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

scholarly journals State of health prediction of medical lithium batteries based on multi-scale decomposition and deep learning

2020 ◽  
Vol 12 (5) ◽  
pp. 168781402092320
Author(s):  
Chang Chun Liu ◽  
Tao Wu ◽  
Cheng He
Keyword(s):  
Lithium Ion Batteries ◽  
Short Term Memory ◽  
Lithium Ion ◽  
Main Trend ◽  
Prediction Methods ◽  
State Of Health ◽  
Short Term ◽  
Boltzmann Machines ◽  
Multi Scale ◽  
Long Short Term Memory

To guarantee rescue time and reduce medical accidents, a health degradation prediction model of medical lithium-ion batteries based on multi-scale deep neural network was proposed aiming at the problems of poor model adaptability and inaccurate prediction in current state of health prediction methods. The collected energy data of medical lithium-ion batteries were decomposed into main trend data and fluctuation data by ensemble empirical mode decomposition and correlation analysis. Then, deep Boltzmann machines and long short-term memory were used to model the main trend and fluctuation data, respectively. The predicting outcomes of deep Boltzmann machines and long short-term memory were effectively integrated to obtain the health predicted results of medical lithium-ion battery. The experimental results show that the method can effectively fit the health trend of medical lithium-ion batteries and obtain accurate state of health prediction results. The performance of the method is better than other typical prediction methods.

State-of-Charge and State-of-Health Lithium-Ion Batteries’ Diagnosis According to Surface Temperature Variation

2016 ◽  
Vol 63 (4) ◽  
pp. 2391-2402 ◽  
Author(s):  
Asmae El Mejdoubi ◽  
Amrane Oukaour ◽  
Hicham Chaoui ◽  
Hamid Gualous ◽  
Jalal Sabor ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Lithium Ion Batteries ◽  
Temperature Variation ◽  
Lithium Ion ◽  
State Of Charge ◽  
State Of Health ◽  
Surface Temperature Variation

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

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.

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