Forward power-train energy management modeling for assessing benefits of integrating predictive traffic data into plug-in-hybrid electric vehicles

He, Yiming; Rios, Jackeline; Chowdhury, Mashrur; Pisu, Pierluigi; Bhavsar, Parth

doi:10.1016/j.trd.2011.11.001

Cited by 40 publications

(20 citation statements)

References 3 publications

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“…It was assumed that vehicles precisely followed the driving cycle input to the model. The power request was first calculated through vehicle dynamics with the parameters of the EV model in Table 2 [39], and then sent to the motor/generator (MG). The MG power in this model was set to 80 kW, which satisfied the power request in all driving tasks considered in this research.…”

Section: Ev Configurationmentioning

confidence: 99%

Connectivity supported dynamic routing of electric vehicles in an inductively coupled power transfer environment

Li¹,

Dey²,

Chowdhury³

et al. 2016

IET Intelligent Transport Systems

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Electric vehicle (EV) charging problem impedes its wide scale commercial adoption. In this study, the authors address this problem through an ant colony optimisation based multiobjective routing algorithm that is dedicated to accommodate EV trips. By using connectivity, EVs communicate with other vehicles and infrastructure components to transmit information in real time for finding the best route, and for intelligently recharging on the move using an inductively coupled power transfer system. Such connected EVs are capable of adapting each trip with the lowest travel time and/or the lowest recharge cost along with an optimal recharge plan to prevent a battery drain. As a case study, a real world roadway network in Charleston, South Carolina was simulated to examine the performance of the routing strategy. Simulation analysis revealed that connected EVs can reduce not only the total travel time and the energy consumption, but also the recharged volume of electricity and corresponding cost, thus significantly relieving the concerns of range anxiety of EV drivers. This routing approach potentially leads to a reduction in the EV battery capacity requirement, which in turn can reduce the cost of energy storage systems to a reasonable level.

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Section: Ev Configurationmentioning

confidence: 99%

Connectivity supported dynamic routing of electric vehicles in an inductively coupled power transfer environment

Li¹,

Dey²,

Chowdhury³

et al. 2016

IET Intelligent Transport Systems

Self Cite

View full text Add to dashboard Cite

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“…Another approach is tractive energy minimization; here, the penalized variable is tractive or propulsion energy demand at the wheels, and hence is more closely related to fuel consumption. Such an approach should in theory reduce fuel consumption to beyond acceleration optimization [7][8][9]. In Ref.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Different Objectives Functions for the Minimal Fuel Drive Cycle Optimization in Autonomous Vehicles

Prakash

Kim

Stefaopoulou

2019

Journal of Dynamic Systems, Measurement, and Control

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With the advent of self-driving autonomous vehicles, vehicle controllers are free to drive their own velocities. This feature can be exploited to drive an optimal velocity trajectory that minimizes fuel consumption. Two typical approaches to drive cycle optimization are velocity smoothing and tractive energy minimization. The former reduces accelerations and decelerations, and hence, it does not require information of vehicle parameters and resistance forces. On the other hand, the latter reduces tractive energy demand at the wheels of a vehicle. In this work, utilizing an experimentally validated full vehicle simulation software, we show that for conventional gasoline vehicles the lower energy velocity trajectory can consume as much fuel as the velocity smoothing case. This implies that the easily implementable, vehicle agnostic velocity smoothing optimization can be used for velocity optimization rather than the nonlinear tractive energy minimization, which results in a pulse-and-glide trajectory.

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“…Therefore some researches have been done about speed optimization of HEV in the ITS in recent years. The researchers at Clemson University and University of Minnesota carried out a series of studies on HEVs with the ITS [9][10][11][12]. In [11], the authors further minimized the energy consumption of plug-in HEVs (PHEVs) through V2I communication.…”

Section: Introductionmentioning

confidence: 99%

Research on Speed Optimization Strategy of Hybrid Electric Vehicle Queue Based on Particle Swarm Optimization

Wang

Shi

et al. 2018

Mathematical Problems in Engineering

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Traffic lights intersections are common in cities and have an impact on the energy consumption of vehicles, so it is significant to optimize the velocities of vehicles in urban road conditions. The novel speed optimization strategy for hybrid electric vehicle (HEV) queue that helps reduce fuel consumption and improve traffic efficiency is presented in this paper, where real-world traffic signal information is used to construct the research scenario. The initial values of the target velocities are obtained based on the signal phase and timing (SPAT). Then the particle swarm optimization (PSO) algorithm is used to solve the nonlinear constrained problem and obtain the optimal target velocities based on vehicle to vehicle communication (V2V) and vehicle to infrastructure communication (V2I). The lower controller, which applies rule based control strategy, is designed to split the power of the engine and two electric motors in a power split HEV, which is quite promising because of its advantages in fuel economy. Simulation results demonstrate the superior performance of the proposed strategy in reducing fuel consumption of the HEV queue and improving traffic smoothness.

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Forward power-train energy management modeling for assessing benefits of integrating predictive traffic data into plug-in-hybrid electric vehicles

Cited by 40 publications

References 3 publications

Connectivity supported dynamic routing of electric vehicles in an inductively coupled power transfer environment

Connectivity supported dynamic routing of electric vehicles in an inductively coupled power transfer environment

A Comparative Study of Different Objectives Functions for the Minimal Fuel Drive Cycle Optimization in Autonomous Vehicles

Research on Speed Optimization Strategy of Hybrid Electric Vehicle Queue Based on Particle Swarm Optimization

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