Eco Look-Ahead Control of Battery Electric Vehicles and Roadway Grade Effects

2020 ◽  
Vol 2674 (10) ◽  
pp. 429-437
Author(s):  
Kyoungho Ahn ◽  
Hesham A. Rakha ◽  
Sangjun Park
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Electric Vehicles ◽  
Combustion Engine ◽  
Critical Factor ◽  
Vehicle Speed ◽  
Battery Electric Vehicles ◽  
Look Ahead ◽  
Extra Energy ◽  
Regenerative Energy

Roadway grade is one of the major variables that affects a vehicle’s energy consumption. This study demonstrates the potential benefits of an eco look-ahead control for battery electric vehicles (BEVs). The proposed eco look-ahead controller was developed for the lead vehicle of a platoon of BEVs. The eco look-ahead control predicts the optimum speed and acceleration levels within a preset speed window to minimize energy consumption considering the instantaneous energy used and the regenerated energy. The developed BEV eco look-ahead control system integrates a BEV energy consumption model and a powertrain model to save fuel while maintaining vehicle speed within a user-specified window. In addition, the study demonstrates the effects of roadway grade on BEV energy consumption. The results show that the energy consumption of BEVs are significantly reduced on downhill roads compared with internal combustion engine vehicles owing to regenerative braking. Specifically, testing showed that BEVs could even produce extra energy on downhill roads. The study also tested the eco look-ahead control on an 18-km section of I-81 and found that the system produced savings of 8.51% and 26.88% in the uphill and downhill directions, respectively. This study demonstrated that regenerative energy in BEVs is a critical factor in energy efficiency and the developed eco look-ahead control significantly improved the energy efficiency for BEVs.

A simulation-based concept design approach for combustion engine and battery electric vehicles

2018 ◽  
Vol 233 (7) ◽  
pp. 1950-1967 ◽  
Author(s):  
Nikola Holjevac ◽  
Federico Cheli ◽  
Massimiliano Gobbi
Keyword(s):  
Energy Consumption ◽  
Internal Combustion Engine ◽  
Electric Vehicles ◽  
Combustion Engine ◽  
Internal Combustion ◽  
Computer Aided Engineering ◽  
Concept Design ◽  
Battery Electric Vehicles ◽  
Computer Aided ◽  
Effective Design

The early concept design of a vehicle is becoming increasingly crucial to determine the success of a car. Broadening market competition, more stringent regulations and fast technological changes require a prompt response from carmakers, and computer-aided engineering has emerged in recent years as the promising way to provide more efficient and cost-effective design and to cut development time and costs. The work presented in this paper shows an approach based on computer-aided engineering to determine vehicle’s energy consumption and performance. The different vehicle’s subsystem are first analyzed separately by using dedicated simulation tools and then integrated to obtain the entire vehicle. The work covers a wide range of vehicle layouts. Internal combustion engine vehicles and battery electric vehicles are considered and various transmission configurations are contemplated with respect to some of the most adopted solutions for these vehicles. The simulation results allow to identify the most effective design variables regarding the combustion engine and the electric motor and to compare the different layouts over various car segments. The results clearly point out that for internal combustion engine vehicles, the combustion engine is the crucial component that defines the vehicle’s characteristics and particularly the energy consumption. Conversely, battery electric vehicles show a more balanced distribution of the losses, and therefore to improve the vehicle’s behavior, different components should be considered in detail. Nevertheless, the choice of the number of electric motors and the transmission choice play a significant role in defining the vehicle performances.

scholarly journals Multi-objective Eco-Routing Model Development and Evaluation for Battery Electric Vehicles

2021 ◽  
pp. 036119812110315
Author(s):  
Kyoungho Ahn ◽  
Youssef Bichiou ◽  
Mohamed Farag ◽  
Hesham A. Rakha
Keyword(s):  
Energy Consumption ◽  
Travel Time ◽  
Electric Vehicles ◽  
Large Scale ◽  
Traffic Assignment ◽  
Combustion Engine ◽  
Model Development ◽  
User Equilibrium ◽  
Multi Objective ◽  
Battery Electric Vehicles

