Saeed Sepasi

SOC estimation for aged lithium-ion batteries using model adaptive extended Kalman filter

Rechargeable batteries as an energy source in electric vehicles (EVs), hybrid electric vehicles (HEVs) and smart grids are receiving more attention with the worldwide demand for reduction of greenhouse gas emission. In all of these applications for secondary batteries, the battery management system (BMS) needs to have an accurate inline estimation of state of charge (SOC) of each individual cell in the battery pack. Yet, this estimation is still difficult, especially after substantial aging of batteries. This paper presents a model adaptive extended Kalman filter (MAEKF) method to estimate SOC of Li-ion batteries. This method uses an optimization algorithm to update the EKF model parameters during a discharge period. State of health (SOH) information would be updated while the battery is charged/discharged, (aged). The effectiveness of the proposed method has been verified based on data acquired from a LiFePO4 battery.

Universal state of charge estimator for battery packs of battery energy storage systems

Battery energy storage system (BESS) is an ideal solution for managing intermittent renewable sources such as photo voltaic (PV) panels and wind turbines connected to grids or micro grids. Their optimized performance is very dependent on accurate estimation of BESSs state of charge (SOC). This accurate estimation is vital for the operating scheme decisions of any BESS. This paper presents a universal method to accurately estimate the SOC for battery packs used in BESSs. Online estimated model parameters are used to update electrical melectrical modelodel of each single cell in the battery pack of the BESS. The algorithms low estimation error and reduced computational requirements, makes it practical for on board estimations. SOC estimation error along with voltage estimation error for a pack of Li-ion batteries are presented to show the accuracy of the algorithm.

A coordinated approach for frequency control of zero emission based smart PV-wind-battery power system

Power demand of isolated communities or islands primarily relies on heavy oil based diesel generator systems where these fossil fuel based power generators are not only costly but also create adverse effect on the environment. Photovoltaic generator system (PV system), wind energy conversion system (WECS) and battery energy storage system (BESS) are clean sources of alternative energy. These energy sources can be the alternative of diesel generator based power system for an isolated communities such as islands. However, both PV systems and WECSs are uncertain and fluctuating sources of energy causing power fluctuations and frequency deviations. This paper proposes a coordinated approach of the frequency control for a zero emission based small power system with renewable energy penetration. PV system, WECS and BESS have been utilized to control frequency of the proposed zero emission based power system. Efficacy of the proposed frequency control system is shown with simulations results.