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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.10 No.6
- 발행연도
- 2021.12
- 수록면
- 496 - 504 (9page)
- DOI
- 10.5573/IEIESPC.2021.10.6.496
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초록· 키워드
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Electric vehicles have high demand due to their ecofriendly nature. From this point of view, lithium batteries have gained high attention in recent days due to their high efficiency and long life time. Hence, it is of the utmost importance to evaluate the battery characteristics, such as the state of charge (SOC), depth of discharge (DOD), and remaining life of a battery to ensure battery safety. These parameters were derived in order to estimate the battery life time before degradation. This estimation is very much required in making a decision about battery usage in the future. In this study, the SOC of a lithium polymer battery was evaluated in a real-time experiment. Charging and discharging cycles were done, and we obtained the voltage, current, and time data from the experimental result. This experimental data trained machine learning methods such as the kNN (k Nearest Neighbor) method to estimate the SOC more precisely. After training the model, a test was done. The proposed estimator was calibrated by experimental data. The results are satisfactory with accuracy of 98% and mean absolute error (MAE) as low as 0.74[%].
목차
Abstract
1. Introduction
2. Analysis of Battery Model for Characteristic Evaluation
3. kNN Algorithm Design for SOC and DOD Model Training
4. Experimental Results
5. Conclusion
References
참고문헌 (16)
참고문헌 신청
Zhang, Wenqiang, et al. "A durable and safe solidstate lithium battery with a hybrid electrolyte membrane." Nano Energy 45 (2018): 413-419. Article(CrossRefLink)
Song, Xiangbao, et al. "Combined CNN-LSTM network for state-of-charge estimation of lithium-ion batteries." IEEE Access 7 (2019): 88894-88902. Article(CrossRefLink)
Chen, C.; Xiong, R.; Yang, R.; Shen, W.; Sun, F. State-of-charge estimation of lithium-ion battery using an improved neural network model and extended Kalman filter. J. Clean. Prod. 2019, 234, 1153–1164. Article(CrossRefLink)
Huang, Deyang, et al. "A model-based state-ofcharge estimation method for series-connected lithium-ion battery pack considering fast-varying cell temperature." Energy 185 (2019): 847-861. Article(CrossRef Link).
Abbas, G., Nawaz, M., & Kamran, F. (2019, January). Performance comparison of NARX & RNN-LSTM neural networks for LiFePO4 battery state of charge estimation. In 2019 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST) (pp. 463-468). IEEE. Article(CrossRef Link)
Song, Xiangbao, et al. "Combined CNN-LSTM network for state-of-charge estimation of lithium-ion batteries." IEEE Access 7 (2019): 88894-88902. Article(CrossRefLink)
Chen, C.; Xiong, R.; Yang, R.; Shen, W.; Sun, F. State-of-charge estimation of lithium-ion battery using an improved neural network model and extended Kalman filter. J. Clean. Prod. 2019, 234, 1153–1164. Article(CrossRefLink)
Huang, Deyang, et al. "A model-based state-ofcharge estimation method for series-connected lithium-ion battery pack considering fast-varying cell temperature." Energy 185 (2019): 847-861. Article(CrossRef Link).
Abbas, G., Nawaz, M., & Kamran, F. (2019, January). Performance comparison of NARX & RNN-LSTM neural networks for LiFePO4 battery state of charge estimation. In 2019 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST) (pp. 463-468). IEEE. Article(CrossRef Link)
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