Lithium-Ion Battery Charging Schedule Optimization to Balance Battery Usage and Degradation

TL;DR

This paper develops an advanced optimization method for lithium-ion battery charging schedules that balances revenue and degradation, using a multivariate approach and novel cost forecasting models.

Contribution

It introduces a multivariate optimization framework with Nesterov momentum gradient descent for battery charging, considering energy costs and degradation.

Findings

Optimized charging schedules improve revenue and reduce degradation.

The method outperforms constant and random pricing strategies.

Gaussian process forecasting enhances cost prediction accuracy.

Abstract

This work optimizes a lithium-ion battery charging schedule while considering a joint revenue and battery degradation model. The study extends the work of Meheswari et. al. to encourage battery usage/charging at optimal intervals depending on energy cost forecasts. This paper utilizes central difference Nesterov momentum gradient descent to come to optimal charging strategies and deal with the non-linearities of the battery degradation model. This optimization strategy is tested against constant, random varied price forecasts and a novel Gaussian process cost forecasting model. Contrary to many other papers regarding battery charging, formulating schedule optimization as a multivariate optimization problem provides meaningful insight to the inherent balance between these two competing objectives.