Fault and defect diagnosis of battery for electric vehicles based on big data analysis methods

By Yang Zhao, Peng Liu, Zhenpo Wang, Lei Zhang, Jichao Hong. 2017

325 citations recorded by the source corpus.

Subfields: Automotive Engineering, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/81470f2cd2e9f9094d9031e0c4015b7b82fc3c2b

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. Xianke Lin
3. Simona Onori
4. Xiaosong Hu
5. Kai Zhang
6. Satadru Dey

In Control and Systems Engineering

1. Kailong Liu
2. Xiaosong Hu
3. Simona Onori
4. Kai Zhang
5. Satadru Dey
6. Xianke Lin

Citations in the corpus

• Advanced Fault Diagnosis for Lithium-Ion Battery Systems: A Review of Fault Mechanisms, Fault Features, and Diagnosis Procedures (2020, methodology): “Compared with ANN, SVM has better generalization ability and is applicable to the small sample cases [73], which is especially suitable for the LIBS with a limited amount of fault data.”