No abstract

This article proposes an offline Energy Management System (EMS) for Parallel Hybrid Electric Vehicles (PHEVs). Dividing the torque between the Electric Motor (EM) and the Internal Combustion Engine (ICE) requires a suitable EMS. Batteries are vital to HEVs and significantly impact overall vehicle cost and performance. High temperature and high battery State of Charge (SOC) are the main factors that accelerate battery aging. SOC is the most critical state variable in EMS and was usually considered the only dynamic variable in previous studies. For simplicity, the battery temperature was often assumed to be constant, and the effect of EMS on temperature change was neglected. In this paper, we first apply Dynamic Programming (DP) to a PHEV without considering battery temperature variations. Then, the battery model is improved by modeling the cooling system to take into account temperature variations and show how neglecting the thermal dynamics of the battery in EMS is impractical. Finally, by integrating battery temperature as a state variable in the optimization problem, a new EMS is proposed to control battery temperature and SOC variation. Simulation results of the tested vehicle show that the proposed method controls battery charge and temperature. The proposed EMS method prevents uncontrolled fluctuations in battery temperature and reduces its deterioration rate.

No abstract

In this paper, the Hook-Jews (HJ) optimization method is used to optimize a 3-phase Squirrel-Cage Induction Motor (SCIM) as an Electric Vehicle's (EV) motor. Optimal designs with different numbers of poles, different nominal and maximum speeds, and different numbers of grooves are compared and the best one is selected. The optimization method used has advantages such as simple programming, omission of gradients, short convergence times, and the possibility of changing individual parameters. Design parameter variations for optimal designs with rated speeds for 2-pole and 4-pole motors are shown and explained. The results show that his 2-pole motor with the rectangular stator and rotor slots and a rated speed of 1800 rpm has the highest efficiency.

No abstract

This article proposes solutions to control the balance of a two-wheel robotic system in the presence of uncertainties in the dynamic equations and without the need for kinematic equations. To do this, the dynamic equations of this system are first transferred to the error area and then these equations are divided into two completely independent subsystems, under excitation, and full excitation. In the following, two completely different sliding mode controllers are presented to control the under-excitation subsystem, which can make this subsystem asymptotically stable in the presence of structural and non-structural uncertainties. After that, a sliding mode control is proposed to control the entire excitation subsystem, making this subsystem asymptotically stable in the presence of existing uncertainties. Since these two subsystems are completely independent of each other, proving their global asymptotic stability provides proof of the global asymptotic stability of the closed-loop system. The isolation of two-wheel balancing robot subsystems eliminates the need to use kinematic equations, resulting in structural uncertainties not affecting the tracking accuracy of closed-loop system state variables. Finally, to verify the performance of the proposed controllers and compare their performance results, three-stage simulations are implemented on the two-wheel Balance robotic system. Mathematical proofs and simulation results show the optimal performance of the proposed solutions.

Nowadays, linear electric motors are used in industries and applications that require linear motion. Different classifications for linear motors can be considered that one of them is based on their secondary. They have two secondary types: Flat (FLIM) and Ladder (LLIM) secondary. LLIMs have more thrust force than FLIMs, however due to their higher design cost, they are less popular. In this paper we proposed a linear induction motor with Hybrid (HLIM) secondary and its relationships with consideration of the end effect. Then, this motor optimally designed using the Particle Swarm Optimization (PSO) algorithm. Next its output speed is controlled by the Direct Thrust Force Control (DTFC) method. According to the results, speed of HLIM reaches the desired speed in less time than and also less ripple than LLIM and FLIM. Also HLIM has more power factor as well as more thrust force and more efficiency than LLIM and FLIM. Also HLIM has less design cost than the LLIM and FLIM.

Electric vehicles have many opportunities to participate in auxiliary operations as research interest in renewables and green energy sources grows. This paper discusses novel energy storage technologies for assessing the impact of electric vehicles and distributed flexible alternating current transmission systems in smart grids for improving various power system performance results using various optimization techniques with real and reactive power minimization as objective functions. The authors should strive to make this review article extremely beneficial to scholars, academics, and practitioners. As a result, this review article will also be beneficial to the social community.