scholarly journals Variability in Measured Real-World Operational Energy Use and Emission Rates of a Plug-In Hybrid Electric Vehicle

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1140 ◽  
Author(s):  
H. Christopher Frey ◽  
Xiaohui Zheng ◽  
Jiangchuan Hu
Keyword(s):  
Real World ◽  
Hybrid Electric Vehicle ◽  
Energy Use ◽  
Average Energy ◽  
Specific Power ◽  
Emission Measurement ◽  
Emission Rates ◽  
Wide Range ◽  
Hybrid Electric ◽  
Cold Starts

Compared to comparably sized conventional light duty gasoline vehicles (CLDGVs), plug-in hybrid electric vehicles (PHEVs) may offer benefits of improved energy economy, reduced emissions, and the flexibility to use electricity as an energy source. PHEVs operate in either charge depleting (CD) or charge sustaining (CS) mode; the engine has the ability to turn on and off; and the engine can have multiple cold starts. A method is demonstrated for quantifying the real-world activity, energy use, and emissions of PHEVs, taking into account these operational characteristics and differences in electricity generation resource mix. A 2013 Toyota Prius plug-in was measured using a portable emission measurement system. Vehicle specific power (VSP) based modal average energy use and emission rates are inferred to assess trends in energy use and emissions with respect to engine load and for comparisons of engine on versus engine off, and cold start versus hot stabilized running. The results show that, compared to CLDGVs, the PHEV operating in CD mode has improved energy efficiency and lower CO2, CO, HC, NOx, and PM2.5 emission rates for a wide range of power generation fuel mixes. However, PHEV energy use and emission rates are highly variable, with periods of relatively high on-road emission rates related to cold starts.

Digital Signal Processing Approach to Identify the Boundary of Vehicle Idle Status during Operation

2018 ◽  
Vol 2672 (25) ◽  
pp. 35-45
Author(s):  
Qing Li ◽  
Fengxiang Qiao ◽  
Lei Yu ◽  
Shuyan Chen ◽  
Tiezhu Li
Keyword(s):  
Fuel Consumption ◽  
Digital Signal ◽  
Operating Mode ◽  
Frequency Component ◽  
High Frequency Component ◽  
Specific Power ◽  
Emission Measurement ◽  
Emission Rates ◽  
Wide Range ◽  
Light Duty Vehicle

The MOVES is a tool to estimate on- and off-road emissions, in which 23 operating mode identification bins were defined based on vehicles’ specific power, speed, and acceleration. Bin 1 indicates an idling mode with the speed within 1.0 mph. However, the speed boundary in an earlier model of MOBILE 6.2 was 2.5 mph. Neither the change in the idling definition of the two models nor the speed boundary were investigated and discussed. This study proposed a method to theoretically redefine the idle boundary by characterizing vehicle emission rates. Vehicle speeds close to 0 mph were carefully studied based on 10,000-mile on-board emission tests in the state of Texas. A portable emission measurement system was used to detect real-time emissions from a 12-year-old gasoline light-duty vehicle, while the vehicle’s activity information was collected from an On-Board Diagnostic (OBD) II port. Power spectral density analysis was conducted on the collected emission and fuel consumption rates to identify a cut-off point that separates the frequency period with higher and lower energy. A Chebeshev I filter was designed to remove the high-frequency component to visualize the variables of emissions and fuel consumption on the vehicle’s moving trend lines. Based on observation and analysis results, 2.26 mph was identified as a boundary for an idle mode at an acceptance level of 95% significant change. It is recommended that the proposed method be applied to the emissions of more different types of vehicles with a wide range of mileages to validate the newly defined idle boundary.

Evaluation of the Precision and Accuracy of Cycle-Average Light Duty Gasoline Vehicles Tailpipe Emission Rates Predicted by Modal Models

2020 ◽  
Vol 2674 (7) ◽  
pp. 566-584
Author(s):  
Tongchuan Wei ◽  
H. Christopher Frey
Keyword(s):  
Goodness Of Fit ◽  
Motor Vehicle ◽  
Vehicle Group ◽  
Specific Power ◽  
Emission Measurement ◽  
Emission Rates ◽  
Light Duty ◽  
Wide Range ◽  
Gasoline Vehicles ◽  
Precision And Accuracy

