State of Health Estimation Methods for Lithium-Ion Batteries

Author:

Nuroldayeva Gulzat12,Serik Yerkin12,Adair Desmond12ORCID,Uzakbaiuly Berik123ORCID,Bakenov Zhumabay123

Affiliation:

1. Institute of Batteries LLC, Kabanbay Batyr Ave 53, S4, 511, Nur-Sultan, Kazakhstan

2. Nazarbayev University, Kabanbay Batyr Ave 53, Nur-Sultan, Kazakhstan

3. National Laboratory Astana, Kabanbay Batyr Ave 53, S2, Nur-Sultan, Kazakhstan

Abstract

Contemporary lithium-ion batteries (LIBs) are one of the main components of energy storage systems that need effective management to extend service life and increase reliability and safety. Their characteristics depend highly on internal and external conditions (ageing, temperature, and chemistry). Currently, the state of batteries is determined using two parameters: the state of charge (SOC) and the state of health (SOH). Applying these two parameters makes it possible to calculate the expected battery life and a battery’s performance. There are many methods for estimating the SOH of batteries, including experimental, model-based, and machine learning methods. By comparing model-based estimations with experimental techniques, it can be concluded that the use of experimental methods is not applicable for commercial cases. The electrochemical model-based SOH estimation method clearly explains processes in the battery with the help of multidifferential equations. The machine learning method is based on creating a program trained to predict the battery’s state of health with the help of past ageing data. In this review paper, we analyze the research available in the literature in this direction. It is found that all methods used to assess the SOH of an LIB play an essential role, and each method has its pros and cons.

Funder

MDDIAI Republic of Kazakhstan

Publisher

Hindawi Limited

Subject

Energy Engineering and Power Technology,Fuel Technology,Nuclear Energy and Engineering,Renewable Energy, Sustainability and the Environment

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