IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v255y2022ics0360544222014414.html
   My bibliography  Save this article

Battery monitoring and prognostics optimization techniques: Challenges and opportunities

Author

Listed:
  • Semeraro, Concetta
  • Caggiano, Mariateresa
  • Olabi, Abdul-Ghani
  • Dassisti, Michele

Abstract

In recent years, many researchers have been conducted on batteries' health monitoring and prognostics, mainly focusing on the batteries' state of charge (SOC). Accurately estimating the state of health (SOH) and predicting the remaining useful life (RUL) of battery components are very important for the prognosis and health management of the overall battery system. However, due to the non-linear dynamics caused by the electrochemical characteristics in batteries, the accurate estimations of SOC, SOH and RUL prediction are still challenging and many technologies have been developed to solve this challenge. This paper reviews and discusses state of the art in SOC and SOH and RUL estimation techniques for all battery types. A novel framework is developed and presented to compare all battery techniques based on three dimensions: battery performance (Z dimension), approaches (X dimension), and criteria (Y dimension) to fulfil. All studies are reviewed and discussed based on the dimensions and the criteria defined in the framework. Based on this investigation, this study summarizes at the end the key outcomes and suggests future research challenges.

Suggested Citation

  • Semeraro, Concetta & Caggiano, Mariateresa & Olabi, Abdul-Ghani & Dassisti, Michele, 2022. "Battery monitoring and prognostics optimization techniques: Challenges and opportunities," Energy, Elsevier, vol. 255(C).
    • Handle: RePEc:eee:energy:v:255:y:2022:i:c:s0360544222014414
    DOI: 10.1016/j.energy.2022.124538
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222014414
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.124538?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ma, Guijun & Zhang, Yong & Cheng, Cheng & Zhou, Beitong & Hu, Pengchao & Yuan, Ye, 2019. "Remaining useful life prediction of lithium-ion batteries based on false nearest neighbors and a hybrid neural network," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    2. Wang, Ya-Xiong & Chen, Zhenhang & Zhang, Wei, 2022. "Lithium-ion battery state-of-charge estimation for small target sample sets using the improved GRU-based transfer learning," Energy, Elsevier, vol. 244(PB).
    3. Shen, Jiangwei & Ma, Wensai & Xiong, Jian & Shu, Xing & Zhang, Yuanjian & Chen, Zheng & Liu, Yonggang, 2022. "Alternative combined co-estimation of state of charge and capacity for lithium-ion batteries in wide temperature scope," Energy, Elsevier, vol. 244(PB).
    4. Zhentong Liu & Hongwen He, 2015. "Model-based Sensor Fault Diagnosis of a Lithium-ion Battery in Electric Vehicles," Energies, MDPI, vol. 8(7), pages 1-19, June.
    5. Xiaodong Xu & Chuanqiang Yu & Shengjin Tang & Xiaoyan Sun & Xiaosheng Si & Lifeng Wu, 2019. "Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Wiener Processes with Considering the Relaxation Effect," Energies, MDPI, vol. 12(9), pages 1-17, May.
    6. Cheng, Gong & Wang, Xinzhi & He, Yurong, 2021. "Remaining useful life and state of health prediction for lithium batteries based on empirical mode decomposition and a long and short memory neural network," Energy, Elsevier, vol. 232(C).
    7. Li Zhang & Min Zheng & Dajun Du & Yihuan Li & Minrui Fei & Yuanjun Guo & Kang Li, 2020. "State-of-Charge Estimation of Lithium-Ion Battery Pack Based on Improved RBF Neural Networks," Complexity, Hindawi, vol. 2020, pages 1-10, December.
    8. Si, Xiao-Sheng & Wang, Wenbin & Hu, Chang-Hua & Zhou, Dong-Hua, 2011. "Remaining useful life estimation - A review on the statistical data driven approaches," European Journal of Operational Research, Elsevier, vol. 213(1), pages 1-14, August.
    9. Ng, Selina S.Y. & Xing, Yinjiao & Tsui, Kwok L., 2014. "A naive Bayes model for robust remaining useful life prediction of lithium-ion battery," Applied Energy, Elsevier, vol. 118(C), pages 114-123.
