Lithium ferro phosphate (LiFePO4) battery soc estimation using particle filter (PF)

• Main Author: Md. Siam, Noor Iswaniza
• Format: Thesis
• Language: English
• Published: 2020
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.utm.my/id/eprint/93013/1/NoorIswanizaMSKE2020.pdf; http://eprints.utm.my/id/eprint/93013/; http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:135884

State of charge (SOC) is a vital indicator in the battery management system (BMS) that monitoring the charging and discharging operation of the battery pack. It is crucial for optimizing the performance and extend the lifespan of battery for EVs/HEVs applications. Lithium Ferro Phosphate (LiFePO4) is one of the battery technology that has good performance, excellent life cycles, fast charging with lesser time as compared to other batteries. Many uncertainties and noises such as fluctuating current, sensor measurement, ambient temperature effect, and calibration error pose a challenge to determine the accuracy of SOC estimation. The objective of this research is to develop a battery model for LiFePO4 battery by using Particle Filter (PF) method to determine the SOC estimation of the lithium-ion battery precisely. The LiFePO4 battery modeling carried out using MATLAB software. Constant discharge test (CDT) is performed to measure and study the usable capacity of the battery. Then, pulse discharge test (PDT) is used to extract the dynamic characteristics of battery and calculate the battery model parameters. Three parallel RC battery model has been chosen for this study due to high accuracy is needed. The proposed PF implements Recursive Bayesian Filter by Monte Carlo sampling which is robust for non-linear and/or non-Gaussian distributions. The simulation result is compared with experimental data of dynamic behaviors of LiFePO4battery for verification purpose. Then, the performance of the algorithm which is in PF is compared to experimental data outcome and Extended Kalman Filter (EKF) method. An accurate SOC estimator with minimum error compared to EKF has been obtained.