Abstract
The performance of estimation algorithms is vital for the correct functioning of batteries in electric vehicles, as poor estimates will inevitably jeopardize the operations that rely on un-measurable quantities, such as State of Charge and State of Health. This paper compares the performance of three nonlinear estimation algorithms: the Extended Kalman Filter, the Unscented Kalman Filter and the Particle Filter, where a lithium-ion cell model is considered. The effectiveness of these algorithms is measured by their ability to produce accurate estimates against their computational complexity in terms of number of operations and execution time required. The trade-offs between estimators' performance and their computational complexity are analyzed.
Highlights
Accurate battery estimation algorithms are considered to be of great importance due to their applications in electrified transportation and energy storage systems
Other approaches include a nonlinear equivalent circuit models (ECMs) to estimate the State of Charge (SoC) of the battery model by applying the Particle Filter (PF) [5] and the use of ECMs to compare the effectiveness of Extended Kalman Filter (EKF) and PF based on state estimations [6]
The nonlinear cell model was based on an equivalent Thevenin circuit
Summary
Accurate battery estimation algorithms are considered to be of great importance due to their applications in electrified transportation and energy storage systems. Other approaches include a nonlinear ECM to estimate the SoC of the battery model by applying the Particle Filter (PF) [5] and the use of ECMs to compare the effectiveness of EKF and PF based on state estimations [6]. Resistances Ro, RTh and the Open Circuit Voltage (OCV) are considered to be parameters and are functions of the SoH, SoC, current, temperature and charge/discharge conditions. They are obtained from experimental results and the procedure to determine them is based on offline methods and explained in [15]. This is given as a function of the number of states n, the number of inputs m and the number of outputs p
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