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Early Battery Performance Prediction for Mixed Use Charging Profiles Using Hierarchal Machine Learning
Author(s) -
Kunz M. Ross,
Dufek Eric J.,
Yi Zonggen,
Gering Kevin L.,
Shirk Matthew G.,
Smith Kandler,
Chen BorRong,
Wang Qiang,
Gasper Paul,
Bewley Randy L.,
Tanim Tanvir R.
Publication year - 2021
Publication title -
batteries and supercaps
Language(s) - English
Resource type - Journals
ISSN - 2566-6223
DOI - 10.1002/batt.202100079
Subject(s) - battery (electricity) , cycling , limiting , computer science , degradation (telecommunications) , constant (computer programming) , process (computing) , simulation , reliability engineering , machine learning , engineering , mechanical engineering , telecommunications , power (physics) , physics , archaeology , quantum mechanics , history , programming language , operating system
A key step limiting how fast batteries can be deployed is the time necessary to provide evaluation and validation of performance. Using data analysis approaches, such as machine learning, the validation process can be accelerated. However, questions on the validity of projecting models trained on limited data or simple cycling profiles, such as constant current cycling, to real‐world scenarios with complex loads remains. Here, we present the ability to predict performance with less than 1.2 % mean absolute percent error when trained on cells aged using complex electric vehicle discharge profiles, and either AC Level 2 charge or DC Fast charge profiles, using only the first 45 cycles, namely 5 % of the total testing time. While error is low across the projections, this study also highlights that battery lifetime analysis using only cycling data may not extrapolate safely to certain real‐world conditions due to the impact of calendar degradation.