스마트미디어저널 (Smart Media Journal)

한국스마트미디어학회 (THE KOREAN INSTITUTE OF SMART MEDIA)
권호 표지
DOI QR코드

DOI QR Code

Data-Driven Approach for Lithium-Ion Battery Remaining Useful Life Prediction: A Literature Review

  • Luon Tran, Van (Department of Artificial Intelligence Convergence, Chonnam National University) ;
  • Lam Tran, Ha (Kongju National University) ;
  • Deokjai, Choi (Department of Electronic and Computer Engineering, Chonnam National University)
  • 투고 : 2022.10.11
  • 심사 : 2022.12.12
  • 발행 : 2022.12.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Nowadays, lithium-ion battery has become more popular around the world. Knowing when batteries reach their end of life (EOL) is crucial. Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is needed for battery health management systems and to avoid unexpected accidents. It gives information about the battery status and when we should replace the battery. With the rapid growth of machine learning and deep learning, data-driven approaches are proposed to address this problem. Extracting aging information from battery charge/discharge records, including voltage, current, and temperature, can determine the battery state and predict battery RUL. In this work, we first outlined the charging and discharging processes of lithium-ion batteries. We then summarize the proposed techniques and achievements in all published data-driven RUL prediction studies. From that, we give a discussion about the accomplishments and remaining works with the corresponding challenges in order to provide a direction for further research in this area.

키워드

과제정보

This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the Innovative Human Resource Development for Local Intellectualization support program (IITP-2022-RS-2022-00156287) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation).

