This paper presents a lithium-ion battery state-of-health (SOH) estimation method based on a long short-term memory neural network. The proposed algorithm uses multi-input for the current state of the battery, which are voltage, current, temperature and state-of-charge (SOC). In addition, it reflects the historical state of the battery such as difference of current and temperature by varying the input sequence length, for a more accurate SOH estimation. To verify the proposed algorithm, the accuracy value when using the error function is checked. Then, the algorithm selects the appropriate sequence length and analyzes the system. The system improves estimation accuracy through the feedback process. Furthermore, the proposed method is compared to other learning methods to support the validation. The results highlight the selection of the appropriate sequence length can improve the accuracy of the estimation and show that the battery SOH estimation with the optimal sequence length is great effect on the performance through RMSE 0.1425, MAE 0.1084, and MAPE 0.8658%.
|Title of host publication||2020 IEEE 29th International Symposium on Industrial Electronics, ISIE 2020 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|Publication status||Published - 2020 Jun|
|Event||29th IEEE International Symposium on Industrial Electronics, ISIE 2020 - Delft, Netherlands|
Duration: 2020 Jun 17 → 2020 Jun 19
|Name||IEEE International Symposium on Industrial Electronics|
|Conference||29th IEEE International Symposium on Industrial Electronics, ISIE 2020|
|Period||20/6/17 → 20/6/19|
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science, the framework of international cooperation program managed by NRF of Korea #NRF-2017K1A4A3013579. This research was also supported by Korea Electric Power Corporation (KEPCO) #R18XA05.
© 2020 IEEE.
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
- Control and Systems Engineering