State of Charge and State of Health Estimation for Lithium Batteries Using Recurrent Neural Networks
By Hicham Chaoui, Chinemerem Christopher Ibe-Ekeocha. IEEE Transactions on Vehicular Technology, 2017
453 citations recorded by the source corpus.
Subfields: Automotive Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/bdac73fe565b15aa84a11c77c02e1f3ad30187dc
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
- Xuning Feng
- Minggao Ouyang
- Languang Lu
- Xiangming He
- Dongsheng Ren
- Xuebing Han
- David A. Howey
- P. Kollmeyer
In Electrical and Electronic Engineering
- Xuning Feng
- Minggao Ouyang
- Languang Lu
- Xiangming He
- Dongsheng Ren
- Xuebing Han
- David A. Howey
- P. Kollmeyer
Citations in the corpus
- Online capacity estimation of lithium-ion batteries with deep long short-term memory networks (2021, methodology): “Recurrent neural networks (RNNs) have also been used to process an input time sequence and obtain the nominal capacity [26,27].”
- Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art (2020, methodology): “In [50], the authors developed a dynamically driven recurrent network (DDRN) based on a nonlinear autoregressive with exogenous input (NARX) neural network architecture.”
- Online State-of-Health Estimation for Li-Ion Battery Using Partial Charging Segment Based on Support Vector Machine (2019, background): “The states of Li-ion batteries that require accurate online estimation include the state-of-charge (SOC) [5]–[12], state-of-health (SOH) [13], [14], state-of-energy (SOE) [15]–[17], and state-of-power (SOP) [18]–[20].”