Probabilistic state-of-charge estimation of lithium-ion batteries considering cell-to-cell variability due to manufacturing tolerance

Abstract

As lithium-ion batteries are gaining more attention in various industries, analysis of their performances especially state-of-charge becomes critical. Due to manufacturing tolerance, battery model parameters may deviate from the nominal parameters. Such a parameter mismatching issue could result in an inaccurate cell state-of-charge estimation. Conducting cell characterization test for each cell, however, is time consuming and economically not possible in reality. This paper proposes a probabilistic approach for battery state-of-charge estimation considering the cell-to-cell variability so that reliable state-of-charge estimation can be obtained for a batch of similar cells. Therefore, parameter characterization test for each cell is not required while ensuring the state-of-charge estimation reliability. The proposed method consists of two major technical components. Firstly, battery model parameters are calibrated and modeled as Gaussian processes over the state-of-charge domain to account for the cell-to-cell variability. Secondly, state-of-charge variability under any charging/discharging profile is effectively quantified through the seamless integration of the extended Kalman filter and an uncertainty quantification method. As such, confidence-based state-of-charge estimation can be produced. Accurate and efficient estimation of the state-of-charge uncertainty are demonstrated for four battery loading profiles.