Equivalent circuit models for batteries are commonly used in electric vehicle battery management systems to estimate state of charge and other important latent variables. They are computationally inexpensive, but they suffer from a loss of accuracy over the full range of conditions that may be experienced in real-life. One reason for this is that the model parameters, such as internal resistance, change over the lifetime of the battery due to degradation. However, estimating long term changes is challenging, because parameters also change with state of charge and other variables. To address this, we modelled the internal resistance parameter as a function of state of charge and degradation using a Gaussian process (GP). This was performed computationally efficiently using an algorithm that interprets a GP to be the solution of a linear time-invariant stochastic differential equation. As a result, inference of the posterior distribution of the GP scales as O(n) and can be implemented recursively using a Kalman filter. Additionally, the Kalman filter was used to calculate the negative unnormalized logarithm of the posterior probability for the GP hyperparameters. Using a uniform prior, the posterior distribution of these hyperparameters, namely the characteristic length scales over state of charge and degradation, were determined by maximizing the marginal likelihood of observing the data to determine the mode, with the parameter variances subsequently calculated by the Laplace approximation.


Combining Non-Parametric and Parametric Models for Stable and Computationally Efficient Battery Health Estimation

Division / Sponsors: 
Dynamic Systems & Control (DSCD)

Paper Proceedings: https://asmedigitalcollection.asme.org/DSCC/DSCC2020/volume/84270

DSCC2020 - Virtual Conference

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