The Co-estimation of State of Charge, State of Health, and State of Function for Lithium-Ion Batteries in Electric Vehicles
By Ping Shen, Minggao Ouyang, Languang Lu, Jianqiu Li, Xuning Feng. IEEE Transactions on Vehicular Technology, 2018
449 citations recorded by the source corpus.
Subfields: Automotive Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/bac4ea67c8449001aebdd4b7a6039d8759b71c18
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
- SoC Estimation for Lithium-ion Batteries: Review and Future Challenges (2017, methodology): “in [111] use a single forgetting factor RLS like that presented in Figure to obtain the second-order ECM parameters and an EKF for the final SoC estimation procedure.”
- Online State-of-Health Estimation for Li-Ion Battery Using Partial Charging Segment Based on Support Vector Machine (2019, methodology): “[24], 5% error in the SOH estimation will lead to a systematic error of 1% in the SOC estimation.”
- Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries (2019, methodology): “Table 2 Description of different EECM models [33,39,69,136,137,150,151].”