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A reduced‐order electrochemical model for coupled prediction of state of charge and state of health of lithium ion batteries under constant current‐constant voltage charging conditions
Author(s) -
Dingari Naga Neehar,
Mynam Mahesh,
Rai Beena
Publication year - 2020
Publication title -
energy storage
Language(s) - English
Resource type - Journals
ISSN - 2578-4862
DOI - 10.1002/est2.194
Subject(s) - battery (electricity) , state of charge , constant current , state of health , voltage , lithium ion battery , trickle charging , constant (computer programming) , electrification , electrical engineering , control theory (sociology) , computer science , engineering , power (physics) , physics , thermodynamics , control (management) , artificial intelligence , programming language , electricity
Accurate and fast prediction of the remaining useful life of lithium (Li) ion batteries is an important requirement for successful electrification of automobiles. Consequently, there is a growing interest in the development of reduced‐order models. The existing reduced‐order electrochemical models can be used to predict battery performance (state of charge [SoC], terminal voltage) when the current through the battery is known a priori. Therefore these models cannot be used for studying the constant voltage (CV) mode of the constant current‐CV (CC‐CV) charging protocol, which is a common battery charging mechanism. In this work, we propose a reduced‐order electrochemical model to estimate the battery SoC under CC‐CV charging conditions, along with an analytical expression to approximate the CV mode charging time. We further propose a framework that accounts for the influence of the battery state of health (SoH) on the battery SoC during an operating cycle and vice‐versa. The proposed framework for estimating the battery SoC and SoH in a coupled manner shows good comparison with a first principles electrochemical model for CC‐CV charging conditions. This model can be used to study battery ageing and it can find applications in real‐time state estimation, charge protocol optimization, and battery design.