An improved robust function correction-adaptive extended kalman filtering algorithm for SOC estimation of lithium-ion batteries
Author:
Affiliation:
1. Southwest University of Science and Technology,School of Information Engineering,Mianyang,China,621010
2. Robert Gordon University,School of Pharmacy and Life Sciences,Aberdeen,UK,AB10-7GJ
3. Yalong River (Yajiang) New Energy Co., Ltd,China
Funder
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10349228/10349229/10349263.pdf?arnumber=10349263
Reference12 articles.
1. State of charge estimation by finite difference extended Kalman filter with HPPC parameters identification
2. Pure hardware design and implementation on FPGA of an EKF based accelerated SoC estimator for a lithium‐ion battery in electric vehicles
3. An improved variable forgetting factor recursive least square-double extend Kalman filtering based on global mean particle swarm optimization algorithm for collaborative state of energy and state of health estimation of lithium-ion batteries
4. A novel non-experiment-based reconstruction method for the relationship between open-circuit-voltage and state-of-charge/state-of-energy of lithium-ion battery
5. An improved adaptive cubature H‑infinity filter for state of charge estimation of lithium‑ion battery
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