    10. Shuqing Li & Chuankun Ju & Jianliang Li & Ri Fang & Zhifei Tao & Bo Li & Tingting Zhang, 2021. "State-of-Charge Estimation of Lithium-Ion Batteries in the Battery Degradation Process Based on Recurrent Neural Network," Energies, MDPI, vol. 14(2), pages 1-21, January.
    11. Khaleghi, S. & Firouz, Y. & Van Mierlo, J. & Van den Bossche, P., 2019. "Developing a real-time data-driven battery health diagnosis method, using time and frequency domain condition indicators," Applied Energy, Elsevier, vol. 255(C).
    12. Xiaoyu Li & Xing Shu & Jiangwei Shen & Renxin Xiao & Wensheng Yan & Zheng Chen, 2017. "An On-Board Remaining Useful Life Estimation Algorithm for Lithium-Ion Batteries of Electric Vehicles," Energies, MDPI, vol. 10(5), pages 1-15, May.
    13. Xiangwei Guo & Longyun Kang & Yuan Yao & Zhizhen Huang & Wenbiao Li, 2016. "Joint Estimation of the Electric Vehicle Power Battery State of Charge Based on the Least Squares Method and the Kalman Filter Algorithm," Energies, MDPI, vol. 9(2), pages 1-16, February.
    14. Wu, Ji & Zhang, Chenbin & Chen, Zonghai, 2016. "An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks," Applied Energy, Elsevier, vol. 173(C), pages 134-140.
    15. Li, Yi & Liu, Kailong & Foley, Aoife M. & Zülke, Alana & Berecibar, Maitane & Nanini-Maury, Elise & Van Mierlo, Joeri & Hoster, Harry E., 2019. "Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    16. Bensi, Michelle & Kiureghian, Armen Der & Straub, Daniel, 2013. "Efficient Bayesian network modeling of systems," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 200-213.
    17. Li, Xiaoyu & Yuan, Changgui & Wang, Zhenpo, 2020. "State of health estimation for Li-ion battery via partial incremental capacity analysis based on support vector regression," Energy, Elsevier, vol. 203(C).
    18. Kim, Minho & Chun, Huiyong & Kim, Jungsoo & Kim, Kwangrae & Yu, Jungwook & Kim, Taegyun & Han, Soohee, 2019. "Data-efficient parameter identification of electrochemical lithium-ion battery model using deep Bayesian harmony search," Applied Energy, Elsevier, vol. 254(C).
    19. Chen, Zewang & Shi, Na & Ji, Yufan & Niu, Mu & Wang, Youren, 2021. "Lithium-ion batteries remaining useful life prediction based on BLS-RVM," Energy, Elsevier, vol. 234(C).
    20. Liu, Guoan & Xu, Cheng & Li, Haomiao & Jiang, Kai & Wang, Kangli, 2019. "State of charge and online model parameters co-estimation for liquid metal batteries," Applied Energy, Elsevier, vol. 250(C), pages 677-684.
    21. Mc Carthy, Kieran & Gullapalli, Hemtej & Kennedy, Tadhg, 2022. "Online state of health estimation of Li-ion polymer batteries using real time impedance measurements," Applied Energy, Elsevier, vol. 307(C).
    22. Chen, Junxiong & Feng, Xiong & Jiang, Lin & Zhu, Qiao, 2021. "State of charge estimation of lithium-ion battery using denoising autoencoder and gated recurrent unit recurrent neural network," Energy, Elsevier, vol. 227(C).
    23. Song, Yuchen & Liu, Datong & Liao, Haitao & Peng, Yu, 2020. "A hybrid statistical data-driven method for on-line joint state estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 261(C).
    24. Lin, Cheng & Mu, Hao & Xiong, Rui & Shen, Weixiang, 2016. "A novel multi-model probability battery state of charge estimation approach for electric vehicles using H-infinity algorithm," Applied Energy, Elsevier, vol. 166(C), pages 76-83.
    25. Deng, Zhongwei & Hu, Xiaosong & Lin, Xianke & Che, Yunhong & Xu, Le & Guo, Wenchao, 2020. "Data-driven state of charge estimation for lithium-ion battery packs based on Gaussian process regression," Energy, Elsevier, vol. 205(C).
    26. Yang, Fangfang & Zhang, Shaohui & Li, Weihua & Miao, Qiang, 2020. "State-of-charge estimation of lithium-ion batteries using LSTM and UKF," Energy, Elsevier, vol. 201(C).
    27. Hu, Chunsheng & Ma, Liang & Guo, Shanshan & Guo, Gangsheng & Han, Zhiqiang, 2022. "Deep learning enabled state-of-charge estimation of LiFePO4 batteries: A systematic validation on state-of-the-art charging protocols," Energy, Elsevier, vol. 246(C).
    28. Xi, Zhimin & Wang, Rui & Fu, Yuhong & Mi, Chris, 2022. "Accurate and reliable state of charge estimation of lithium ion batteries using time-delayed recurrent neural networks through the identification of overexcited neurons," Applied Energy, Elsevier, vol. 305(C).
