Electric vehicle

About: Electric vehicle is a research topic. Over the lifetime, 51504 publications have been published within this topic receiving 556850 citations. The topic is also known as: EV & electrically-powered vehicle.

Modeling of Load Demand Due to EV Battery Charging in Distribution Systems

Abstract: This paper presents a methodology for modeling and analyzing the load demand in a distribution system due to electric vehicle (EV) battery charging. Following a brief introduction to the common types of EV batteries and their charging characteristics, an analytical solution for predicting the EV charging load is developed. The method is stochastically formulated so as to account for the stochastic nature of the start time of individual battery charging and the initial battery state-of-charge. A comparative study is carried out by simulating four EV charging scenarios, i.e., uncontrolled domestic charging, uncontrolled off-peak domestic charging, “smart” domestic charging and uncontrolled public charging-commuters capable of recharging at the workplace. The proposed four EVs charging scenarios take into account the expected future changes to the electricity tariffs in the electricity market place and appropriate regulation of EVs battery charging loads. A typical U.K. distribution system is adopted as an example. The time-series data of EV charging loads is taken from two commercially available EV batteries: lead-acid and lithium-ion. Results show that a 10% market penetration of EVs in the studied system would result in an increase in daily peak demand by up to 17.9%, while a 20% level of EV penetration would lead to a 35.8% increase in peak load, for the scenario of uncontrolled domestic charging-the “worst-case” scenario.

Predictive energy management of a power-split hybrid electric vehicle

Abstract: In this paper, a Model Predictive Control (MPC) strategy is developed for the first time to solve the optimal energy management problem of power-split hybrid electric vehicles. A power-split hybrid combines the advantages of series and parallel hybrids by utilizing two electric machines and a combustion engine. Because of its many modes of operation, modeling a power-split configuration is complex and devising a near-optimal power management strategy is quite challenging. To systematically improve the fuel economy of a power-split hybrid, we formulate the power management problem as a nonlinear optimization problem. The nonlinear powertrain model and the constraints are linearized at each sample time and a receding horizon linear MPC strategy is employed to determine the power split ratio based on the updated model. Simulation results over multiple driving cycles indicate better fuel economy over conventional strategies can be achieved. In addition the proposed algorithm is causal and has the potential for real-time implementation.

The state of the art of electric and hybrid vehicles

Abstract: In a world where environment protection and energy conservation are growing concerns, the development of electric vehicles (EV) and hybrid electric vehicles (HEV) has taken on an accelerated pace. The dream of having commercially viable EVs and HEVs is becoming a reality. EVs and HEVs are gradually available in the market. This paper will provide an overview of the present status of electric and hybrid vehicles worldwide and their state of the art, with emphasis on the engineering philosophy and key technologies. The importance of the integration of technologies of automobile, electric motor drive, electronics, energy storage, and controls and also the importance of the integration of society strength from government, industry, research institutions, electric power utilities, and transportation authorities are addressed. The challenge of EV commercialization is discussed.

Real-Time Coordination of Plug-In Electric Vehicle Charging in Smart Grids to Minimize Power Losses and Improve Voltage Profile

Abstract: This paper proposes a novel load management solution for coordinating the charging of multiple plug-in electric vehicles (PEVs) in a smart grid system. Utilities are becoming concerned about the potential stresses, performance degradations and overloads that may occur in distribution systems with multiple domestic PEV charging activities. Uncontrolled and random PEV charging can cause increased power losses, overloads and voltage fluctuations, which are all detrimental to the reliability and security of newly developing smart grids. Therefore, a real-time smart load management (RT-SLM) control strategy is proposed and developed for the coordination of PEV charging based on real-time (e.g., every 5 min) minimization of total cost of generating the energy plus the associated grid energy losses. The approach reduces generation cost by incorporating time-varying market energy prices and PEV owner preferred charging time zones based on priority selection. The RT-SLM algorithm appropriately considers random plug-in of PEVs and utilizes the maximum sensitivities selection (MSS) optimization. This approach enables PEVs to begin charging as soon as possible considering priority-charging time zones while complying with network operation criteria (such as losses, generation limits, and voltage profile). Simulation results are presented to demonstrate the performance of SLM for the modified IEEE 23 kV distribution system connected to several low voltage residential networks populated with PEVs.