Review on State of Health estimation methodologies for lithium-ion batteries in the context of circular economy

2021 ◽  
Vol 32 ◽  
pp. 517-528
Author(s):  
Akash Basia ◽  
Zineb Simeu-Abazi ◽  
Eric Gascard ◽  
Peggy Zwolinski
Keyword(s):  
Lithium Ion Batteries ◽  
Circular Economy ◽  
Lithium Ion ◽  
State Of Health

Dynamic overcharge investigations of lithium ion batteries with different state of health

2021 ◽  
Vol 507 ◽  
pp. 230262
Author(s):  
Lei Feng ◽  
Lihua Jiang ◽  
Jialong Liu ◽  
Zhaoyu Wang ◽  
Zesen Wei ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
State Of Health

State of Health Estimation of Lithium-Ion Batteries Based on Fixed Size LS-SVM

2018 ◽  
Author(s):  
Zheng Chen ◽  
Xuelei Xia ◽  
Mengmeng Sun ◽  
Jiangwei Shen ◽  
Renxin Xiao
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
State Of Health ◽  
Fixed Size

A review on state of health estimation for lithium ion batteries in photovoltaic systems

2019 ◽  
Vol 2 ◽  
pp. 100028 ◽  
Author(s):  
Jinpeng Tian ◽  
Rui Xiong ◽  
Weixiang Shen
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Photovoltaic Systems ◽  
State Of Health

A multi-scale state of health prediction framework of lithium-ion batteries considering the temperature variation during battery discharge

2021 ◽  
Vol 42 ◽  
pp. 103076
Author(s):  
Jianfang Jia ◽  
Keke Wang ◽  
Yuanhao Shi ◽  
Jie Wen ◽  
Xiaoqiong Pang ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Temperature Variation ◽  
Lithium Ion ◽  
State Of Health ◽  
Multi Scale ◽  
Battery Discharge

Human-knowledge-augmented Gaussian Process Regression for State-of-Health Prediction of Lithium-ion Batteries with Charging Curves

2021 ◽  
pp. 1-10
Author(s):  
Quan Zhou ◽  
Chongming Wang ◽  
Zeyu Sun ◽  
Ji Li ◽  
Huw Williams ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Lithium Ion Batteries ◽  
Gaussian Process Regression ◽  
Lithium Ion ◽  
Learning Performance ◽  
Transport Systems ◽  
State Of Health ◽  
Human Knowledge ◽  
Voltage Profile ◽  
Inference System

Abstract Lithium-ion batteries have been widely used in renewable energy storage and electrified transport systems, and State-of-Health (SoH) prediction is critical for safe and reliable operation of the lithium-ion batteries. Following the standard routine which predicts battery SoH based on charging curves, a human-knowledge-augmented Gaussian process regression (HAGPR) model is newly proposed for SoH prediction by incorporating two promising artificial intelligence techniques, i.e., the Gaussian process regression (GPR) and the adaptive neural fuzzy inference system (ANFIS). Based on human knowledge on voltage profile during battery degradation, a ANFIS is developed for feature extraction that helps improve machine learning performance and reduce the need of physical testing. Then, the ANFIS is integrated with a GPR model to enable SoH prediction with the extracted feature from battery aging test data. With a conventional GPR model as the baseline, a comparison study is conducted to demonstrate the advantage and robustness of the proposed HAGPR model. It indicates that the proposed HAGPR model can reduce at least 12% root mean square error with 31.8% less battery aging testing compared to the GPR model.

scholarly journals Design of a Test Platform for the Determination of Lithium-Ion Batteries State of Health

2018 ◽  
Vol 141 (2) ◽  
Author(s):  
Jules-Adrien Capitaine ◽  
Qing Wang
Keyword(s):  
Lithium Ion Batteries ◽  
Storage System ◽  
Lithium Ion ◽  
Energy Storage System ◽  
State Of Health ◽  
Battery Cell ◽  
Test Platform ◽  
Battery Energy ◽  
Novel Design

This paper presents a novel design for a test platform to determine the state of health (SOH) of lithium-ion batteries (LIBs). The SOH is a key parameter of a battery energy storage system and its estimation remains a challenging issue. The batteries that have been tested are 18,650 Li-ion cells as they are the most commonly used batteries on the market. The test platform design is detailed from the building of the charging and discharging circuitry to the software. Data acquired from the testing circuitry are stored and displayed in LabVIEW to obtain the charging and discharging curves. The resulting graphs are compared to the outcome predicted by the battery datasheets, to verify that the platform delivers coherent values. The SOH of the battery is then calculated using a Coulomb counting method in LabVIEW. The batteries will be discharged through various types of resistive circuits, and the differences in the resulting curves will be discussed. A single battery cell will also be tested over 30 cycles and the decrease in the SOH will be clearly identified.

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