Abstract
References
- Agudelo, B. O., Zamboni, W., Postiglione, F., & Monmasson, E. (2023). Battery state-of-health estimation based on multiple charge and discharge features. Energy, 263, 125637.
- Celik B., Sandt R., dos Santos L.C.P., & Spatschek R. (2022) Prediction of battery cycle life using early-cycle data, machine learning and data management. Batteries 2022. 8(12), 266.
- Chang, C., Wang, Q., Jiang, J., & Wu, T. (2021). Lithium-ion battery state of health estimation using the incremental capacity and wavelet neural networks with genetic algorithm. Journal of Energy Storage, 38, 102570.
- Chen, M., Ma, G., Liu, W., Zeng, N., & Luo, X. (2023). An overview of data-driven battery health estimation technology for battery management system. Neurocomputing, 532(1), 152-169.
- Cheng, D., Sha, W., Wang, L., Tang, S., Aijun, M., Chen, Y., Wang, H., Lou, P., Lu, S., & Cao, Y. (2021). Solid-state lithium battery cycle life prediction using machine learning. Applied Science, 11, 4671.
Abstract
As an energy storage system, Li-Ion batteries have many applications from mobile devices to vehicles. No matter what application they are used in, Li-Ion batteries lose performance over time, and this negatively affects the user experience in terms of both comfort and safety. For this reason, it is extremely important to estimate state of health (SOH) of Li-Ion batteries and to use the batteries accordingly. In this study, examinations on the SOH estimation of batteries with different machine learning (ML) methods are included using Constant Current (CC) and Constant Voltage (CV) charge-discharge characteristics of the li-Ion batteries. Moreover, how the estimation performance changes by both short-term and long-term features is observed by using mutual information metric. According to results, the highest accuracy on SOH estimation is achieved when long-term features are used with Bayesian Ridge Regression. When the short-term features are used, the accuracy of Bayesian Ridge Regression is dramatically reduced, and Random Forest Regression provides highest performance.
References
- Agudelo, B. O., Zamboni, W., Postiglione, F., & Monmasson, E. (2023). Battery state-of-health estimation based on multiple charge and discharge features. Energy, 263, 125637.
- Celik B., Sandt R., dos Santos L.C.P., & Spatschek R. (2022) Prediction of battery cycle life using early-cycle data, machine learning and data management. Batteries 2022. 8(12), 266.
- Chang, C., Wang, Q., Jiang, J., & Wu, T. (2021). Lithium-ion battery state of health estimation using the incremental capacity and wavelet neural networks with genetic algorithm. Journal of Energy Storage, 38, 102570.
- Chen, M., Ma, G., Liu, W., Zeng, N., & Luo, X. (2023). An overview of data-driven battery health estimation technology for battery management system. Neurocomputing, 532(1), 152-169.
- Cheng, D., Sha, W., Wang, L., Tang, S., Aijun, M., Chen, Y., Wang, H., Lou, P., Lu, S., & Cao, Y. (2021). Solid-state lithium battery cycle life prediction using machine learning. Applied Science, 11, 4671.
Details
| Primary Language | English |
|---|---|
| Subjects | Computer Software |
| Journal Section | Articles |
| Authors | |
| Early Pub Date | August 8, 2023 |
| Publication Date | September 1, 2023 |
| Published in Issue | Year 2023Volume: 22 |
