Abstract
State of charge (SOC) is one of the most important parameters in battery management systems, as it indicates the available battery capacity at every moment. There are numerous battery model-based methods used for SOC estimation, the accuracy of which depends on the accuracy of the model considered to describe the battery dynamics. The SOC estimation method proposed in this paper is based on an Extended Kalman Filter (EKF) and nonlinear battery model which was parameterized using extended laboratory tests performed on several 13 Ah lithium titanate oxide (LTO)-based lithium-ion batteries. The developed SOC estimation algorithm was successfully verified for a step discharge profile at five different temperatures and considering various initial SOC initialization values, showing a maximum SOC estimation error of 1.16% and a maximum voltage estimation error of 44 mV. Furthermore, by carrying out a sensitivity analysis it was showed that the SOC and voltage estimation error are only slightly dependent on the variation of the battery model parameters with the SOC.
Highlights
Lithium-ion (Li-ion) batteries have developed as the key energy storage technology for automotive applications and are evaluated in different stationary renewable energy storage applications [1,2,3,4,5].This is mainly because Li-ion batteries are characterized by superior performance in terms of power capability, efficiency, lifetime than other storage technologies [2,6]
In this paper a sensitivity analysis was performed, in order to observe the influence of the battery model parameters variation with SOC and temperature, on the accuracy of the proposed equivalent electrical circuit model and subsequently on the accuracy of the SOC estimation
A battery model-based on Extended Kalman Filter (EKF) SOC estimation algorithm was developed and verified for an lithium titanate oxide (LTO)-based Li-ion battery
Summary
Lithium-ion (Li-ion) batteries have developed as the key energy storage technology for automotive applications and are evaluated in different stationary renewable energy storage applications [1,2,3,4,5]. In order to parameterize a battery electrical model, which is able to accurately estimate the battery SOC and voltage for a wide range of operating conditions, the dependence of the battery parameters on the aforementioned states should be considered during the parametrization stage Such an extended battery parameterization requires extensive laboratory tests, which are extremely time-consuming and cost-demanding and sometimes are not reflected in the model accuracy improvement. In this paper a sensitivity analysis was performed, in order to observe the influence of the battery model parameters variation with SOC and temperature, on the accuracy of the proposed equivalent electrical circuit model and subsequently on the accuracy of the SOC estimation. The last section evaluates the sensitivity of the battery SOC and voltage estimation on the changes of EEC parameters
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