A comparative study of commercial lithium ion battery cycle life in electric vehicle: Capacity loss estimation
By Xuebing Han, Minggao Ouyang, Languang Lu, Jianqiu Li. 2014
281 citations recorded by the source corpus.
Subfields: Automotive Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/a2162e854fde5e18510c66a16d2c926a8fb19280
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
- Saad Mekhilef
- Moh Yamani Idna Idris
In Electrical and Electronic Engineering
- Xuning Feng
- Minggao Ouyang
- Languang Lu
- Xiangming He
- Dongsheng Ren
- Xuebing Han
- Tey Kok Soon
- Saad Mekhilef
Citations in the corpus
- Online State-of-Health Estimation for Li-Ion Battery Using Partial Charging Segment Based on Support Vector Machine (2019, methodology): “Currently, a common approach for the SOH estimation relies on model-based prediction plus interval calibration [31].”
- Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries (2019, methodology): “Based on the interpolation method, the OCV for the battery model corresponding to the predicted SOC can be calculated from the SOC–OCV relationship [63,140,178].”
- Efficient Direct Recycling of Lithium-Ion Battery Cathodes by Targeted Healing (2020, background): “In general, a 20% capacity loss is considered to be the end of life for EVs batteries.(34) By considering secondary use, one can assume that 50% capacity decay might be the lower limit of the service life of an LIB for any applications.”