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Abstract The integration of supercapacitors as hybrid energy storage systems in electric vehicles has attracted the attention of many researchers and has been considered as a promising solution. Bidirectional DC/DC converters (BDDCs) play a fundamental role in HESS, as they manage the power flow by controlling currents and regulating the DC bus voltage. However, they encounter the challenge of uncertainties and high fluctuation power loads, necessitating the fast dynamics, stability, and high robustness of the controller. This paper proposes a novel hybrid proportional–integral and backstepping cascade controller to regulate the DC‐bus voltage under uncertainties and load variations, and to control the current references of the on‐boarded sources. To confirm the asymptotic stability of the whole system, a nonlinear stability analysis is conducted using the Lyapunov theorem. A power management strategy is applied to distribute the power loads and generate reference currents for the BDDCs controller. Simulations results under various driving cycles using MATLAB/Simulink demonstrate the superiority of the proposed controller compared to conventional proportional–integral and backstepping controllers. A real‐time controller‐hardware‐in‐the‐loop test bench is developed to validate the effectiveness of the proposed strategy.

This paper presents a control strategy to optimize both the vehicle speed and gearbox position on curved roads with the aim of maximizing fuel economy. Previous studies have focused only on the vehicle speed optimization during cornering. Combining vehicle speed and gearbox position optimization can significantly maximize the energy-saving for an Internal combustion engine (ICE) vehicle. The problem is formulated as two successive optimization problems. In the first one, based on the road map and traffic data, the vehicle’s optimal speed profile is calculated by minimizing the energy spent for the entire curve. In the second one, the gearbox position is optimized for fuel consumption minimization using realistic engine map. The presented control structure’s effectiveness was evaluated through co-simulation of Matlab/Simulink and CarSim in various driving scenarios. The results show that approximately 5.25% to 11.44 % of fuel savings can be achieved compared with a typical driver model.

The design and integration of intelligent energy management systems in hybrid electric vehicle (EV) charging stations, leveraging industry 4.0 and renewable energy sources, is crucial for advancing sustainability, efficiency, and technological development. The innovative hybrid EV charging station described in this study uses a combination of fuel cells, batteries, and solar panels that run at 14 amps a piece at 240 volts. The system consists of five essential components that work together to transfer power wirelessly: an EV battery bank, a boost converter, an HF inverter, transfer coils, and a power supply. Two crucial phases make up the optimization process. In phase 1, the boost converter’s maximum power point is tracked and optimized to generate the most power possible by varying the duty cycle between 10% and 90%. In phase 2, the HF uses a class ϕ2 inverter at 30 MHz to synchronize with the resonant frequency of wireless power transfer coils. Zero-voltage switching is used by a digital signal processor card to carry out control for effective operations. By utilizing hybrid sources to optimize power transmission, this design improves the sustainability of EV charging options.

This paper introduces a fractional-order PD approach (F-oPD) designed to control a large class of dynamical systems known as fractional-order chaotic systems (F-oCSs). The design process involves formulating an optimization problem to determine the parameters of the developed controller while satisfying the desired performance criteria. The stability of the control loop is initially assessed using the Lyapunov’s direct method and the latest stability assumptions for fractional-order systems. Additionally, an optimization algorithm inspired by the flight skills and foraging behavior of hummingbirds, known as the Artificial Hummingbird Algorithm (AHA), is employed as a tool for optimization. To evaluate the effectiveness of the proposed design approach, the fractional-order energy resources demand-supply (Fo-ERDS) hyperchaotic system is utilized as an illustrative example.

