, A.H., and Yousif, Y.K. (2022). An Automated Hyperparameter Tuning Recurrent Neural Network Model for Fruit Classification. Mathematics, 10.10.3390/math10132358 ↗
19. Feng, Z., Hu, P., Li, S., and Mo, D. (2020). Prediction of Significant Wave Height in Offshore China Based on the Machine Learning Method. J. Mar. Sci. Eng., 10.10.3390/jmse10060836 ↗