Ensemble bias-correction based state of charge estimation of lithium-Ion batteries

It is an essential task of battery management system (BMS) to online estimate the State of Charge (SoC) of a Lithium-ion (Li-ion) battery, an important indicator of the remaining charge in the battery. Accurate modeling of the electrical behavior of a Li-ion battery can provide an accurate approximation of the battery dynamic characteristics during charging/discharging and relaxation phases. This is essential to accurate online estimation of the battery SoC. Equivalent circuit models (ECMs) are widely used to assist with online SoC estimation because of their simplicity and high computational efficiency. This thesis proposes an ensemble bias-correction (BC) method with adaptive weights to improve the accuracy of an equivalent circuit model (ECM) in dynamic modeling of Li-ion batteries. The contribution of this thesis is threefold: (i) the introduction of the concept of time period; (ii) the development of a novel ensemble method based on BC learning to model the dynamic characteristics of Li-ion batteries; and (iii) the creation of an adaptive-weighting scheme to learn online the weights of offline member BC models for building an online ensemble BC model. Repeated pulsing discharge tests with single and multiple C-rates were conducted on seven Li-ion battery cells to evaluate the effectiveness of the proposed ensemble BC method.