This paper develops a multi-objective eco-routing algorithm (eco- and travel time-optimum routing) for battery electric vehicles (BEVs) and internal combustion engine vehicles (ICEVs) and investigates the network-wide impacts of the proposed multi-objective Nash optimum (user equilibrium) traffic assignment on a large-scale network. Unlike ICEVs, BEVs are more energy efficient on low-speed arterial trips compared with highway trips. Different energy consumption patterns require different eco-routing strategies for ICEVs and BEVs. This study found that single-objective eco-routing could significantly reduce the energy consumption of BEVs but also significantly increase their average travel time. Consequently, the study developed a multi-objective routing model (eco- and travel time-routing) to improve both energy and travel time measures. The model introduced a link cost function that uses the specification of the value of time and the cost of fuel/energy. The simulation study found that multi-objective routing could reduce BEV energy consumption by 13.5%, 14.2%, 12.9%, and 10.7%, as well as ICEV fuel consumption by 0.1%, 4.3%, 3.4%, and 10.6% for “not congested, “slightly congested,”“moderately congested,” and “highly congested” conditions, respectively. The study also found that multi-objective user equilibrium routing reduced the average vehicle travel time by up to 10.1% compared with the standard user equilibrium traffic assignment for highly congested conditions, producing a solution closer to the system optimum traffic assignment. The results indicate that the proposed multi-objective eco-routing strategy can reduce vehicle fuel/energy consumption effectively with minimum impacts on travel times for both BEVs and ICEVs.

A review on powertrain subsystems and charging technology in battery electric vehicles: Current and future trends

2021 ◽  
pp. 095440702110259
Author(s):  
Kunal Wagh ◽  
Pankaj Dhatrak
Keyword(s):  
Electric Vehicles ◽  
Combustion Engine ◽  
Future Research ◽  
Future Trends ◽  
Road Transportation ◽  
Market Prices ◽  
Local Pollution ◽  
Battery Electric Vehicles ◽  
Global Emission ◽  
Battery Technology

The transport industry is a major contributor to both local pollution and greenhouse gas emissions (GHGs). The key challenge today is to mitigate the adverse impacts on the environment caused by road transportation. The volatile market prices and diminishing supplies of fuel have led to an unprecedented interest in battery electric vehicles (BEVs). In addition, improvements in motor efficiencies and significant advances in battery technology have made it easier for BEVs to compete with internal combustion engine (ICE) vehicles. This paper describes and assesses the latest technologies in different elements of the BEV: powertrain architectures, propulsion and regeneration systems, energy storage systems and charging techniques. The current and future trends of these technologies have been reviewed in detail. Finally, the key issue of electric vehicle component recycling (battery, motor and power electronics) has been discussed. Global emission regulations are pushing the industry towards zero or ultra-low emission vehicles. Thus, by 2025, most cars must have a considerable level of powertrain electrification. As the market share of electric vehicles increases, clear trends have emerged in the development of powertrain systems. However, some significant barriers must be overcome before appreciable market penetration can be achieved. The objective of the current study is to review and provide a complete picture of the current BEV technology and a framework to assist future research in the sector.

scholarly journals Comprehensive Evaluation of the Sustainable Development of Battery Electric Vehicles in China

2019 ◽  
Vol 11 (20) ◽  
pp. 5635 ◽  
Author(s):  
Wang ◽  
Zhou ◽  
Li ◽  
Wei
Keyword(s):  
Life Cycle Assessment ◽  
Life Cycle ◽  
Energy Consumption ◽  
Electric Vehicles ◽  
Social Life ◽  
Sustainability Assessment ◽  
Rapid Development ◽  
Social Life Cycle Assessment ◽  
Battery Electric Vehicles ◽  
The Social

Due to the rapid growth in the total number of vehicles in China, energy consumption and environmental pollution are serious problems. The development of electric vehicles (EVs) has become one of the important measures for solving these problems. As EVs are in a period of rapid development, sustainability research on them is conducive to the timely discovery of—and solution to—problems in the development process, but current research on the sustainability of EVs is still scarce. Based on the strategic development direction of EVs in China, battery electric vehicles (BEVs) were chosen as the research object of this study. The theory and method of the life cycle sustainability assessment (LCSA) were used to study the sustainability of BEVs. Specifically, the indicators of the life cycle assessment (LCA) were constructed, and the GaBi software was used to assess the environmental dimensions. The framework of life cycle costing (LCC) was used to assess the economic dimensions from the perspective of consumers. The indicators of the social life cycle assessment (SLCA) of stakeholders were constructed to assess the social dimension. Then, the method of the technique for order preference by similarity to ideal solution (TOPSIS) was selected for multicriteria decision-making in order to integrate the three dimensions. A specific conclusion was drawn from a comparison of BEVs and internal combustion engine vehicles (ICEVs). The study found that the life cycle sustainability of ICEVs in China was better than that of BEVs. This result might be unexpected, but there were reasons for it. Through sensitivity analysis, it was concluded that the current power structure and energy consumption in the operation phase of BEVs had a higher environmental impact, and the high cost of batteries and the government subsidy policy had a higher impact on the cost of BEVs. Corresponding suggestions are put forward at the end of the article.

