A Dynamic High-Order Equivalent Modeling of Lithium-Ion Batteries for the State-of-Charge Prediction Based on Reduced-Order Extended Kalman Filtering Algorithm

• DOI: 10.7176/jetp/11-3-03
• Publication Date: 2021-07-02T14:58:36Z
• Authors: Emmanuel Appiah; Shunli Wang; Chuanyun Zou; Hongying Zhang; Etse Dablu Bobobee; Manasah Musariri; Islam Md Monirul
• Keywords: State of charge

Detection of battery power has always been the core management system electric vehicles, and fast accurate estimation charged state can guarantee safe operation vehicles. The key to improving state-of-charge is an appropriate model establishment coupled with a suitable algorithm. This research seeks adopt accomplish lithium-ion based on Gaussian function build up open-circuit voltage A reduced-order extended Kalman filtering algorithm proposed hybrid pulse characterization parameter identification estimate state-of-charge. model’s parameters in different points are calculated through battery’s charge discharge process; 2RC modeling correction method Reduced-order filter used separately High-order equivalent modeling. Experimental results show that above achieve more accurately conveniently, providing certain reference value for rational distribution batteries. maximum error established high-order using less than 1.85%. REKF achieved 0.0409V average 0.0299V therefore satisfy accuracy application needs. Keywords: Lithium-ion battery; state-of-charge; modeling; voltage; identification; DOI: 10.7176/JETP/11-3-03 Publication date: June 30 th 2021