    29. Yang, Duo & Wang, Yujie & Pan, Rui & Chen, Ruiyang & Chen, Zonghai, 2018. "State-of-health estimation for the lithium-ion battery based on support vector regression," Applied Energy, Elsevier, vol. 227(C), pages 273-283.
    30. Xu, Tingting & Peng, Zhen & Wu, Lifeng, 2021. "A novel data-driven method for predicting the circulating capacity of lithium-ion battery under random variable current," Energy, Elsevier, vol. 218(C).
    31. Feng, Xiong & Chen, Junxiong & Zhang, Zhongwei & Miao, Shuwen & Zhu, Qiao, 2021. "State-of-charge estimation of lithium-ion battery based on clockwork recurrent neural network," Energy, Elsevier, vol. 236(C).
    32. Prakash Venugopal & Vigneswaran T., 2019. "State-of-Health Estimation of Li-ion Batteries in Electric Vehicle Using IndRNN under Variable Load Condition," Energies, MDPI, vol. 12(22), pages 1-29, November.
    33. Yang, Fangfang & Li, Weihua & Li, Chuan & Miao, Qiang, 2019. "State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network," Energy, Elsevier, vol. 175(C), pages 66-75.
    34. Li, Junfu & Wang, Lixin & Lyu, Chao & Pecht, Michael, 2017. "State of charge estimation based on a simplified electrochemical model for a single LiCoO2 battery and battery pack," Energy, Elsevier, vol. 133(C), pages 572-583.
    35. Xu, Zhicheng & Wang, Jun & Lund, Peter D. & Zhang, Yaoming, 2022. "Co-estimating the state of charge and health of lithium batteries through combining a minimalist electrochemical model and an equivalent circuit model," Energy, Elsevier, vol. 240(C).
    36. Chang, Yang & Fang, Huajing & Zhang, Yong, 2017. "A new hybrid method for the prediction of the remaining useful life of a lithium-ion battery," Applied Energy, Elsevier, vol. 206(C), pages 1564-1578.
    37. Hashemi, Seyed Reza & Mahajan, Ajay Mohan & Farhad, Siamak, 2021. "Online estimation of battery model parameters and state of health in electric and hybrid aircraft application," Energy, Elsevier, vol. 229(C).
    38. Ng, Kong Soon & Moo, Chin-Sien & Chen, Yi-Ping & Hsieh, Yao-Ching, 2009. "Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries," Applied Energy, Elsevier, vol. 86(9), pages 1506-1511, September.
    39. Yang, Xiaolong & Chen, Yongji & Li, Bin & Luo, Dong, 2020. "Battery states online estimation based on exponential decay particle swarm optimization and proportional-integral observer with a hybrid battery model," Energy, Elsevier, vol. 191(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Semeraro, Concetta & Aljaghoub, Haya & Abdelkareem, Mohammad Ali & Alami, Abdul Hai & Olabi, A.G., 2023. "Digital twin in battery energy storage systems: Trends and gaps detection through association rule mining," Energy, Elsevier, vol. 273(C).
    2. Wei, Meng & Balaya, Palani & Ye, Min & Song, Ziyou, 2022. "Remaining useful life prediction for 18650 sodium-ion batteries based on incremental capacity analysis," Energy, Elsevier, vol. 261(PA).
    3. García, Antonio & Monsalve-Serrano, Javier & Ponce-Mora, Alberto & Fogué-Robles, Álvaro, 2023. "Development of a calibration methodology for fitting the response of a lithium-ion cell P2D model using real driving cycles," Energy, Elsevier, vol. 271(C).
    4. Zheng, Jianfei & Ren, Jincheng & Zhang, Jianxun & Pei, Hong & Zhang, Zhengxin, 2023. "A lifetime prediction method for Lithium-ion batteries considering storage degradation of spare parts," Energy, Elsevier, vol. 282(C).
    5. Zhu, Yunlong & Dong, Zhe & Cheng, Zhonghua & Huang, Xiaojin & Dong, Yujie & Zhang, Zuoyi, 2023. "Neural network extended state-observer for energy system monitoring," Energy, Elsevier, vol. 263(PA).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rauf, Huzaifa & Khalid, Muhammad & Arshad, Naveed, 2022. "Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    2. Chen, Junxiong & Zhang, Yu & Wu, Ji & Cheng, Weisong & Zhu, Qiao, 2023. "SOC estimation for lithium-ion battery using the LSTM-RNN with extended input and constrained output," Energy, Elsevier, vol. 262(PA).
    3. Sui, Xin & He, Shan & Vilsen, Søren B. & Meng, Jinhao & Teodorescu, Remus & Stroe, Daniel-Ioan, 2021. "A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery," Applied Energy, Elsevier, vol. 300(C).
