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Abstract
The fluctuating nature of power produced by renewable energy sources results in a substantial supply and demand mismatch. To curb the imbalance, energy storage systems comprising batteries and supercapacitors are widely employed. However, due to the 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 a lithium-ion battery and capacitor/supercapacitor under dynamic conditions to utilize the storage capacity to a fuller extent. The grey box modeling approach involving the chemical and electrical energy transfers/interactions governed by ordinary differential equations was developed in MATLAB. The model parameters were extracted from experimental data employing regression techniques. The state-of-charge (SoC) of the battery was predicted by employing the extended Kalman (EK) estimator and the unscented Kalman (UK) estimator. The model was eventually validated via loading profile tests. As a performance indicator, the extended Kalman estimator indicated the strong competitiveness of the developed model with regard to tracking of the internal states (e.g., SoC) which have first-order nonlinearities.
Highlights
Energy storage is a key component of renewable energy systems in terms of ensuring reliable and sustained energy supply
Chemical energy storage involves the conversion of electrical energy into electrochemical energy for intermediate storage, and it is classified as batteries, fuel cells, and electrochemical capacitors [1]
Several experiments a capacity openand circuit voltage (OCV) (voltage state-of-charge test (OCV–SoC), anddepends loadingon profile test were conducted at various temperatures under no load conditions generally the battery design and operating temperature) and from 5 °C to 25test
Summary
Energy storage is a key component of renewable energy systems in terms of ensuring reliable and sustained energy supply. Due to the delicate nature of miniaturized smart low-power electrochemical components, the precise estimation of battery attributes such as state-of-charge (SoC), state-of-health (SoH), internal. Due to the delicate nature of miniaturized smart low-power electrochemical components, the precise estimation of battery attributes such as state-of-charge (SoC), state-of-health (SoH), internal resistance, and temperature dependency is crucial for effective power management [4,5]. It is not considered to be the most crucial parameter of those that govern the power flow It is not measurable and requires estimation practiced through several methodologies. The black box technique affords pragmatic simplification and is widely adopted These models are extremely simplified, involve fewer parameters, and afford ease of widely adopted. The techniques available for estimating the SoC of a battery are classified as the direct discharge of the battery. Estimators for parameter identification to estimate the SoC (UK)battery estimators for Kalman parameter identification of the Li-ion and capacitor/supercapacitor [24].to estimate the SoC of the Li-ion battery and of the Li-ion battery and capacitor/supercapacitor [24]
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