Useful energy prediction model of a Lithium-ion cell operating on various duty cycles

Abstract

The paper deals with the subject of the prediction of useful energy during the cycling of a lithium-ion cell (LIC), using machine learning-based techniques. It was demonstrated that depending on the combination of cycling parameters, the useful energy (RUEc) that can be transferred during a full cycle is variable, and also three different types of evolution of changes in RUEc were identified. The paper presents a new non-parametric RUEc prediction model based on Gaussian process regression. It was proven that the proposed methodology enables the RUEc prediction for LICs discharged, above the depth of discharge, at a level of 70% with an acceptable error, which is confirmed for new load profiles. Furthermore, techniques associated with explainable artificial intelligence were applied to determine the significance of model input parameters – the variable importance method – and to determine the quantitative effect of individual model parameters (their reciprocal interaction) on RUEc – the accumulated local effects model of the first and second order.

A Review of the Applications of Explainable Machine Learning for Lithium–Ion Batteries: From Production to State and Performance Estimation

“…While the previously mentioned papers focus on the SoC estimation problem, ref. [131] addresses the SoE estimation problem using explainability techniques. SoE estimation essentially has a different approach to it: unlike the SoC which is mainly based on Coulomb counting and affected by the electric current measurement and its accuracy, the SoE is both dependent on current and voltage values.…”

“…Ref. [131] designs a GPR model considering various duty cycles and shows an acceptable accuracy for any depth of discharge above 70 percent. The model takes the input variables of the cell temperature, average charge, discharge current, depth of discharge, and the energy throughput or the number of full equivalent cycles as a representation of the cells' state of health to estimate its SoE.…”

Bulletin of the Polish Academy of Sciences: Technical Sciences

“…According to forecasts of the Environmental Protection Agency, in 2025 at the global and EU level, lead-acid technologies (LAB; used mainly in the automotive industry) will continue to dominate; however, it has been pointed out that since 2018 the market of lithium-ion batteries has been systematically growing (LIB; batteries used in portable electronic devices) [19][20][21].…”

“…The literature describes various methods of analyzing processes in terms of the possibility of detecting potential sources of threats leading to losses, including those having a negative impact on the environment [21]. In this research, the PFMEA method (Process Failure Mode and Effects Analysis) [22] was adopted for this purpose.…”

Modelling of Li-Ion battery state-of-health with Gaussian processes

“…However, numerous ways of its implementation, variations and use are still under extensive research. [28,30,31]. As we mentioned, our proposal is based on the use of Markov Chains to sample data using the model we created earlier using STAN.…”