State of charge estimation for Li-ion batteries using neural network modeling and unscented Kalman filter-based error cancellation

By Wei He, Nicholas Williard, Chao-Shiou Chen, Michael G. Pecht. 2014

476 citations recorded by the source corpus.

Subfields: Automotive Engineering, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/d2271603c187a2f0de06aaf053a1687e76fc5054

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. Xuning Feng
2. Minggao Ouyang
3. Languang Lu
4. Xianke Lin
5. Xiaosong Hu
6. Jianqiu Li
7. Simona Onori
8. Carlos Vidal

In Control and Systems Engineering

1. Ali Emadi
2. Xiaosong Hu
3. Kwok Leung Tsui
4. Yunhong Che
5. Simona Onori
6. Carlos Vidal
7. Pawel Malysz
8. Dong Wang

Citations in the corpus

• Combined CNN-LSTM Network for State-of-Charge Estimation of Lithium-Ion Batteries (2019, background): “According to [16], [24], [25], it is sufficient to depict the temporal nonlinearity inside the battery with one LSTM layer.”
• The Co-estimation of State of Charge, State of Health, and State of Function for Lithium-Ion Batteries in Electric Vehicles (2018, methodology): “Common algorithms of SOC estimation include the coulombic counting approaches, the model-based open circuit voltage (OCV) approaches [2], the neural network model approaches [3], [4] etc.”
• Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art (2020, methodology): “The FNN presented in [15] is a supervised ML algorithm, which is characterized by the use of a known reference or target employed to calculate theML output estimation error.”
• Battery Health Prediction Using Fusion-Based Feature Selection and Machine Learning (2020, methodology): “the observation equation of the battery, and then the UKF was used to estimate the remaining capacity of the battery [80].”