Approach for Rigorous Evaluation of a Battery Fuel Gauge

A battery management system (BMS) is crucial for the safe and reliable operation of a battery pack. During use, it is important to monitor the remaining charge in the battery, known as the state of charge (SOC), to preserve battery health and lifetime. However, the SOC of a battery cannot be directly measured and it is approximated by the battery fuel gauge (BFG) using several empirical approaches. The accuracy of the SOC calculated by the BFG is affected by (i) temperature (ii) charging/usage history (iii) hysteresis and relaxation effects. Evaluating the SOC values reported by a BFG remains a challenging problem due to the fact that it is not possible to know the true SOC value. Consequently, indirect measures were developed to evaluate the SOC estimates reported by a BFG. In this paper, three BFG evaluation metrics: the Coulomb counting (CC) metric, the open circuit voltage (OCV) metric and the time-to-voltage (TTV) metric are demonstrated. The present paper is focused on demonstrating the implementation details of the above three BFG evaluation metrics. The proposed metrics are modified versions of previously reported ones to make the BFG evaluation more robust. Voltage and current data generated from a battery simulator and a BFG based on the extended Kalman filter algorithms were employed to demonstrate the proposed evaluation scheme. The battery in the simulator is set to an Rint approximation of the equivalent circuit model (ECM) and the BFG is set to assume the knowledge of the ECM model parameters. Voltage and current measurements were simulated based on a noisy model with zero mean and known standard deviation. Under these assumptions, the BFG under evaluation produced less than 1% error in SOC and less than 15 minutes in TTV error. These values, produced under the known model assumption, can be taken as a benchmark for the same voltage and current measurement noise statistics.