Adaptive unscented Kalman filter based state of energy and power capability estimation approach for lithium-ion battery
By Weige Zhang, W. Shi, Zeyu Ma. 2015
170 citations recorded by the source corpus.
Subfields: Automotive Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/4b9d7fe16c426f54bb12762c01c51cdfe6634f81
How researchers see this paper
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In Automotive Engineering
- Moh Yamani Idna Idris
- Najoua Essoukri Ben Amara
- Saad Mekhilef
- Prashant Shrivastava
- Bilal Manai
- Sabeur Jemmali
- Tey Kok Soon
- Ines Baccouche
In Electrical and Electronic Engineering
- Moh Yamani Idna Idris
- Najoua Essoukri Ben Amara
- Saad Mekhilef
- Prashant Shrivastava
- Bilal Manai
- Sabeur Jemmali
- Tey Kok Soon
- Ines Baccouche
Citations in the corpus
- Improved OCV Model of a Li-Ion NMC Battery for Online SOC Estimation Using the Extended Kalman Filter (2017, methodology): “Different variants of Kalman filtering techniques like the linear Kalman filter (KF) [38], the extended Kalman filter (EKF) [35], the unscented Kalman filter (UKF) [50] and the sigma-point Kalman filter (SPKF) [51] have been used to estimate the SOC based on different battery equivalent models.”
- Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries (2019, methodology): “[197], AUKF was utilised to build the joint SOP and SOE estimator; the estimation error was less than 2% for assumed erroneous initial conditions.”