Open Access

ARTICLE

State of Health Estimation of LiFePO₄ Batteries for Battery Management Systems

Areeb Khalid^1,*, Syed Abdul Rahman Kashif¹, Noor Ul Ain¹, Ali Nasir²

1 Department of Electrical Engineering, University of Engineering and Technology, Lahore, Pakistan
2 Department of Electrical Engineering, University of Central Punjab, Lahore, Pakistan

* Corresponding Author: Areeb Khalid. Email:

Computers, Materials & Continua 2022, 73(2), 3149-3164. https://doi.org/10.32604/cmc.2022.029322

Received 01 March 2022; Accepted 13 April 2022; Issue published 16 June 2022

Abstract

When considering the mechanism of the batteries, the capacity reduction at storage (when not in use) and cycling (during use) and increase of internal resistance is because of degradation in the chemical composition inside the batteries. To optimize battery usage, a battery management system (BMS) is used to estimate possible aging effects while different load profiles are requested from the grid. This is specifically seen in a case when the vehicle is connected to the net (online through BMS). During this process, the BMS chooses the optimized load profiles based on the least aging effects on the battery pack. The major focus of this paper is to design an algorithm/model for lithium iron phosphate (LiFePO₄) batteries. The model of the batteries is based on the accelerated aging test data (data from the beginning of life till the end of life). The objective is to develop an algorithm based on the actual battery trend during the whole life of the battery. By the analysis of the test data, the complete trend of the battery aging and the factors on which the aging is depending on is identified, the aging model can then be recalibrated to avoid any differences in the production process during cell manufacturing. The validation of the model was carried out at the end by utilizing different driving profiles at different C-rates and different ambient temperatures. A Linear and non-linear model-based approach is used based on statistical data. The parameterization was carried out by dividing the data into small chunks and estimating the parameters for the individual chunks. Self-adaptive characteristic map using a lookup table was also used. The non-linear model was chosen as the best candidate among all other approaches for longer validation of 8-month data with real driving data set.

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