Validation and benchmark methods for battery management system functionalities: State of charge estimation algorithms

Several state of charge estimation algorithms have been developed and validated in the past. However, due to varying validation methods, the results cannot be compared. This paper presents an approach for a generalised validation and benchmark method for state of charge estimation algorithms. The independence of standardised driving cycles is obtained by developing a synthetic load cycle. To do so, a frequency analysis is performed for 149 different driving cycles and the five major time constants are identified at 55.8 s, 9 s, 5.1 s, 3.8 s and 1 s. Using the synthetic load profile, three validation profiles are created. In addition to low- and high-dynamic behaviour, long-term stability is considered at five different temperatures (-10 °C, 0 °C, 10 °C, 25 °C and 40 °C). During the long-term test, the temperature varies between -10 °C and 40 °C. To ensure comparability, a quantitative rating technique is introduced for estimation accuracy, transient behaviour, drift, failure stability, temperature stability and residual charge estimation to evaluate the performance of different state estimation algorithms. Furthermore, the benchmark can be used to optimise the state estimator, such as a linear and an extended Kalman filter examined within this study.