Author:
Zhu Tianye,Shi Zhihan,Zhang Tianyang,Zhang Guangming
Abstract
Abstract
For the construction of a second-order model, it is necessary to accurately identify five key parameters: R
0, Rp
, Cp
, Rp
, and Rd
. Traditional offline parameter identification methods rely solely on fitting the curve during the quiescent period after discharge to determine these parameters. This paper employs the Particle Swarm Optimization (PSO) algorithm combined with a second-order RC discrete model to fit the operating curve, thereby enhancing the model’s accuracy. In subsequent estimations, an adaptive Kalman filter is introduced to compare these two sets of parameters.
Reference10 articles.
1. Sizing and optimal operation of battery energy storage system for peak shaving application;Oudalov,2007
2. Economic analysis for demand-side hybrid photovoltaic and battery energy storage system [J];Su;IEEE Transactions on Industry Applications,2001
3. Battery Management System for SOC Estimation of Lithium-Ion Battery in Electric Vehicles: A Review;Shete
4. A Review of Lithium-Ion Battery Modeling [J];Yang;Energy Storage Science and Technology,2019
5. Analysis of Charging and Discharging Characteristics of Lithium Batteries Based on Second-Order RC Model [J];Shang;High Voltage Apparatus,2023