참고문헌

  1. Xiong, Rui, Yongzhi Zhang, Ju Wang, Hongwen He, Simin Peng, and Michael Pecht. "Lithium-ion battery health prognosis based on a real battery management system used in electric vehicles," IEEE Transactions on Vehicular Technology, Vol. 68, No. 5, pp. 4110-4121, May, 2019.  https://doi.org/10.1109/tvt.2018.2864688
  2. Voelker, Paul, and T. F. Scientific. "Trace degradation analysis of lithium-ion battery components," R&D Magazinde,(Apr. 2014) http://www. rdmag.com/articles/2014/04/trace-degradation-analysislithium-ion-battery-components Search PubMed,(accessed August 2015). 
  3. Hu, Xiaosong, Dongpu Cao, and Bo Egardt. "Condition monitoring in advanced battery management systems: Moving horizon estimation using a reduced electrochemical model," IEEE/ASME Transactions on Mechatronics, Vol. 23, No. 1, pp. 167-178. Mar. 2017.  https://doi.org/10.1109/tmech.2017.2675920
  4. Xu, Xin, and Nan Chen. "A state-space-based prognostics model for lithium-ion battery degradation," Reliability Engineering & System Safety, Vol. 159, pp. 47-57, Mar. 2017.  https://doi.org/10.1016/j.ress.2016.10.026
  5. Virkar, Anil V. "A model for degradation of electrochemical devices based on linear non-equilibrium thermodynamics and its application to lithium ion batteries," Journal of Power Sources, Vol. 196, No. 14, pp. 5970-5984, Jul. 2011.  https://doi.org/10.1016/j.jpowsour.2011.03.005
  6. Ren, Lei, Li Zhao, Sheng Hong, Shiqiang Zhao, Hao Wang, and Lin Zhang. "Remaining useful life prediction for lithium-ion battery: A deep learning approach," Ieee Access, Vol. 6, pp. 50587-50598, Jul. 2018.  https://doi.org/10.1109/access.2018.2858856
  7. Ren, Lei, Jiabao Dong, Xiaokang Wang, Zihao Meng, Li Zhao, and M. Jamal Deen. "A data-driven auto-cnn-lstm prediction model for lithium-ion battery remaining useful life," IEEE Transactions on Industrial Informatics, Vol. 17, No. 5, pp. 3478-3487, Jul. 2020. 
  8. Chinomona, Benvolence, Chunhui Chung, Lien-Kai Chang, Wei-Chih Su, and Mi-Ching Tsai, "Long short-term memory approach to estimate battery remaining useful life using partial data," Ieee Access, Vol. 8, pp. 165419-165431, Sep. 2020.  https://doi.org/10.1109/access.2020.3022505
  9. Park, Kyungnam, Yohwan Choi, Won Jae Choi, Hee-Yeon Ryu, and Hongseok Kim, "LSTM-based battery remaining useful life prediction with multi-channel charging profiles," Ieee Access Vol. 8, pp. 20786-20798, Jan. 2020.  https://doi.org/10.1109/access.2020.2968939
  10. Afshari, Sajad Saraygord, Shihao Cui, Xiangyang Xu, and Xihui Liang, "Remaining useful life early prediction of batteries based on the differential voltage and differential capacity curves," IEEE Transactions on Instrumentation and Measurement, Vol. 71, pp. 1-9, Oct. 2021. 
  11. Lee, Seongjun, Jonghoon Kim, Jaemoon Lee, and Bo H. Cho, "State-of-charge and capacity estimation of lithium-ion battery using a new open-circuit voltage versus state-of-charge," Journal of power sources, Vol. 185, No. 2, pp. 1367-1373, Dec. 2008.  https://doi.org/10.1016/j.jpowsour.2008.08.103
  12. Xiong, Rui, Yongzhi Zhang, Hongwen He, Xuan Zhou, and Michael G. Pecht, "A double-scale, particle-filtering, energy state prediction algorithm for lithium-ion batteries," IEEE Transactions on Industrial Electronics, Vol. 65, No. 2 pp. 1526-1538, Jul. 2017.  https://doi.org/10.1109/tie.2017.2733475
  13. Wang, Dong, Fangfang Yang, Kwok-Leung Tsui, Qiang Zhou, and Suk Joo Bae, "Remaining useful life prediction of lithium-ion batteries based on spherical cubature particle filter," IEEE Transactions on Instrumentation and Measurement, Vol. 65, No. 6, pp. 1282-1291, Mar. 2016.  https://doi.org/10.1109/TIM.2016.2534258
  14. Xiong, Rui, and Weixiang Shen, "Advanced battery management technologies for electric vehicles," John Wiley & Sons, 2019. 
  15. Hu, Chao, Gaurav Jain, Puqiang Zhang, Craig Schmidt, Parthasarathy Gomadam, and Tom Gorka, "Data-driven method based on particle swarm optimization and k-nearest neighbor regression for estimating capacity of lithium-ion battery," Applied Energy, Vol. 129, pp. 49-55, Sep. 2014.  https://doi.org/10.1016/j.apenergy.2014.04.077
