This paper proposes an adaptive sensorless measurement technique for internal temperature of lithium-ion (Li-ion) batteries. The proposed technique is based on measuring the impedance-phase of the battery in real time by a simple search algorithm. The proposed technique then identifies the zero-crossing frequency at which the phase of the impedance is zero. From the value of the zero-crossing frequency, the cell's internal temperature is estimated. Details on the proposed technique followed by experimental verification using a 3.8V/2600mAh Samsung Li-ion battery cell at temperatures between -20° C and +50° C are presented.

The worldwide electricity supply network has recently experienced a huge rate of solar photovoltaic penetration. Grid-connected photovoltaic (PV) systems range from smaller custom built-in arrays to larger utility power plants. When the size and share of PV systems in the energy mix increases, the operational complexity and reliability of grid stability also increase. The growing concern about PV plants compared to traditional power plants is the dispersed existence of PV plants with millions of generators (PV panels) spread over kilometers, which increases the possibility of faults occurring and associated risk. As a result, a robust fault diagnosis and mitigation framework remain a key component of PV plants. Various fault monitoring and diagnostic systems are currently being used, defined by calculation of electrical parameters, extracted electrical parameters, artificial intelligence, and thermography. This article explores existing PV fault diagnostic systems in a detailed way and addresses their possible merits and demerits.

A concentrated photovoltaic system is evaluated as a thermal energy source employing phase change material to meet the domestic water heating demand. A paraffin wax-based phase change material is selected with a 58 °C melting point to store enough thermal energy to match the hot water demand in the buildings. The energy performance of the concentrated photovoltaics containing phase change materials is compared to that of the reference to determine the increased energy outputs due to the heat removal by the material. The concentrated photovoltaics-phase change material achieved 30% higher energy output compared to the reference concentrated photovoltaic, thus providing a strong justification for the improved thermal management design. An enthalpy-based thermal model is developed to compare the experimental results with model predictions, confirming a reasonable agreement between the results. The model is used determine the optimum melting point and container size for different phase change materials under different radiation concentrations for the hot climate of the United Arab Emirates.

The share of photovoltaic (PV) power generation in the energy mix is increasing at a rapid pace with dramatically increasing capacity addition through utility-scale PV power plants globally. As PV plants are forecasted to be a major energy generator in the future, their reliable operation remains of primary concern due to a possibility of faults in a tremendously huge number of PV panels involved in power generation in larger plants. The precise detection of nature and the location of the faults along with a prompt remedial mechanism is deemed crucial for smoother power plant operation. The existing fault diagnostic methodologies based on thermal imaging of the panels as well as electrical parameters through inverter possess certain limitations. The current article deals with a novel fault diagnostic technique based on PV panel electrical parameters and junction temperatures that can precisely locate and categorize the faults. The proposed scheme has been tested on a 1.6 kW photovoltaic system for short circuit, open circuit, grounding, and partial shading faults. The proposed method showed improved accuracy compared to thermal imaging on panel scale fault detection, offering a possibility to adapt to the PV plant scale.