Abstract
This paper presents a framework for battery modeling in online, real-time applications where accuracy is important but speed is the key. The framework allows users to select model structures with the smallest number of parameters that is consistent with the accuracy requirements of the target application. The tradeoff between accuracy and speed in a battery model identification process is explored using different model structures and parameter-fitting algorithms. Pareto optimal sets are obtained, allowing a designer to select an appropriate compromise between accuracy and speed. In order to get a clearer understanding of the battery model identification problem, “identification surfaces” are presented. As an outcome of the battery identification surfaces, a new analytical solution is derived for battery model identification using a closed-form formula to obtain a battery’s ohmic resistance and open circuit voltage from measurement data. This analytical solution is used as a benchmark for comparison of other fitting algorithms and it is also used in its own right in a practical scenario for state-of-charge estimation. A simulation study is performed to demonstrate the effectiveness of the proposed framework and the simulation results are verified by conducting experimental tests on a small NiMH battery pack.
Highlights
N OWADAYS, batteries are used in a wide range of applications varying from small smartphones and laptops to large high-power electric road vehicles, aircraft, and marine craft
Because of the relatively small number of relevant studies in the literature, this paper has focused on the identification time as discussed below
The identification process is repeated over the whole range of state of charge (SoC) at 20 ◦C temperature at regular intervals; called “identification window” or “identification horizon.”
Summary
N OWADAYS, batteries are used in a wide range of applications varying from small smartphones and laptops to large high-power electric road vehicles, aircraft, and marine craft. In response to this growing demand, much research is focused on the development of new battery technologies. Integral to this is battery modeling, which is vital for many applications. In an electric vehicle (EV), it is important to understand state-of-charge (or remaining capacity), which is vital for any kind of range prediction.
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