This paper presents a straightforward power management algorithm that supervises the contribution of more than one energy source for charging a vehicle, even if the car is in motion. The system is composed of a wireless charging system, photovoltaic (PV) generator, fuel cell (FC), and a battery system. It also contains a group of power converters associated with each energy resource to make the necessary adaptation between the input and output electrical signals. The boost converter relates to the PV/FC, and the boost–buck converter is connected with the battery pack. In this work, the wireless charging, FC, and PV systems are connected in parallel via a DC/DC converter for feeding the battery bank when the given energy is in excess. Therefore, for each of these elements, the mathematical model is formulated, then the corresponding power management loop is built, which presents the significant contribution of this paper. The efficient power management methodology proposed in this work was verified on Matlab/Simulink platforms. The battery state of charge and the hydrogen consumption obtained results were compared to show the effectiveness of this multi-source system.
This research focuses on a photovoltaic system that powers an Electric Vehicle when moving in realistic scenarios with partial shading conditions. The main goal is to find an efficient control scheme to allow the solar generator producing the maximum amount of power achievable. The first contribution of this paper is the mathematical modelling of the photovoltaic system, its function and its features, considering the synthesis of the step-up converter and the maximum power point tracking analysis. This research looks at two intelligent control strategies to get the most power out, even with shading areas. Specifically, we show how to apply two evolutionary algorithms for this control. They are the “particle swarm optimization method” and the “grey wolf optimization method”. These algorithms were tested and evaluated when a battery storage system in an Electric Vehicle is fed through a photovoltaic system. The Simulink/Matlab tool is used to execute the simulation phases and to quantify the performances of each of these control systems. Based on our simulation tests, the best method is identified.
In this paper, the amount of microgrid frequency deviation in the dynamic state can be reduced by improving the frequency controller and implementing a new method. The proposed controller is designed for a microgrid including renewable resources, and the proposed control strategy is such that the controller coefficients are adjusted and optimised at all times by the model predictive control (MPC). The weight parameters of the MPC controller have been optimised by the particle swarm optimisation (PSO) algorithm. The proposed controller is located in the secondary frequency control loop, and by applying a control signal to the sources, the frequency perturbations following the power changes in the microgrid are reduced. The simulation results show that the proposed controller performs better than the Ziegler-Nichols PI controller (PI-ZN) method, PI-based controllers that rely on fuzzy logic (PI-Fuzzy), the fractional-order proportional-integral-derivative (FOPID) controller that is based on chaos particle swarm optimisation (FOPID-CPSO) algorithm and the PID controllers based on CPSO algorithm (PID-CPSO). It has been able to effectively reduce the frequency fluctuations in terms of amplitude and number of oscillations is also more resistant to the uncertainty of microgrid parameters and shows better performance when changing parameters than other methods.
Electric Vehicles (EVs) have emerged rapidly across the globe as a powerful eco-friendly initiative that if integrated well with an urban environment could be iconic for the city’ host’s commitment to sustainable mobility and be a key ingredient of the smart city concept. This paper examines ways that will help us to develop a better understanding of how EVs can achieve energy use optimization and be connected with a smart city. As a whole, the present study is based on an original idea that would be useful in informing policy-makers, automotive manufacturers and transport operators of how to improve and embrace better EV technologies in the context of smart cities. The proposed approach is based on vehicles and buildings communication for sharing some special information related to the vehicle status and to the road condition. EVs can share their own information related to the energy experience on a specific path. This information can be gathered in a gigantic database and used for managing the power inside these vehicles. In this field, this paper exposes a new approach to power management inside an electric vehicle based on bi-communication between vehicles and buildings. The principle of this method is established on two sections; the first one is related to vehicles’ classification and the second one is attached to the buildings’ recommendation, according to the car position. The classification problem is resolved using the support vector classification method. The recommendation phase is resolved using the artificial intelligence principle and the neural network was employed, for giving the best decision. The optimal decision will be calculated inside the building, according to its position and using the old vehicle’s data, and transferred to the coming vehicle, for optimizing its energy consumption method in the corresponding building zone. Different possibilities and situations were discussed in this approach. The proposed power management methodology was tested and validated using Simulink/Matlab tool. Results related to the battery state of charge and to the consumed energy were compared at the end of this work, for showing the efficiency of this approach.
An increased electricity demand and dynamic load changes are creating a huge burden on the modern utility grid, thereby affecting supply reliability and quality. It is thus crucial for modern power system researchers to focus on these aspects to reduce grid outages. High-quality power is always desired to run various businesses smoothly, but power-electronic-converter-based renewable energy integrated into the utility grid is the major source of power quality issues. Many solutions are constantly being invented, yet a continuous effort and new optimized solutions are encouraged to address these issues by adhering to various global standards (IEC, IEEE, EN, etc.). This paper therefore proposes a concept of establishing a renewable-energy-based microgrid cluster by integrating various buildings located in an urban community. This enhances power supply reliability by managing the available energy in the cluster without depending on the utility grid. Further, a “fuzzy space vector pulse width modulation” (FSV-PWM) technique is proposed to control the inverter, which improves the power supply quality. This work uniquely optimized the dq reference currents using fuzzy logic theory, which were used to plot the space vectors with effective sector selection to generate accurate PWM signals for inverter control. The modeling/simulation of the microgrid cluster involving the FSV-PWM-based inverter was carried out using MATLAB/Simulink®. The efficacy of the proposed FSV-PWM over the conventional ST-PWM was verified by plotting voltage, frequency, real/reactive power, and harmonic distortion characteristics. Various power quality indices were calculated under different disturbance conditions. The results showed that the use of the proposed FSV-PWM-based inverter adhered to all the key standard requirements, while the conventional system failed in most of the indices.
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