State of health estimation of battery modules via differential voltage analysis with local data symmetry method

Abstract

Cyclic voltammogram (CV) and differential voltage analysis (DVA) are two effective techniques to analyze the aging mechanism and estimate the aging state of a battery. However, the effectiveness of the two methods reported previously is based on single battery cells. In this paper, a comparison of the two methods is stated, and the equivalent relation is further derived. Besides, a local data symmetry method is introduced to calculate the differential voltage (DV) curve. The DV curves calculated by the proposed method are much smoother than that by the numerical-derivative method. Based on the location interval of two inflection points in the DV curve, a new method is inferred for lithium iron phosphate (LiFePO4) battery cells, and is applied to estimate the state of health (SOH) of battery modules. The applicability of the method is further verified via battery module simulation and experimental data. The results show that the DV curves fluctuate and do not overlap in the voltage plateau region due to the uneven currents flowing through each in-parallel battery cells. There is also a good linear regression of the two inflection point location interval versus battery module capacity within 2% error bounds, suggesting that the DVA method inferred from battery cells can be directly applied to battery modules.