Swarm Intelligence-Based Optimization for PHEV Charging Stations

2015 ◽  
pp. 374-405 ◽  
Author(s):  
Imran Rahman ◽  
Pandian Vasant ◽  
Balbir Singh Mahinder Singh ◽  
M. Abdullah-Al-Wadud
Keyword(s):  
Hybrid Electric Vehicle ◽  
Performance Indicator ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Computation Time ◽  
Charging Infrastructure ◽  
Charging Time ◽  
Battery Capacity ◽  
Hybrid Electric ◽  
Charging Stations

In this chapter, Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO) technique were applied for intelligent allocation of energy to the Plug-in Hybrid Electric Vehicles (PHEVs). Considering constraints such as energy price, remaining battery capacity, and remaining charging time, they optimized the State-of-Charge (SoC), a key performance indicator in hybrid electric vehicle for the betterment of charging infrastructure. Simulation results obtained for maximizing the highly non-linear objective function evaluates the performance of both techniques in terms of global best fitness and computation time.

scholarly journals Perceived Usage Potential of Fast-Charging Locations

2018 ◽  
Vol 9 (1) ◽  
pp. 14 ◽  
Author(s):  
Julia Krause ◽  
Stefan Ladwig ◽  
Lotte Saupp ◽  
Denis Horn ◽  
Alexander Schmidt ◽  
...  
Keyword(s):  
Electric Vehicles ◽  
Point Of View ◽  
Charging Infrastructure ◽  
Charging Time ◽  
Points Of Interest ◽  
Fast Charging ◽  
Business Perspective ◽  
Charging Stations ◽  
User Perspectives ◽  
Model Point

Fast-charging infrastructure with charging time of 20–30 min can help minimizing current perceived limitations of electric vehicles, especially considering the unbalanced and incomprehensive distribution of charging options combined with a long perceived charging time. Positioned on optimal location from user and business perspective, the technology is assumed to help increasing the usage of an electric vehicle (EV). Considering the user perspectives, current and potential EV users were interviewed in two different surveys about optimal fast-charging locations depending on travel purposes and relevant location criteria. The obtained results show that customers prefer to rather charge at origins and destinations than during the trip. For longer distances, charging locations on axes with attractive points of interest are also considered as optimal. From the business model point of view, fast-charging stations at destinations are controversial. The expensive infrastructure and the therefore needed large number of charging sessions are in conflict with the comparatively time consuming stay.

Development of Fuel Cell Hybrid Electric Vehicle Simulator and HCU

2010 ◽  
Vol 26-28 ◽  
pp. 1110-1114
Author(s):  
Dong Ji Xuan ◽  
Qian Ning ◽  
Zhen Zhe Li ◽  
Tai Hong Cheng ◽  
Yun De Shen
Keyword(s):  
Fuel Cell ◽  
Electric Vehicle ◽  
Control Strategy ◽  
Cell Hybrid ◽  
Hybrid Electric Vehicle ◽  
Driver Model ◽  
Vehicle Speed ◽  
Battery Capacity ◽  
Hybrid Electric ◽  
Fuel Cell Hybrid

Based on the Matlab/Simulink module modeling for Fuel Cell Hybrid Electric Vehicle was carried out, which is comprised of the fuel cell stack model, a DC/DC converter model, a battery model, a motor model, avehiclemodel and a driver model, and Hybrid Control Unit(HCU) was developed. The HCU control strategy also incorporates regenerative braking and recharge for battery capacity recovery. Vehicle speed effect is evaluated in New Europe Driving Cycle. The simulation result that the control strategy implemented by HCU is achievable, and which proves that the mode of Start, Accele_FCBat, Cruise, RE_Brake, Power_FC and Pause operate sequently as well as reliably.

Optimal Plug-In Hybrid Vehicle Design and Allocation for Minimum Life Cycle Cost, Petroleum Consumption and Greenhouse Gas Emissions

2010 ◽  
Author(s):  
Ching-Shin Norman Shiau ◽  
Scott B. Peterson ◽  
Jeremy J. Michalek
Keyword(s):  
Life Cycle ◽  
Greenhouse Gas ◽  
Life Cycle Cost ◽  
Hybrid Electric Vehicle ◽  
Operating Cost ◽  
Ghg Emissions ◽  
The United States ◽  
Vehicle Design ◽  
Battery Capacity ◽  
Hybrid Electric