    4. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Yang, Xiaoyong & Fernandez, Carlos, 2022. "An optimized long short-term memory-weighted fading extended Kalman filtering model with wide temperature adaptation for the state of charge estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 326(C).
    5. Siyi Tao & Bo Jiang & Xuezhe Wei & Haifeng Dai, 2023. "A Systematic and Comparative Study of Distinct Recurrent Neural Networks for Lithium-Ion Battery State-of-Charge Estimation in Electric Vehicles," Energies, MDPI, vol. 16(4), pages 1-17, February.
    6. Zhu, Yunlong & Dong, Zhe & Cheng, Zhonghua & Huang, Xiaojin & Dong, Yujie & Zhang, Zuoyi, 2023. "Neural network extended state-observer for energy system monitoring," Energy, Elsevier, vol. 263(PA).
    7. Wang, Yujie & Tian, Jiaqiang & Sun, Zhendong & Wang, Li & Xu, Ruilong & Li, Mince & Chen, Zonghai, 2020. "A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    8. Zhang, Meng & Hu, Tao & Wu, Lifeng & Kang, Guoqing & Guan, Yong, 2021. "A method for capacity estimation of lithium-ion batteries based on adaptive time-shifting broad learning system," Energy, Elsevier, vol. 231(C).
    9. Fei, Zicheng & Yang, Fangfang & Tsui, Kwok-Leung & Li, Lishuai & Zhang, Zijun, 2021. "Early prediction of battery lifetime via a machine learning based framework," Energy, Elsevier, vol. 225(C).
    10. Ma, Yan & Shan, Ce & Gao, Jinwu & Chen, Hong, 2022. "A novel method for state of health estimation of lithium-ion batteries based on improved LSTM and health indicators extraction," Energy, Elsevier, vol. 251(C).
    11. Chen, Zheng & Zhao, Hongqian & Shu, Xing & Zhang, Yuanjian & Shen, Jiangwei & Liu, Yonggang, 2021. "Synthetic state of charge estimation for lithium-ion batteries based on long short-term memory network modeling and adaptive H-Infinity filter," Energy, Elsevier, vol. 228(C).
    12. Panagiotis Eleftheriadis & Spyridon Giazitzis & Sonia Leva & Emanuele Ogliari, 2023. "Data-Driven Methods for the State of Charge Estimation of Lithium-Ion Batteries: An Overview," Forecasting, MDPI, vol. 5(3), pages 1-24, September.
    13. Li, Renzheng & Wang, Hui & Dai, Haifeng & Hong, Jichao & Tong, Guangyao & Chen, Xinbo, 2022. "Accurate state of charge prediction for real-world battery systems using a novel dual-dropout-based neural network," Energy, Elsevier, vol. 250(C).
    14. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Li, Huan & Xu, Wenhua & Fernandez, Carlos, 2022. "An optimized relevant long short-term memory-squared gain extended Kalman filter for the state of charge estimation of lithium-ion batteries," Energy, Elsevier, vol. 260(C).
    15. Chen, Liping & Wu, Xiaobo & Lopes, António M. & Yin, Lisheng & Li, Penghua, 2022. "Adaptive state-of-charge estimation of lithium-ion batteries based on square-root unscented Kalman filter," Energy, Elsevier, vol. 252(C).
    16. Zhang, Yu & Peng, Zhen & Guan, Yong & Wu, Lifeng, 2021. "Prognostics of battery cycle life in the early-cycle stage based on hybrid model," Energy, Elsevier, vol. 221(C).
    17. Cheng, Gong & Wang, Xinzhi & He, Yurong, 2021. "Remaining useful life and state of health prediction for lithium batteries based on empirical mode decomposition and a long and short memory neural network," Energy, Elsevier, vol. 232(C).
    18. Molla Shahadat Hossain Lipu & Tahia F. Karim & Shaheer Ansari & Md. Sazal Miah & Md. Siddikur Rahman & Sheikh T. Meraj & Rajvikram Madurai Elavarasan & Raghavendra Rajan Vijayaraghavan, 2022. "Intelligent SOX Estimation for Automotive Battery Management Systems: State-of-the-Art Deep Learning Approaches, Open Issues, and Future Research Opportunities," Energies, MDPI, vol. 16(1), pages 1-31, December.
    19. Jun Peng & Zhiyong Zheng & Xiaoyong Zhang & Kunyuan Deng & Kai Gao & Heng Li & Bin Chen & Yingze Yang & Zhiwu Huang, 2020. "A Data-Driven Method with Feature Enhancement and Adaptive Optimization for Lithium-Ion Battery Remaining Useful Life Prediction," Energies, MDPI, vol. 13(3), pages 1-20, February.
    20. Hu, Chunsheng & Ma, Liang & Guo, Shanshan & Guo, Gangsheng & Han, Zhiqiang, 2022. "Deep learning enabled state-of-charge estimation of LiFePO4 batteries: A systematic validation on state-of-the-art charging protocols," Energy, Elsevier, vol. 246(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:255:y:2022:i:c:s0360544222014414. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.