• Energy Research

Effective thermal management is essential for ensuring the safety, performance, and longevity of lithium-ion batteries across diverse applications, from electric vehicles to energy storage systems. This paper presents a thorough review of thermal management strategies, emphasizing recent advancements and future prospects. The analysis begins with an evaluation of industry-standard practices and their limitations, followed by a detailed examination of single-phase and multi-phase cooling approaches. Successful implementations and challenges are discussed through relevant examples. The exploration extends to innovative materials and structures that augment thermal efficiency, along with advanced sensors and thermal control systems for real-time monitoring. The paper addresses strategies for mitigating the risks of overheating and propagation. Furthermore, it highlights the significance of advanced models and numerical simulations in comprehending long-term thermal degradation. The integration of machine learning algorithms is explored to enhance precision in detecting and predicting thermal issues. The review concludes with an analysis of challenges and solutions in thermal management under extreme conditions, including ultra-fast charging and low temperatures. In summary, this comprehensive review offers insights into current and future strategies for lithium-ion battery thermal management, with a dedicated focus on improving the safety, performance, and durability of these vital energy sources.

This paper deals with the application and comparison of several metaheuristic techniques to optimize the placement and supply area of biomass-fueled power plants. Both, trajectory and population-based methods are applied for our goal. In particular, two well-known trajectory method, such as Simulated Annealing (SA) and Tabu Search (TS), and two commonly used population-based methods, such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) are hereby considered. In addition, a new binary PSO algorithm has been proposed, which incorporates an inertia weight factor, like the classical continuous approach. The fitness function for the metaheuristics is the profitability index, defined as the ratio between the net present value and the initial investment. In this work, forest residues are considered as biomass source, and the problem constraints are: the generation system must be located inside the supply area, and its maximum electric power is 5 MW. The comparative results obtained by all considered metaheuristics are discussed. Random walk has also been assessed for the problem we deal with.

Air–water heat pumps are gaining interest in modern architectures, and they are a suitable option as a replacement for fossil fuel-based heating systems. These systems consume less electricity by combining solar panels, a heat pump, thermal storage, and a smart control system. This study was applied to a completely ecological rural health sub-center built on the basis of recycled bottles, and that, for its regular operation, requires an energy system according to the needs of the patients in the rural community. Detailed analyses were performed for heating and hot water preparation in two scenarios with different conditions (standard and fully integrated). From a technical perspective, different strategies were analyzed to ensure its functionality. If the photovoltaic system is sized to achieve advanced control, the system can even operate autonomously. However, due to the need to guarantee the energy efficiency of the center, the analyses were performed with a grid connection, and it was determined that the photovoltaic system guarantees at least two-thirds of the energy required for its autonomous operation. The results show that the system can operate normally thanks to the optimal size of the photovoltaic system, which positively influences the rural population in the case under analysis.

Recently, fast uptake of renewable energy sources (RES) in the world has introduced new difficulties and challenges; one of the most important challenges is providing economic energy with high efficiency and good quality. To reach this goal, many traditional and smart algorithms have been proposed and demonstrated their feasibility in obtaining the optimal solution. Therefore, this paper introduces an improved version of Bonobo Optimizer (BO) based on a quasi-oppositional method to solve the problem of designing a hybrid microgrid system including RES (photovoltaic (PV) panels, wind turbines (WT), and batteries) with diesel generators. A comparison between traditional BO, the Quasi-Oppositional BO (QOBO), and other optimization techniques called Harris Hawks Optimization (HHO), Artificial Electric Field Algorithm (AEFA) and Invasive Weed Optimization (IWO) is carried out to check the efficiency of the proposed QOBO. The QOBO is applied to a stand-alone hybrid microgrid system located in Aswan, Egypt. The results show the effectiveness of the QOBO algorithm to solve the optimal economic design problem for hybrid microgrid power systems.

