Accurate battery health modeling allows one to make better design decisions, enables health conscious control, and allows for feed-forward State of Health estimation. However, experiments are necessary in order to obtain and validate these models. Unfortunately, battery health experiments are costly in terms of time, person-hours, and equipment. This makes it extremely important to minimize the number of experimental iterations.

This paper aims to minimize time and expense of experiments while maximizing information gathered by bridging an important gap between the Optimal Experimental Design (OED) and the battery health experimental/modeling literature. We demonstrate how to apply static OED methods to a battery aging experiment. This allows us to select a set of Constant Current Constant Voltage (CCCV) cycles that maximizes the amount of information gathered — in turn allowing us to better identify the health model parameters. The CCCV cycling is carried out in a laboratory using 14 LiFePO4 cells (10 for fitting and 4 for validation). Each of these cells undergoes 429 days of battery health cycling. Results from these experiments include: a model of battery capacity fade based on voltage and current, battery health dependence on voltage, and a lack of power fade under the cycling conditions. The use of OED to coordinate our model form and experiment helped to ensure a fruitful model resulted when processing the collected data. Based on this success we suggest a generalized framework for Optimal Battery Health Model Experiments (OBHME), which allows one to apply OED to a variety of related problems.

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