A Study of Battery State-of-health Tracking Using an Extended Kalman Filter and Randle\u27s Battery Circuit Model

Microgrids often require energy storage to improve the efficiency and economics of the available energy sources. Batteries are a common technology to fulfill these needs. Over time, a battery\u27s state-of-health (SoH) decreases, which reduces its energy storage capacity and can compromise the microgrid\u27s efficiency and reliability. The major requirements for a battery state-of-health measurement system include: 1) batteries must remain online during the state-of-health measurement; 2) minimal calibration required for each battery model; 3) state-of-health measurements can be obtained in less than two hours; and 4) state-of-health must be within 2% accuracy based the IEEE performance test. Traditional methods and some alternative methods require complex studies of the battery aging process or cannot be used when the batteries are under load. Extended Kalman Filters are able to track nonlinear systems and include unaccounted for system dynamics. The objectives of this research were to simulate the Extended Kalman Filter using Randle\u27s battery circuit model and Bhangu et al \u27s state space model in a computer environment; design the hardware and software for a state-of-health measurement system; test the state-of-health measurement system against the IEEE standard; and examine the application of this technique in an embedded system. A static resistance was initially used in the battery model, but it could only identify relative state-of-health between batteries at high states-of-charge. The state-of-health error was at best 10% error using a 29.5A constant current load and at 100% state-of-charge. The Extended Kalman Filter did not converge when the state-of-charge was at 50% or below. A dynamic internal resistance gave a best case error of 10% using a 29.5A load at 75% state-of-charge, but it was unstable. Future work should include tracking how the circuit model resistances change with state-of-charge and current, as well as techniques that can calculate the circuit resistances dynamically