A Quantum Particle Swarm Optimization Extended Kalman Quantum Particle Filter approach on state of charge estimation for lithium-ion battery
Journal of Energy Storage, ISSN: 2352-152X, Vol: 100, Page: 113677
2024
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Studies from Tsinghua University Provide New Data on Energy Storage (A Quantum Particle Swarm Optimization Extended Kalman Quantum Particle Filter Approach On State of Charge Estimation for Lithium-ion Battery)
2024 OCT 18 (NewsRx) -- By a News Reporter-Staff News Editor at Energy Daily News -- Fresh data on Energy - Energy Storage are presented
Article Description
The accurate estimation of battery state of charge serves as the foundation for estimating state of health, remaining useful life, and safety, making it an indispensable part of battery management system. Based on the lithium-ion battery parameter identification using quantum particle swarm optimization, this paper presents a state of charge estimation method utilizing the Extended Kalman Quantum Particle Filter. A Quantum Particle Swarm Optimization algorithm is used for second-order equivalent circuit model parameter identification. By combining Extended Kalman Filter with Particle Filter and employing Grover search algorithm for resampling of quantized particles, the Extended Kalman Quantum Particle Filter is designed for estimation. Validation is conducted under standard operating conditions, and the Quantum Particle Swarm Optimization Extended Kalman Quantum Particle Filter algorithm demonstrates better accuracy and adaptability compared to Extended Kalman Filter, Extended Kalman Particle Filter and other popular algorithms under FUDS, UDDS and DST working conditions by two types of battery. Both theoretical and experimental results indicate good performance of Quantum Particle Swarm Optimization Extended Kalman Quantum Particle Filter in state of charge estimation.
Bibliographic Details
Elsevier BV
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