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Battery monitoring and prognostics optimization techniques: Challenges and opportunities

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  • 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
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    2. 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).
    3. Wang, Fengfei & Tang, Shengjin & Han, Xuebing & Yu, Chuanqiang & Sun, Xiaoyan & Lu, Languang & Ouyang, Minggao, 2024. "Capacity prediction of lithium-ion batteries with fusing aging information," Energy, Elsevier, vol. 293(C).
    4. Gomez, William & Wang, Fu-Kwun & Chou, Jia-Hong, 2024. "Li-ion battery capacity prediction using improved temporal fusion transformer model," Energy, Elsevier, vol. 296(C).
    5. 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).
    6. 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).
    7. 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).
    8. 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).
    9. Wei, Meng & Ye, Min & Zhang, Chuanwei & Wang, Qiao & Lian, Gaoqi & Xia, Baozhou, 2024. "Integrating mechanism and machine learning based capacity estimation for LiFePO4 batteries under slight overcharge cycling," Energy, Elsevier, vol. 296(C).

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