Forecasting the Ecology Effects of Electric Cars Deployment in Krasnodar Region: Learning Curves Approach

2018 ◽  
Vol 9 (1) ◽  
pp. 82 ◽  
Author(s):  
Svetlana RATNER ◽  
Marina ZARETSKAYA
Keyword(s):  
Energy Efficiency ◽  
Electric Vehicles ◽  
Environmental Effects ◽  
Combustion Engine ◽  
Environmental Indicators ◽  
Learning Curves ◽  
Transport Systems ◽  
Clear Understanding ◽  
Electric Cars ◽  
Krasnodar Region

One of the most urgent problems of modern urban agglomerations is the optimization of the structure and technological maintenance of transport systems. As one of the options to solve this problem, the development of electric vehicles (EV) is usually suggested. But the scientific community has still not developed a clear understanding of whether electric vehicles are a better alternative to traditional cars, considering all environmental indicators. The aim of this work is to develop a method of forecasting the environmental effects of diffusion of EV technologies and test it on the example of the Krasnodar region of Russia as a region with the highest motorization ratios in the country, a complicated ecologic situation in large cities, a high population density and a modern structure for energy generation.  The technical progress in energy efficiency of each technology is taken into consideration. We use learning theory as a methodological framework, which is common for solution of problems of forecasting technological development. According to the calculations, the total emissions from private motor vehicles, with an increase in energy efficiency of vehicles with internal combustion engine and increase penetration of electric vehicles should decrease in 2025 by 15% comparing business-as-usual scenario, despite a significant increase in the level of motorization (almost 65%). Thus, a wide spread of EV technologies is preferable from an environmental point of view. The proposed approach to predict the environmental effects of diffusion of EV technologies allows us to estimate the reduction in emissions from road transport in any region while maintaining the direction and speed of the following key trends: the growth of energy efficiency and environmental performance of traditional cars with combustion engines, the growth of the level of motorization of the population in Russia, and reduction of EVs costs. Additional effects of stimulating (or de-stimulating) policies are not considered in this model.

scholarly journals Comparisons of Real-World Vehicle Energy Efficiency with Dynamometer-Based Ratings and Simulation Models

2021 ◽  
Vol 12 (4) ◽  
pp. 161
Author(s):  
Karim Hamza ◽  
Kang-Ching Chu ◽  
Matthew Favetti ◽  
Peter Keene Benoliel ◽  
Vaishnavi Karanam ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Electric Vehicles ◽  
Fuel Economy ◽  
Real World ◽  
Hybrid Electric Vehicle ◽  
Energy Intensity ◽  
Hybrid Electric Vehicles ◽  
Combustion Engine ◽  
Simulation Models ◽  
Hybrid Electric

Software tools for fuel economy simulations play an important role during design stages of advanced powertrains. However, calibration of vehicle models versus real-world driving data faces challenges owing to inherent variations in vehicle energy efficiency across different driving conditions and different vehicle owners. This work utilizes datasets of vehicles equipped with OBD/GPS loggers to validate and calibrate FASTSim (software originally developed by NREL) vehicle models. The results show that window-sticker ratings (derived from dynamometer tests) can be reasonably accurate when averaged across many trips by different vehicle owners, but successfully calibrated FASTSim models can have better fidelity. The results in this paper are shown for nine vehicle models, including the following: three battery-electric vehicles (BEVs), four plug-in hybrid electric vehicles (PHEVs), one hybrid electric vehicle (HEV), and one conventional internal combustion engine (CICE) vehicle. The calibrated vehicle models are able to successfully predict the average trip energy intensity within ±3% for an aggregate of trips across multiple vehicle owners, as opposed to within ±10% via window-sticker ratings or baseline FASTSim.

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