Analysis and modeling of lithium iron phosphate (LiFePO4) batteries for state of health estimation

Abstract

Li-ion batteries have been chosen as the best candidate for portable, mobile and high power applications due to their high energy densities, long lifetimes, and high efficiencies compared to other battery types, like lead-acid and nickel-based batteries recently. On the other hand, these batteries are prone to failure due to charge imbalance in batteries linked in series or parallel, which may be catastrophic. Therefore, they must be carefully monitored in real-time. So, the Li-ion cells chemistry batteries have exceptional performance compared to other chemistries, but only if treated well (cell balancing, protection from overcharge, over-discharge, and short circuit conditions). Despite the advantages of lithium-ion batteries, these batteries still have a short life due to particular aging processes inside the batteries and improper cell balancing, which become evident under defined conditions. Reliable battery calendar ageing prediction, on the other hand, remains a crucial yet complex issue for improving the performance of related storage devices. So, manufacturers and researchers need to investigate the cell balancing process and aging processes in lithium-ion batteries to determine the aging effects that occur and the factors that cause a rapid decrease in their lifetimes. Nowadays, Lithium iron phosphate (LiFePO4) based chemistry batteries are considered to be one of the most valuable lithium-ion batteries in the market because of their high energy density, lack of memory effect, lower self-discharge, long lifetime, large cycle life number, inherently safe cathode structure under critical conditions, and non polluting characteristics. In this thesis, 3S-1P battery pack cell balancing system is implemented by using passive cell balancing technique including the thermal effects and an aging prediction model has been developed for lithium iron phosphate (LiFePO4) batteries using MATLAB/Simulink©, which has been able to predict the aged effect in terms of capacity fade and internal resistance increase. Thevenin equivalent circuit model is utilized and SOH is estimated. Parameter estimation is done using soft computing technique. Overall, different models of SOH estimations are studied and reviewed. The results are developed in MATLAB Simscape and further extended in MATLAB scripts. Possible effects of cell balancing and internal resistance has been discussed in the conclusion.