A scaling approach for improved state of charge representation in rechargeable batteries

Abstract

State of charge estimation is one of the key elements in battery management systems. Accurate estimation of state of charge in real time is crucial in many applications such as in electric vehicles and aerospace systems. As a result, state of charge modeling and real-time state of charge tracking remain active topics in the battery management systems research domain. One of the key steps in real-time state of charge estimation is the representation of the open circuit voltage as a parametrized function of the state of charge – these parameters will later be used in real-time state of charge estimation based on instantaneous voltage and current measurements. The accuracy of a real-time state of charge estimation scheme is built on the assumption that the open circuit voltage curve is error free. In this paper, we show an example where most of the traditional open circuit voltage characterization approaches would result in up to 10% worst-case state of charge error. Then we present a scaling approach that can reduce this worst-case modeling error to less than 1%. Later, we demonstrate how the proposed scaling approach can be incorporated in real-time state of charge estimation methods, such as the extended Kalman filter based ones. The proposed methods are demonstrated on data collected from nine different battery cells at 16 different temperatures ranging from -25°C to 50°C.