State of Charge Estimation of Lithium Battery Model Based on Improved Extended Kalman Filter Algorithm

Energy storage battery pack is the core component of the seismic station energy supply, to ensure the seismic station green economy, safe and stable operation, improve the lithium battery charge state accurate estimation, is proposed based on adaptive particle swarm improved estimate battery State-o...

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Vydáno v:2024 2nd International Conference on Signal Processing and Intelligent Computing (SPIC) s. 616 - 619
Hlavní autoři: Feng, Mengru, Shi, Zhengang
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 20.09.2024
Témata:
Accuracy
earthquake measuring platform station
Estimation
extended kalman filter
Kalman filters
Lithium batteries
lithium ion battery
Noise
Noise measurement
Optimization
particle swarm algorithm
Particle swarm optimization
Signal processing algorithms
SOC estimation
State of charge
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Shrnutí:Energy storage battery pack is the core component of the seismic station energy supply, to ensure the seismic station green economy, safe and stable operation, improve the lithium battery charge state accurate estimation, is proposed based on adaptive particle swarm improved estimate battery State-of-Charge extension Kalman filter algorithm, through the forgetting factor recurrence multiplication online identification battery model parameters based on the SOC estimate, the Adaptive Mixovariation Particle Swarm Optimization algorithm optimization Extended Kalman Filter algorithm noise variance matrix, and the system and the measurement noise matrix optimization, further improve the estimation accuracy of SOC. The results show that the AMPSO-EKF algorithm can accurately identify the battery model parameters and the SOC values, and can correct the initial error of the state variable well.
DOI:10.1109/SPIC62469.2024.10691552