Multi-Objective Bayesian Optimization of Lithium-Ion Battery Cells for Electric Vehicle Operational Scenarios

Abstract

The development of lithium-ion batteries (LIBs) based on current practice allows an energy density increase estimated at 10% per year. However, the required power for portable electronic devices is predicted to increase at a much faster rate, namely 20% per year. Similarly, the global electric vehicle battery capacity is expected to increase from around 170 GWh per year today to 1.5 TWh per year in 2030—this is an increase of 125% per year. Without a breakthrough in battery design technology, it will be difficult to keep up with their increasing energy demand. The objective of this investigation is to develop a design methodology to accelerate the LIB development through the integration of electro-chemical numerical simulations and machine learning algorithms. In this work, the Gaussian process (GP) regression model is used as a fast approximation of numerical simulation (conducted using Simcenter Battery Design Studio®). The GP regression models are systematically updated through a multi-objective Bayesian optimization algorithm, which enables the exploration of innovative designs as well as the determination of optimal configurations. The results reported in this work include optimal thickness and porosities of LIB electrodes for several practical charge–discharge scenarios which maximize energy density and minimize capacity fade.

Bayesian optimisation for efficient material discovery: a mini review

“…4 BO has been successfully applied to many materials science problems. Some recent applications include drug design, 7,8 lithium-ion battery optimisation, 9,10 optical material design, [11][12][13] optimisation of thermoelectric properties 14 and energy predictions of materials. 15,16 It has also been applied to optimise operational conditions such as heating conditions 17 and equipment configurations.…”