Abstract
Lithium-ion batteries are an important component in electric vehicles (EVs), and ensuring their optimal performance is vital for the longevity and efficiency of the vehicle. Efficient testing of battery cells plays a major role in verifying their performance. Traditionally, a single-cell testing unit is used for batch processing during battery pack assembly. However, this method is time-consuming and lacks scalability, which limits productivity. To address this, the research proposes a multicell testing approach that concurrently estimates the state of charge (SOC) and state of health (SOH) of multiple battery cells in parallel. By doing so, the approach significantly reduces testing time and improves efficiency. The dual filter concept is incorporated to categorize cells based on their performance, ensuring only high- quality cells are selected for inclusion in the battery pack. Furthermore, a custom Temporal Convolutional Network (TCN) model, achieving an accuracy of 89%, is employed to accurately estimate SOC and SOH. In addition, a predictive battery temperature forecasting model is introduced to forecast the temperature of the battery cells over the next three days, which aids in proactive temperature management and prevents potential degradation. Overall, the proposed approach enhances battery testing productivity and ensures higher accuracy in SOC and SOH estimation, contributing to the development of more reliable and efficient EV batteries.
References
- R. Bayir and E. Soylu, “Bayir, Raif and Emel Soylu. “Real Time Determination of Rechargeable Batteries’ Type and the State of Charge via Cascade Correlation Neural Network.” Elektronika Ir Elektrotechnika 24 (2018): 25-30.
- G. Nobile, M. Cacciato, G. Scarcella, and G. Scelba, “Multi-criteria experimental comparison of batteries circuital models for automotive applications”, Communications, vol. 20, no. 1, 2018. 97–104.
- Fang, Yu, Rui Xiong and Jun Shan Wang. “Estimation of Lithium-Ion Battery State of Charge for Electric Vehicles Based on Dual Extended Kalman Filter.” Energy Procedia (2018):
- Li, Ning, Yu Hui Zhang, Fuxing He, Long Long Zhu, Xiaoping Zhang, Yonghui Ma and Shun-Ting Wang. “Review of lithium-ion battery state of charge estimation.” Global Energy Interconnection (2021)
- Lin, Qizhe, Xiaoqi Li, Bicheng Tu, Junwei Cao, Ming Zhang and Jiawei Xiang. “Stable and Accurate Estimation of SOC Using eXogenous Kalman Filter for Lithium-Ion Batteries.” Sensors (Basel, Switzerland) 23 (2023):
- Hannan, Mahammad Abdul, Molla Shahadat Hossain Lipu, Aini Hussain and Azah Mohamed. “A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations.” Renewable & Sustainable Energy Reviews 78 (2017): 834-854.
- Ali, Muhammad Umair, Amad Zafar, Sarvar Hussain Nengroo, Sadam Hussain, Muhammad Junaid Alvi and Hee-Je Kim. “Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation.” Energies (2019):
- Zhao, Feng, Yun Guo and Baoming Chen. “A Review of Lithium-Ion Battery State of Charge Estimation Methods Based on Machine Learning.” World Electric Vehicle Journal (2024)
- Liu, Yuefeng, Yingjie He, Haodong Bian, Wei Guo and Xiaoyan Zhang. “A review of lithium-ion battery state of charge estimation based on deep learning: Directions for improvement and future trends.” Journal of Energy Storage (2022)
- Arandhakar, Sairaj and Jayaram Nakka. “State of charge estimation of lithium ion battery for electric vehicle using cutting edge machine learning algorithms: A review.” Journal of Energy Storage (2024)