Remaining Useful Life Estimation for LFP Cells in Second Life Applications

    •  Sanz-Gorrachategui, I., Pastor-Flores, P., Pajovic, M., Wang, Y., Orlik, P.V., Bernal-Ruiz, C., Bono-Nuez, A., Artal-Sevil, J.S., "Remaining Useful Life Estimation for LFP Cells in Second Life Applications", IEEE Transactions on Instrumentation and Measurement, DOI: 10.1109/​TIM.2021.3055791, Vol. 70, pp. 1-10, March 2021.
      BibTeX TR2021-023 PDF
      • @article{Sanz-Gorrachategui2021mar,
      • author = {Sanz-Gorrachategui, Ivan and Pastor-Flores, Pablo and Pajovic, Milutin and Wang, Ye and Orlik, Philip V. and Bernal-Ruiz, Carlos and Bono-Nuez, Antonio and Artal-Sevil, Jesús Sergio},
      • title = {Remaining Useful Life Estimation for LFP Cells in Second Life Applications},
      • journal = {IEEE Transactions on Instrumentation and Measurement},
      • year = 2021,
      • volume = 70,
      • pages = {1--10},
      • month = mar,
      • doi = {10.1109/TIM.2021.3055791},
      • issn = {1557-9662},
      • url = {}
      • }
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  • Research Areas:

    Machine Learning, Optimization, Signal Processing


The increasing deployment of battery storage applications in both grid storage and electric vehicle fields is generating a vast used battery market. These batteries are typically recycled but could be reused in Second Life applications. One of the challenges is to obtain an accurate Remaining Useful Life (RUL) estimation algorithm, which determines whether a battery is suitable for reuse and estimates the number of second life cycles the battery will last. In this paper, the RUL estimation problem is considered. We propose several Health Indicators (HI), some of which have not been explored before, along with simple yet effective estimation and classification algorithms. These algorithms include classification techniques such as Regularized Logistic Regression (RLR), and regression techniques such as Multivariable Linear Regression (MLR) and Multi-Layer Perceptron (MLP). As a more advanced solution, a multiple expert system combining said techniques is proposed. The performance of the algorithms and features is evaluated on a recent Lithium Iron Phosphate (LFP) dataset from Toyota Research Institute. We obtain satisfactory results in the estimation of RUL cycles with errors down to 49 RMSE cycles for cells that live up to 1200 cycles, and 0.24% MRE for the prediction of the evolution of capacity.