A new neural network model for the state-of-charge estimation in the battery degradation process

By Liuwang Kang, Xuan Zhao, Jian Ma. 2014

315 citations recorded by the source corpus.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/102d33a9d916720a7311699b9f161604004dd613

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. Z. Ye
2. Qingqing Zhai
3. Kwok Leung Tsui
4. Fangfang Yang

In Electrical and Electronic Engineering

1. Kwok Leung Tsui
2. Z. Ye
3. Fangfang Yang

Citations in the corpus

• RUL Prediction of Deteriorating Products Using an Adaptive Wiener Process Model (2017, background): “2684821 [13], artificial neural networks [14], and naı̈ve Bayes models [15], to capture the degradation pattern from measured data.”
• Combined CNN-LSTM Network for State-of-Charge Estimation of Lithium-Ion Batteries (2019, methodology): “The commonly used machine learning methods include artifi-cial neural networks [13], fuzzy logic [14], support vector machine [15], and so on.”