Optimal energy management of electric vehicles using slap swarm optimization and differential flatness control has been proposed. A battery–supercapacitor power system is adopted. Each source is connected in parallel to the DC-bus using DC–DC bidirectional converters and supplies a synchronous reluctance motor (SynRM) based drive. The proposed EMS fundamental forces lie in using a combination of complementary proprieties of two approaches, a Slap Swarm optimization Algorithm and Differential Flatness (DF). With a fast optimization mechanism, the Slap Swarm optimization algorithm allows adapting in real-time conditions the DF gains to optimize the system performances. On its side, DF uses predefined trajectories respecting the physical proprieties of the system, which is a powerful tool to guarantee the dynamic constraints of the sources when ensuring desired robust control proprieties. To check the feasibility and performance of the suggested EMS, comprehensive processor-in-the-loop co-simulations of the electric vehicle were carried out using the C2000 launchxl-f28379d DSP board. The main goal of the proposed EMS is to guarantee the DC-bus stabilization, reducing the DC-bus voltage ripples (Δv = 5 V) and the voltage overshoots 15 V (3.2%), respect the source dynamics, and satisfy the SynRM motor power demand. Furthermore, the algorithm minimizes induced harmonics by the drive (10.49%), reducing the battery current ripple by 17.15A, thereby enhancing the battery lifecycle.

Due to their improved performance and advantages for the environment, fuel cell hybrid electric cars, or FCEVs, have garnered a lot of attention. Establishing an energy management strategy (EMS) for fuel cell electric vehicles (FCEVs) is essential for optimizing power distribution among various energy sources. This method addresses concerns regarding hydrogen utilization and efficiency. The Arithmetic Optimization Algorithm is employed in the proposed energy management system to enhance the strategy of maximizing external energy, leading to decreased hydrogen consumption and increased system efficiency. The performance of the proposed EMS is evaluated using the Federal Test Procedure (FTP-75) to replicate city driving situations and is compared with existing algorithms through a comparison co-simulation. The co-simulation findings indicate that the suggested EMS surpasses current approaches in reducing fuel consumption, potentially decreasing it by 59.28%. The proposed energy management strategy demonstrates an 8.43% improvement in system efficiency. This enhancement may reduce dependence on fossil fuels and mitigate the adverse environmental effects associated with automobile emissions. To assess the feasibility and effectiveness of the proposed EMS, the system is tested within a Processor-in-the-Loop (PIL) co-simulation environment using the C2000 launchxl-f28379d Digital Signal Processing (DSP) board.

In this paper, an optimal energy management system (EMS) for an electric vehicle (EV)microgrid made of a battery-supercapacitor hybrid power system is proposed. Through bidirectional DC-DC converters, the storage systems are coupled in parallel to the DC-bus and fed via an inverter, a synchronous reluctance motor (SynRM). The driving factor behind the suggested EMS is using the complementing properties of two techniques: the Slap Swarm optimization Algorithm (SSA) and State Machine Control (SMC). The SSA’s fast optimization method makes real-time adaption of the SMC improvements possible, maximizing system performance. The primary objective of the proposed EMS is to provide DC-bus stability, respect source dynamics, and meet SynRM motor power requirements. Additionally, the algorithm lessens the impact of the motor’s harmonics, hence improving battery lifetime. To test the control design and assess the efficacy of the proposed EMS, extensive simulations of the advocated optimal EMS of the hybrid power system of an EV are carried out in a Matlab/Simulink environment.

The objective of this work is to suggest a new energy management strategy (EMS) for a hybrid power system that is based on a load-following strategy and Fractional-Order proportional-integral (FOPI) controller. The lithium-ion battery, supercapacitor, and two bidirectional DC-DC converters are the components that make up the hybrid power system that has been decided for. The suggested EMS algorithm includes two primary objectives: the first is to improve power quality and supply the power system with the appropriate power references, and the second is to improve the lives of the energy components. The simulation and co-simulation results of the processor in the loop (PIL) show that the proposed power management method is effective for load profile operation configurations.

Many industrial and electrical systems, such as electric vehicles, employ DC/DC converters. Since DC/DC converters are nonlinear and time-varying systems, it is inappropriate to regulate them using linear control techniques. In this paper, a super-twisting sliding mode control (STSMC) is applied for bidirectional DC-DC converter in electric vehicle applications. The traction chain of the electric vehicle consists of a battery-supercapacitor energy storage system (ESS) linked in parallel to the DC bus through two bidirectional DC–DC converters and a synchronous reluctance motor drive (SynRM). The benefit of sharing power in an HPS, which is primarily made up of a supercapacitor and battery, is the ability to use the strengths of both sources, with the former having high specific energy and the latter having high specific power. The on-board energy storage system's performance is enhanced by this hybridization in terms of cost, mass, and charging time. In addition to the proposed control, the hybrid power system (HPS) needs a strategy for routing energy between the two sources and the motor. This strategy is based on frequency decoupling (DFS), which protects the battery from peak current during the acceleration and deceleration phases. The objective of using the control law based on super-twisting sliding mode control (STSMC) is to follow the reference trajectories in finite time with high precision, very good robustness, and a reduction in chattering. Using the MATLAB/Simulink software, a thorough simulation of an electric vehicle was performed in order to confirm the effectiveness of the suggested control approach and assess the effectiveness of the source's energy management strategy. This control method ensures a reduction in the bus voltage stress (Δv=5V) and improves the quality of the power. The overshoot voltage for a load power of 21 kw is reduced to 15V (3.2%) compared to the linear control techniques, improving the system's robustness and reliability..