A novel state of health estimation method of Li-ion battery using group method of data handling

By Ji Wu, Yujie Wang, Xu Zhang, Zonghai Chen. 2016

207 citations recorded by the source corpus.

Subfields: Automotive Engineering, Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/1e4e3a258cdbf725537f8136260ec31b7448b5ed

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. Kailong Liu
2. Guangcai Zhao
3. Yandan Tan
4. Yi Li
5. Maitane Berecibar
6. Carlos Vidal
7. Pawel Malysz
8. Daniel-Ioan Stroe

In Control and Systems Engineering

1. Guangcai Zhao
2. Yandan Tan
3. Kailong Liu
4. Yi Li
5. Jingwen Wei
6. Remus Teodorescu
7. Ali Emadi
8. Zonghai Chen

In Electrical and Electronic Engineering

1. Kailong Liu
2. Guangcai Zhao
3. Yandan Tan
4. Carlos Vidal
5. Pawel Malysz
6. Yi Li
7. Daniel-Ioan Stroe
8. Remus Teodorescu

Citations in the corpus

• Remaining Useful Life Prediction and State of Health Diagnosis for Lithium-Ion Batteries Using Particle Filter and Support Vector Regression (2018, background): “Digital Object Identifier 10.1109/TIE.2017.2782224 its remaining useful life (RUL) and diagnose its state-of-health (SOH), due to the electrical properties, safety and stability alterations of LIB would change with battery RUL and SOH [3], [4].”
• Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art (2020, methodology): “The authors in [55] have established that the geometric variation of the battery terminal voltage can be correlated to the battery SOHr.”
• Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019, methodology): “Processed external features are normally extracted from differential charging curves under constant current rate, such as IC/DV curves [77,78] and voltage gradient curves (dV/dt) [79,80].”
• Transfer Learning With Long Short-Term Memory Network for State-of-Health Prediction of Lithium-Ion Batteries (2020, methodology): “[12] utilized the group method of data handling (GMDH) to establish the relationship between the differential geometric properties and SOH to predict the current state.”
• An Overview and Comparison of Online Implementable SOC Estimation Methods for Lithium-Ion Battery (2018, methodology): “Methods, such as ANN [52], [55]–[57], support vector machine (SVM) [58], extreme learning machine (ELM) [59], multivariate adaptive regression splines (MARS) [9], [60], etc.”