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Theoretical Framework for State Estimation
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DOI：10.1007/978-3-319-03527-7_7.png)
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Abstract
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One of the most important functions of the battery management system is to accurately estimate the battery state using minimal onboard instrumentation. In this chapter, we present a recursive Bayesian filtering framework for onboard battery state estimation by assimilating measurables like cell voltage, current, and temperature with a physics-based model prediction. This framework can be numerically implemented using state-of-the-art filtering/data assimilation algorithms. We first develop a generic framework and then discuss some of the most popular algorithms for its implementation.
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