Machine learning pipeline for battery state-of-health estimation

Abstract

Lithium-ion batteries are ubiquitous in modern day applications ranging from portable electronics to electric vehicles. Irrespective of the application, reliable real-time estimation of battery state of health (SOH) by on-board computers is crucial to the safe operation of the battery, ultimately safeguarding asset integrity. In this paper, we design and evaluate a machine learning pipeline for estimation of battery capacity fade -a metric of battery health -on 179 cells cycled under various conditions. The pipeline estimates battery SOH with an associated confidence interval by using two parametric and two non-parametric algorithms. Using segments of charge voltage and current curves, the pipeline engineers 30 features, performs automatic feature selection and calibrates the algorithms. When deployed on cells operated under the fast-charging protocol, the best model achieves a root mean squared percent error of 0.45%. This work provides insights into the design of scalable data-driven models for battery SOH estimation, emphasising the value of confidence bounds around the prediction. The pipeline methodology combines experimental data with machine learning modelling and can be generalized to other critical components that require real-time estimation of SOH. IntroductionRechargeable Li-ion batteries play a crucial role in many modern-day applications ranging from portable electronics and medical devices, to renewable energy integration in power grids and electric vehicles. The steep decrease in the price of lithium-ion based battery storage by

State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives

“…SVM shows high efficiency in solving nonlinear and high-dimensional model fitting and classification with less samples ( Chen et al., 2018b ). The SVM algorithm deals with the linear inseparability of sample data based on kernel functions ( Roman et al., 2021 ). It maps the vector to a higher dimensional space, separates the data on both sides of the hyperplane by determining two parallel hyperplanes, and maximizes the distance between the two parallel hyperplanes ( Cai et al., 2020 ).…”

KoRASA: Pipeline Optimization for Open-Source Korean Natural Language Understanding Framework Based on Deep Learning

“…ey could not only discover the potential ADRs of drugs but also predict the possible ADRs of new drugs. Roman et al [26] proposed a machine learning pipeline for battery state-of-health estimation, providing insights into the design of scalable datadriven models for battery SOH estimation, emphasizing the value of confidence bounds around the prediction. [18], and it follows a typical NLU process, including tokenization, featurization, intent classification, and entity extraction, as shown in Figure 2.…”

Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale

“…analyzing tomography data [64] ), predicting key parameters from experimental data (e.g. battery lifespan prediction [65] and monitoring the state of health [66] ), and optimizing experimental protocols (e.g. fast‐charge protocol optimization [67] ).…”

“…[63] Its incorporation with experiments can be classified into three categories:a ssisting experimental result analysis (e.g.a nalyzing tomography data [64] ), predicting key parameters from experimental data (e.g. battery lifespan prediction [65] and monitoring the state of health [66] ), and optimizing experimental protocols (e.g.f astcharge protocol optimization [67] ).…”