LITHIUM-ION BATTERY DEGRADATION EVALUATION THROUGH BAYESIAN NETWORK METHOD FOR RESIDENTIAL ENERGY STORAGE SYSTEMS

In recent years, a great importance has been given to hybrid systems of energy generators and energy storages. This article presents the results of our research aimed at checking the possibility of connecting a photovoltaic (PV) module and a lithium-ion battery (LIB), using a simplified control module towards a cheap and efficient system. The photovoltaic modules based on crystalline silicon solar cells, tempered glass as the front layer and ethylene-vinyl acetate (EVA) copolymer as encapsulation material are the most popular type in the industry. The disadvantage of such module type is the high weight of about 15 kg/m2. The weight of PV module used in the presented energy storage system is twice as small. This new type of PV module is based on treated poly(methyl methacrylate) (PMMA) as back sheet; high transparent foil as front sheet. Changing glass layer to PMMA requires additional modification of the lamination process parameters and EVA polymer type. For this reason, an EVA polymer with reduced crosslinking temperature was used in most cases; the voltage obtained from solar panels is significantly different from the one required by battery system. Hence, voltage converters (step-up or step-down) are needed. The use of a voltage stabilizing converter (which is a kind of electrical buffer) between the solar cell and lithium-ion battery can in some cases replace the battery overcharge protection system. However, an indispensable element is the system protecting the battery from excessive discharge. The voltage converter permits direct connection between the electricity storage and power supply, which current-voltage parameters do not match. The converter’s task is to change the value of current and voltage in a way that meets the requirements of the powered receiver, minimizing power losses, increasing the whole system efficiency. Photovoltaic parameters of the energy storage systems were examined in laboratory and real conditions.

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

Batteries ◽

10.3390/batteries5030050 ◽

2019 ◽

Vol 5 (3) ◽

pp. 50

Author(s):

Qamar Navid ◽

Ahmed Hassan

Keyword(s):

Energy Storage ◽

Lithium Ion Battery ◽

Performance Indicator ◽

Storage Systems ◽

Renewable Energy Sources ◽

Lithium Ion ◽

State Of Charge ◽

Accurate Estimation ◽

Model Parameters ◽

Energy Storage Systems

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.

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