Plug-in hybrid electric vehicle (PHEV) technology has the potential to help address economic, environmental, and national security concerns in the United States by reducing operating cost, greenhouse gas (GHG) emissions and petroleum consumption from the transportation sector. However, the net effects of PHEVs depend critically on vehicle design, battery technology, and charging frequency. To examine these implications, we develop an integrated optimization model utilizing vehicle physics simulation, battery degradation data, and U.S. driving data to determine optimal vehicle design and allocation of vehicles to drivers for minimum life cycle cost, GHG emissions, and petroleum consumption. We find that, while PHEVs with large battery capacity minimize petroleum consumption, a mix of PHEVs sized for 25–40 miles of electric travel produces the greatest reduction in lifecycle GHG emissions. At today’s average US energy prices, battery pack cost must fall below $460/kWh (below $300/kWh for a 10% discount rate) for PHEVs to be cost competitive with ordinary hybrid electric vehicles (HEVs). Carbon allowance prices have marginal impact on optimal design or allocation of PHEVs even at $100/tonne. We find that the maximum battery swing should be utilized to achieve minimum life cycle cost, GHGs, and petroleum consumption. Increased swing enables greater all-electric range (AER) to be achieved with smaller battery packs, improving cost competitiveness of PHEVs. Hence, existing policies that subsidize battery cost for PHEVs would likely be better tied to AER, rather than total battery capacity.

Hybrid Electric Vehicle Energy Management With Battery Thermal Considerations Using Multi-Rate Dynamic Programming

2013 ◽  
Author(s):  
Rajit Johri ◽  
Wei Liang ◽  
Ryan McGee
Keyword(s):  
Dynamic Programming ◽  
Electric Vehicle ◽  
Thermal Management ◽  
Hybrid Electric Vehicle ◽  
Computational Time ◽  
Bulk Temperature ◽  
Thermal Dynamics ◽  
Battery Thermal Management ◽  
Battery Capacity ◽  
Hybrid Electric

Battery capacity and battery thermal management control have a significant impact on the Hybrid Electric Vehicle (HEV) fuel economy. Additionally, battery temperature has a key influence on the battery health in an HEV. In the past, battery temperature and cooling capacity has not been included while performing optimization studies for power management or optimal battery sizing. This paper presents an application of Dynamic Programming (DP) to HEV optimization with battery thermal constraints. The optimization problem is formulated with 3 state variables, namely, the battery State Of Charge (SOC), the engine speed and the battery bulk temperature. This optimization is critical for determining appropriate battery size and battery thermal management design. The proposed problem has a major challenge in computation time due to the large state space. The paper describes a novel multi-rate DP algorithm to reduce the computational challenges associated with the particular class of large-scale problem where states evolve at very different rates. In HEV applications, the battery thermal dynamics is orders of magnitude slower than powertrain dynamics. The proposed DP algorithm provides a novel way of tackling this problem with multiple time rates for DP with each time rate associated with the fast and slow states separately. Additionally, the paper gives possible numerical techniques to reduce the DP computational time and the time reduction for each technique is shown.

scholarly journals A Brief Survey on Important Interconnection Standards for Photovoltaic Systems and Electric Vehicles

2021 ◽  
Vol 12 (3) ◽  
pp. 117
Author(s):  
Suvetha Poyyamani Poyyamani Sunddararaj ◽  
Shriram S. Rangarajan ◽  
Subashini Nallusamy ◽  
E. Randolph Collins ◽  
Tomonobu Senjyu
Keyword(s):  
Electric Vehicles ◽  
Large Scale ◽  
Reliable Operation ◽  
Charging Infrastructure ◽  
Grid Systems ◽  
Pv Systems ◽  
Charging Time ◽  
Cost Distance ◽  
Smart Charging ◽  
Charging Stations

The consumer adoption of electric vehicles (EVs) has become most popular. Numerous studies are being carried out on the usage of EVs, the challenges of EVs, and their benefits. Based on these studies, factors such as battery charging time, charging infrastructure, battery cost, distance per charge, and the capital cost are considered factors in the adoption of electric vehicles and their interconnection with the grid. The large-scale development of electric vehicles has laid the path to Photovoltaic (PV) power for charging and grid support, as the PV panels can be placed at the top of the smart charging stations connected to a grid. By proper scheduling of PV and grid systems, the V2G connections can be made simple. For reliable operation of the grid, the ramifications associated with the PV interconnection must be properly addressed without any violations. To overcome the above issues, certain standards can be imposed on these systems. This paper mainly focuses on the various standards for EV, PV systems and their interconnection with grid-connected systems.

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