Parameterization of a Battery Simulation Model Using Numerical Optimization Methods

Typically, battery models are complex and difficult to parameterize to match real-world data. Achieving a good generalized fit between measured and simulated results should be done using a variety of laboratory data. Numerical optimizations can ensure the best possible fit between a simulation model and measured data, given a set of constraints.

In this paper, we propose a semi-automated process for parameterizing a lithium polymer battery (LiPB) cell simulation model that is able to satisfy constraints on the optimized parameters. This process uses a number of measured data sets under a variety of conditions. An iterative numerical optimization algorithm using Simulink Parameter Estimation was implemented to estimate parameter values by minimizing error between measured and simulated results.