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An Accurate and Precise Grey Box Model of a Low Power Lithium-Ion Battery and Capacitor/Super-Capacitor for Accurately Estimation of State of Charge

Qamar Navid

The fluctuating nature of power produced by renewable energy sources results in a substantial supply and demand mismatch. In an attempt to curb the imbalance, energy storage systems comprising batteries and super capacitors are widely employed. However, due to variety of operational conditions, the performance prediction of the energy storage systems entails a substantial complexity that leads to capacity utilization issues. The current article attempts to precisely predict the performance of lithium-ion battery and capacitor/super-capacitor under dynamic conditions to utilize storage capacity to fuller extent. Grey box modelling approach that involves the chemical and electrical energy transfers/interactions governed by ordinary differential equations is developed in MATLAB. The model parameters are extracted from experimental data employing regression technique. The state of charge (SoC) of the battery is predicted by employing extended Kalman estimator, unscented Kalman estimator. The model is eventually validated through the loading profile tests. Relaying on the performances, extended Kalman estimator indicates much  competitiveness to the developed model (in tracking the internal states e.g. SoC) which have nonlinarites of first order.