Energy consumption model of an electric vehicle

Abousleiman, Rami; Rawashdeh, Osamah

doi:10.1109/itec.2015.7165773

Cited by 40 publications

(22 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where F roll is the rolling resistance force, F grade is the grade related force, F air is the resistance force against the air, M eq is the mass of the vehicle, g is gravity, μ fr is the rolling resistance coefficient, ρ a is the air density, C d is the aerodynamic drag coefficient, A f is the frontal area, V EV is the vehicle speed, θ is the road grade, η t is the transmission efficiency, and η em is the motor average efficiency . Solving Eq.…”

Section: Modelingmentioning

confidence: 99%

Passive and Active Coupling Comparison of Fuel Cell and Supercapacitor for a Three‐Wheel Electric Vehicle

et al. 2019

View full text Add to dashboard Cite

The desire to reduce the power electronics related issues has turned the attentions to passive coupling of powertrain components in fuel cell hybrid electric vehicles (FCHEVs). In the passive coupling, the fuel cell (FC) stack is directly connected to an energy storage system on the DC bus as opposed to the active configuration where a DC‐DC converter couples the FC stack to the DC bus. This paper compares the use of passive and active couplings in a three‐wheel FCHEV to reveal their strengths and weaknesses. In this respect, a passive configuration, using a FC stack and a supercapacitor, is suggested first through formulating a sizing problem. Subsequently, the components are connected in an active configuration where an optimized fuzzy energy management strategy is used to split the power between the components. The performance of the vehicle is compared at each case in terms of capital cost and trip cost, which is composed of FC degradation and hydrogen consumption, and total cost of the system per hour. The obtained results show the superior performance of the passive configuration by 17% in terms of total hourly cost, while the active one only results in less degradation rate in the FC system.

show abstract

Section: Modelingmentioning

confidence: 99%

Passive and Active Coupling Comparison of Fuel Cell and Supercapacitor for a Three‐Wheel Electric Vehicle

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Besides, in view of the characteristics of braking energy regeneration of EVs, Fiori et al [12] and Chang et al [15] considered this feature in the kinetic energy consumption formula, the regenerative energy and the energy consumed can be distinguished and fused, and the braking regenerative energy can be calculated by using the instantaneous power parameters of EVs. However, instantaneous power does not fully reflect the total energy consumption of EVs over time, So, Abousleiman and Rawashdeh [16] and Wu et al [4] added the energy conversion coefficient to make the model reflect the energy conversion efficiency of EVs more accurately. Also, many models have been developed to estimate the impact of artificial factors on energy consumption of EVs.…”

Section: Literature Reviewmentioning

confidence: 99%

Drivingon GWB: energy‐efficiency‐driven route optimisation for EVs

Wang

Cai

Zhang

et al. 2019

IET Intelligent Transport Systems

View full text Add to dashboard Cite

“…To the authors' best of knowledge, although there have been numerous studies on modeling rail electric consumption, these studies were of limited application especially for road electric vehicles ( [33][34][35][36][37]). Specifically, they either cannot model train transient behavior or fail to capture energy regeneration at a microscopic level or are not simple enough to be implemented within complex systems (e.g.…”

Section: Existing Rail Energy Modeling Approachesmentioning

confidence: 99%

Electric train energy consumption modeling

Wang

2017

Applied Energy

View full text Add to dashboard Cite

The paper develops an electric train energy consumption modeling framework considering instantaneous regenerative braking efficiency in support of a rail simulation system. The model is calibrated with data from Portland, Oregon using an unconstrained non-linear optimization procedure, and validated using data from Chicago, Illinois by comparing model predictions against the National Transit Database (NTD) estimates. The results demonstrate that regenerative braking efficiency varies as an exponential function of the deceleration level, rather than an average constant as assumed in previous studies. The model predictions are demonstrated to be consistent with the NTD estimates, producing a predicted error of 1.87% and-2.31%. The paper demonstrates that energy recovery reduces the overall power consumption by 20% for the tested Chicago route. Furthermore, the paper demonstrates that the proposed modeling approach is able to capture energy consumption differences associated with train, route and operational parameters, and thus is applicable for project-level analysis. The model can be easily implemented in traffic simulation software, used in smartphone applications and eco-transit programs given its fast execution time and easy integration in complex frameworks.

show abstract

Energy consumption model of an electric vehicle

Cited by 40 publications

References 7 publications

Passive and Active Coupling Comparison of Fuel Cell and Supercapacitor for a Three‐Wheel Electric Vehicle

Passive and Active Coupling Comparison of Fuel Cell and Supercapacitor for a Three‐Wheel Electric Vehicle

Drivingon GWB: energy‐efficiency‐driven route optimisation for EVs

Electric train energy consumption modeling

Contact Info

Product

Resources

About