Abstract The problem of climate change because greenhouse gas emissions is aggravating, especially in public transport, which encourages the development of new technologies and clean energy control methods for the propulsion of vehicles such as tramways. A new energy control for a real tramway has been proposed in this paper, combining renewable sources, supercapacitors and lithium ion batteries, both components will absorb the energy from the regenerative braking of the tramway. The system has been modeled in Matlab considering certain restrictions in each component in order to supply the load on the round trip. Finally, a techno-economic and environmental analysis has been done identifying new patterns with respect to existing tramway systems. The annual energy required by the tramway is 867.62 MWh/year. The power variations are mainly supplied by the supercapacitor and the lithium ion battery functions as a backup. In this regard, the proposed system saves $ 2205,724 by supplying energy to the tramway and selling the excess energy to the grid for 20 years. Finally, the renewable system will have avoided 8445.4 tCO2/MWh.

The paper proposes a real-time model for electric vehicles (EVs) controlled load charging. The proposed demand-side management (DSM) of EVs is implemented based on queuing analysis with a nonhomogeneous arrival rate and charging service periods dataset. An electric vehicle model is used which is based on a statistical survey to represent the uncontrolled demand of the EVs. A probability distribution for the time at which EVs are plugged and the corresponding value of the state of charges (SOCs) are considered. The preferences of individual EVs have been fully exploited through a set of instructions to fulfill the needs of the vehicles’ owners. The designated preferences include the owner setting for both, charging price preferences (OPR), and the maximum estimated parking time duration (EPTD). The quasi-static time-series (QSTS) simulation is used to simulate real-time scenarios of the 24-h simulation period. The IEEE 123 nodes radial test feeder is analyzed with different daily load curves, EV charging scenarios, and wind power penetrations. The results show the effectiveness of the proposed DSM in avoiding excessive levels of charging with/without penetration of non-dispatchable wind power generation. The proposed DSM enables the EVs to charge with low tariff rates either at excessive renewable power generation or late evening hours with available committed bulk power plants and light loading conditions.

Abstract Recently, Proton Exchange Membrane Fuel Cells (PEMFCs) become one of the most promising friendly renewable energy sources. Therefore, developing a mathematical model for the PEMFC is an urgent necessity for simulation and evaluation of the processes occurring inside the fuel cell (FC) stack. In this paper, a precis model, which can stimulate the electrical and electrochemical phenomenon of the PEMFC is introduced. Improved salp swarm algorithm (ISSA) is proposed to enhance the performance of the conventional SSA and avoid getting stuck on local optimum. The proposed ISSA has been utilized for identifying the unknown parameter values of PEMFC stack models. The proposed ISSA is validated on four different FC stacks and a comparison between the computed and measured results has been accomplished. The Sum of Squared Errors (SSE) between experimental and estimated voltages is adopted as the objective function which has to be minimized. For validating the goodness of the ISSA, the generated values of the unknown parameters and the value of SSE using the ISSA-based PEMFC model are compared with the corresponding ones obtained by other optimization techniques. Furthermore, statistical analysis of proposed ISSA compared with the conventional SSA is carried out for all the PEMFC stacks involved in this work. The simulation results under various conditions of operation and the statistical results proved the stability and reliability of ISSA in comparison with recently utilized algorithms.

This research work examines the performance of an experimental gasification plant fueled with exhausted olive pomace pellets for the concurrent production of electricity, heat and biochar in the olive oil industry. The gasification plant consists of an air-blown downdraft fixed-bed gasifier that generates a lean fuel gas, termed producer gas, in a self-sustaining autothermal process. After conditioning of the producer gas in a cooling and cleaning unit, a four-stroke spark-ignition engine coupled to an electric generator is eventually used as power generation unit. An extensive experimental assessment of this facility was performed under partial and nominal load operation and was supplemented by a physicochemical analysis of the carbonaceous solid material discharged from the gasifier. The mass and energy balances of the gasification plant were calculated, including the carbon conversion efficiency and diverse energy conversion efficiencies. The results revealed an overall stable operation of the gasification plant in terms of composition and heating value of the producer gas and cogenerative production of electricity and heat in the engine–generator set. Under nominal operating conditions, the net electrical efficiency of the gasification plant was 12%–13%, with an average carbon conversion efficiency of the biomass feedstock into producer gas just above 80% and an average cold gas efficiency close to 70%.