  16. Patil, Meru A., Piyush Tagade, Krishnan S. Hariharan, Subramanya M. Kolake, Taewon Song, Taejung Yeo, and Seokgwang Doo. "A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation," Applied energy, Vol. 159, pp. 285-297, Dec. 2015.  https://doi.org/10.1016/j.apenergy.2015.08.119
  17. Severson, Kristen A., Peter M. Attia, Norman Jin, Nicholas Perkins, Benben Jiang, Zi Yang, Michael H. Chen et al. "Data-driven prediction of battery cycle life before capacity degradation," Nature Energy, Vol. 4, No. 5, pp. 383-391, Mar. 2019.  https://doi.org/10.1038/s41560-019-0356-8
  18. Kato, Hisashi, Yo Kobayashi, and Hajime Miyashiro, "Differential voltage curve analysis of a lithium-ion battery during discharge," Journal of Power Sources, Vol. 398, pp. 49-54, Sep. 2018.  https://doi.org/10.1016/j.jpowsour.2018.07.043
  19. Wang, Shuai, Lingling Zhao, Xiaohong Su, and Peijun Ma, "Prognostics of lithium-ion batteries based on battery performance analysis and flexible support vector regression," Energies, Vol. 7, No. 10, pp. 6492-6508, Oct. 2014.  https://doi.org/10.3390/en7106492
  20. Saha, Bhaskar, Kai Goebel, Scott Poll, and Jon Christopherse,. "Prognostics methods for battery health monitoring using a Bayesian framework," IEEE Transactions on instrumentation and measurement, Vol. 58, No. 2, pp. 291-296, Oct. 2008.  https://doi.org/10.1109/TIM.2008.2005965
  21. Widodo, Achmad, Min-Chan Shim, Wahyu Caesarendra, and Bo-Suk Yang, "Intelligent prognostics for battery health monitoring based on sample entropy," Expert Systems with Applications, Vol. 38, No. 9, pp. 11763-11769, Sep. 2011.  https://doi.org/10.1016/j.eswa.2011.03.063
  22. Goebel, Kai, Bhaskar Saha, Abhinav Saxena, Jose R. Celaya, and Jon P. Christophersen, "Prognostics in battery health management," IEEE instrumentation & measurement magazine, Vol. 11, No. 4, pp. 33-40, Aug. 2008.  https://doi.org/10.1109/MIM.2008.4579269
  23. Liu, Jie, Abhinav Saxena, Kai Goebel, Bhaskar Saha, and Wilson Wang, "An adaptive recurrent neural network for remaining useful life prediction of lithium-ion batteries," National Aeronautics And Space Administration Mof ett Field CA Ames Research Center, Oct. 2010. 
  24. Zhang, Yongzhi, Rui Xiong, Hongwen He, and Michael G. Pecht, "Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries," IEEE Transactions on Vehicular Technology, Vol. 67, No. 7, pp. 5695-5705, Feb. 2018.  https://doi.org/10.1109/tvt.2018.2805189
  25. Graves, Alex. "Long short-term memory," Supervised sequence labelling with recurrent neural networks, pp. 37-45. 2012. 
  26. B. Saha and K. Goebel, ''Battery data set,'' NASA Ames Prognostics Data Repository, NASA Ames, Moffett Field, CA, USA, Tech. Rep, 2007. 
  27. Zhao, Lin, Yipeng Wang, and Jianhua Cheng. "A hybrid method for remaining useful life estimation of lithium-ion battery with regeneration phenomena," Applied sciences, Vol. 9, No. 9, 2019. 
  28. Choi, Yohwan, Seunghyoung Ryu, Kyungnam Park, and Hongseok Kim. "Machine learning-based lithium-ion battery capacity estimation exploiting multi-channel charging profiles," Ieee Access, Vol. 7, pp. 75143-75152, Jun. 2019.  https://doi.org/10.1109/access.2019.2920932
  29. Li, Xiaoyu, Lei Zhang, Zhenpo Wang, and Peng Dong. "Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks," Journal of Energy Storage, Vol. 21, pp. 510-518, 2019.  https://doi.org/10.1016/j.est.2018.12.011
  30. Zhou, Funa, Po Hu, and Xiaohui Yang, "RUL prognostics method based on real time updating of LSTM parameters," In 2018 Chinese Control And Decision Conference (CCDC), pp. 3966-3971. Shenyang, China, Jun. 2018. 
  31. Li, Xiaoyu, Zhenpo Wang, Lei Zhang, Changfu Zou, and David D. Dorrell, "State-of-health estimation for Li-ion batteries by combing the incremental capacity analysis method with grey relational analysis," Journal of Power Sources, Vol. 410-411, pp. 106-114. Jan. 2019.  https://doi.org/10.1016/j.jpowsour.2018.10.069
  32. Li, Yi, Mohamed Abdel-Monem, Rahul Gopalakrishnan, Maitane Berecibar, Elise Nanini-Maury, Noshin Omar, Peter van den Bossche, and Joeri Van Mierlo, "A quick on-line state of health estimation method for Li-ion battery with incremental capacity curves processed by Gaussian filter," Journal of Power Sources 373, pp. 40-53, Jan. 2018. https://doi.org/10.1016/j.jpowsour.2017.10.092