Machine learning pipeline for battery state of health estimation

02/01/2021
by   Darius Roman, et al.
0

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.

READ FULL TEXT

Authors

page 18

07/17/2018

Battery health prediction under generalized conditions using a Gaussian process transition model

Accurately predicting the future health of batteries is necessary to ens...
03/07/2022

Battery Cloud with Advanced Algorithms

A Battery Cloud or cloud battery management system leverages the cloud c...
12/19/2020

Analysis of NARXNN for State of Charge Estimation for Li-ion Batteries on various Drive Cycles

Electric Vehicles (EVs) are rapidly increasing in popularity as they are...
12/09/2020

Electric Vehicle Battery Remaining Charging Time Estimation Considering Charging Accuracy and Charging Profile Prediction

Electric vehicles (EVs) have been growing rapidly in popularity in recen...
03/08/2022

Second-life Lithium-ion batteries: A chemistry-agnostic and scalable health estimation algorithm

Battery state of health is an essential metric for diagnosing battery de...
12/07/2017

Gaussian Process Regression for In-situ Capacity Estimation of Lithium-ion Batteries

Accurate on-board capacity estimation is of critical importance in lithi...
06/12/2021

iThing: Designing Next-Generation Things with Battery Health Self-Monitoring Capabilities for Sustainable IoT in Smart Cities

An accurate and reliable technique for predicting Remaining Useful Life ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.