Rapid Estimation of Battery Storage Capacity through Multiple Linear Regression

Abstract

Due to global warming issues, the rapid growth of electric vehicle sales is fully expected to result in a dramatic increase in returned batteries after the first use. Naturally, industries have shown great interest in establishing business models for retired battery reuse and recycling. However, they still have many challenges, such as high costs from the logistics of returned batteries and evaluating returned battery quality. One of the most important characteristics of a returned battery is the battery storage capacity. Conventionally, the battery’s energy capacity is measured through the low current full charging and discharging process. While this traditional measurement procedure gives a reliable estimate of battery storage capacity, the time required for a reliable estimate is unacceptably long to support profitable business models. In this paper, we propose a new algorithm to estimate battery storage capacity that can dramatically reduce the time for estimation through the partial discharging process. To demonstrate the applicability of the proposed algorithm, cylindrical and prismatic cells were used in the experiments. Initially, five indicators were selected from the voltage response curves that can identify battery storage capacity. Then, the five indicators were applied to principal component analysis (PCA) to extract dominant factors. The extracted factors were applied to a multiple linear regression model to produce a reliable estimation of battery storage capacity.

Rapid Estimation of Static Capacity Based on Machine Learning: A Time-Efficient Approach

“…As the main goal of this study was to reduce the estimation time for the battery's static capacity, parametric experiments were carried out for the cases of a 6 min, 3 min, and 1 min discharge period, respectively. In earlier research, training data were collected through constant-current discharging at a rate of 1C, and the researchers of that study employed a linear regression to assess the static capacity of batteries with the data obtained through partial discharging at 10% state of charge (SOC) intervals [30]. A 10% SOC interval was implemented with a 6 min partial discharge at a 1C rate, and by utilizing voltage characteristic data, the method effectively used the voltage data at various SOCs to take aging into account for the successful estimation of the static capacities.…”