In this article, an artificial algorithm called hummingbirds optimization method, named AHA algorithm, is settled to extract accurately the parameters of PV modules under outdoor operational conditions. AHA is the main contribution in this work, regarding its efficiency and good performance in terms of standard deviation (StD), root mean square error (RMSE), sum of squared error (SSE), maximum number of iterations (MaxIt), particularly, for extracting parameter from modules operating in real conditions, and under different temperature and irradiance levels. The AHA is applied on a Polycrystalline-solar panel module type 320W-72P at real operating conditions and on a PV array of three polycrystalline PV modules connected in series. PV cells of the same modules are assumed operating under the same conditions where they share the same electric current and voltage values. In this last pattern, eight different scenarios are chosen. In the first five scenarios, the temperatures are (46.97, 44.23, 42.87, 40.59 and 30.60°C), and the irradiance varies, such as (910, 800.57, 614.13, 415.1 and 200.87W/m2), respectively. In the other three testing scenarios, the solar irradiance is equal (803 W/ m2), and the temperature differs, such as $\text{T}= 47.93\,\,^{\circ }\text{C}$ , 53.38 °C and 36.62°C. Lower values of root mean square errors (RMSE) are achieved ( $9.8602\times {10}^{-4}$ and $2.572533\times {10}^{-2}$ for RTC France PV cell and the 320W-72P module respectively) with 6000 iterations. Moreover, all the eight scenarios are lower than $7.245916\times {10}^{-2}$ for the last case study. Moreover, results show that we could recommend the AHA algorithm as an advanced and efficient method for dealing with real time parameter optimization of photovoltaic modules. In fact, a high closeness between the simulated and the experimental curves is achieved, which indicates the perfectness of this optimization method. Finally, the proposed AHA algorithm can be engaged as tools for the best designing of PV systems.

Nowadays, microgrids (MGs) are receiving a lot of attention. In an economical MG, the battery energy storage system (BESS) plays an important role. One of the biggest challenges in MGs is the optimal choice of the BESS that can lead to better performance of the MG, which will be more flexible, efficient, and effective than traditional power systems. In this paper, we present the modeling and simulation of different energy storage systems including Li-ion, lead-acid, nickel cadmium (Ni-Cd), nickel-metal hybrid (Ni-Mh), and supercapacitor (SC), for renewable energy applications, and more specifically for MGs. The results of simulation show that Li-ion batteries have a better response time than lead-acid batteries, Ni-Cd batteries, and Ni-Mh batteries and thus are more suitable for combination with supercapacitors. Li-ion batteries are the best option for fast-charging applications in MGs. The discharge phase ends with SOC ≤ ±94%, SOC ≤ ±95%, SOC = 95%, SOC < 95%, and SOC < 60%, respectively, for Li-ion, lead-acid, Ni-Cd, Ni-Mh, and supercapacitor. Moreover, the use of the battery management system (BMS) can significantly improve the performance of BESS, leading to higher levels of SOC and longer life span. The obtained results have shown that with an optimization algorithm for energy storage systems, more specifically for the battery-charging mode, the response time of BESSs can be further improved. The effect of ambient temperature has also been investigated on the functional capacities of the batteries. The obtained results demonstrated that extreme temperatures (80°C to −80°C) have a significant impact on battery performance and capacity, especially for Li-ion batteries, with a drop in capacity of up to 50% at −40°C. This highlights the importance of considering the ambient temperature in the design and operation of MGs. Overall, our study provides valuable insights into the optimal selection of BESS and the impact of ambient temperature on their performance, which can help in the development of more efficient and reliable MGs.

DC/DC converters are widely used in photovoltaic (PV) systems to track the maximum power points (MPP) of a photovoltaic generator (PVG). The variation of solar radiation (G) and PV cells temperature (T) affect the power efficiency of these DC/DC converters because they change the MPP, thus a sizing adaptation of the component values in these DC/DC converters is needed. Power loss in the inductor due to core saturation can severely degrade power efficiency. This paper proposes a new method that allows to adapt the inductor values according to the variable output power of the PV array in order to minimize losses and improve the converter power efficiency. The main idea is to replace the DC/DC inductor with a coupled inductor where the DC/DC inductor value is adjusted through an additional winding in the magnetic core that modulates the magnetic field inside it. Low current intensities from the PVG supply this winding through a circuit controlled by a fuzzy logic controller in order to regulate the second winding current intensity. Experimental results show a significant improvement of the power efficiency of the proposed solution as compared to a conventional converter.

In this paper, we propose an application of an algorithm, based on the T-S (Takagi-Sugeno) technique, to stabilize a quadrotor helicopter. After giving the nonlinear model of the vehicle, its representation by a T-S fuzzy model is discussed. Following this, a fuzzy controller is synthesized, which will guarantee the stability of the quadrotor. The proposed T-S controller is designed with measurable premise variables and the conditions of stability are given in terms of linear matrix inequality (LMI). Simulations and real-time experiments using a test-bed platform prove the performance of a PDC control algorithm to stabilize the vehicle robustly at a desired set point.