A vehicle specific power (VSP) modal model and the MOtor Vehicle Emission Simulator (MOVES) Operating Mode (OpMode) model have been used to evaluate and quantify the fuel use and emission rates (FUERs) for on-road vehicles. These models bin second-by-second FUERs based on factors such as VSP, speed, and others. The validity of binning approaches depends on their precision and accuracy in predicting variability in cycle-average emission rates (CAERs). The objective is to quantify the precision and accuracy of the two modeling methods. Since 2008, North Carolina State University has used portable emission measurement systems to measure tailpipe emission rates for 214 light duty gasoline vehicles on 1,677 driving cycles, including 839 outbound cycles and 838 inbound cycles on the same routes. These vehicles represent a wide range of characteristics and emission standards. For each vehicle, the models were calibrated based on outbound cycles and were validated based on inbound cycles. The goodness-of-fit of the calibrated models was assessed using linear least squares regression without intercept between model-predicted versus empirical CAERs for individual vehicles. Based on model calibration and validation, the coefficients of determination ( R2) typically range from 0.60 to 0.97 depending on the vehicle group and pollutant, indicating moderate to high precision, with precision typically higher for higher-emitting vehicle groups. The slopes of parity plots for each vehicle group and all vehicles typically range from 0.90 to 1.10, indicating good accuracy. The two modeling approaches are similar to each other at the microscopic and macroscopic levels.

Trip Energy Estimation Methodology and Model Based on Real-World Driving Data for Green-Routing Applications

2018 ◽  
Vol 2672 (24) ◽  
pp. 41-48 ◽  
Author(s):  
Jacob Holden ◽  
Harrison Van Til ◽  
Eric Wood ◽  
Lei Zhu ◽  
Jeffrey Gonder ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Fuel Consumption ◽  
Real World ◽  
Energy Use ◽  
Average Energy ◽  
Estimation Model ◽  
Energy Estimation ◽  
Conventional Vehicle ◽  
Look Up Table ◽  
Wide Range

A data-informed model to predict energy use for a proposed vehicle trip has been developed in this paper. The methodology leverages roughly one million miles of real-world driving data to generate the estimation model. Driving is categorized at the sub-trip level by average speed, road gradient, and road network geometry, then aggregated by category. An average energy consumption rate is determined for each category, creating an energy rate look-up table. Proposed vehicle trips are then categorized in the same manner, and estimated energy rates are appended from the look-up table. The methodology is robust and applicable to a wide range of driving data. The model has been trained on vehicle travel profiles from the Transportation Secure Data Center at the National Renewable Energy Laboratory and validated against on-road fuel consumption data from testing in Phoenix, Arizona. When compared against the detailed on-road conventional vehicle fuel consumption test data, the energy estimation model accurately predicted which route would consume less fuel over a dozen different tests. When compared against a larger set of real-world origin–destination pairs, it is estimated that implementing the present methodology should accurately select the route that consumes the least fuel 90% of the time. The model results can be used to inform control strategies in routing tools, such as change in departure time, alternate routing, and alternate destinations to reduce energy consumption. This work provides a highly extensible framework that allows the model to be tuned to a specific driver or vehicle type.

Battery Sizing as a Function of Powertrain Component Efficiencies for Various Drive Cycles

2011 ◽  
Author(s):  
Lynn R. Gantt ◽  
R. Jesse Alley ◽  
Douglas J. Nelson
Keyword(s):  
Real World ◽  
Hybrid Electric Vehicle ◽  
Energy Use ◽  
Net Energy ◽  
Vehicle Speed ◽  
Drive Cycle ◽  
Component Efficiency ◽  
Technical Specifications ◽  
Hybrid Electric ◽  
The Individual

The market segment of hybrid-electric and full function electric vehicles is growing within the automotive transportation sector. While many papers exist concerning fuel economy or fuel consumption and the limitations of conventional powertrains, little published work is available for vehicles which use grid electricity as an energy source for propulsion. Generally, the emphasis is put solely on the average drive cycle efficiency for the vehicle with very little thought given to propelling and braking powertrain losses for individual components. The modeling section of this paper will take basic energy loss equations for vehicle speed and acceleration, along with component efficiency information to predict the grid energy consumption in AC Wh/km for a given drive cycle. An electric-only range target is established as part of the vehicle technical specifications. This set range along with component characteristics will impact the sizing of the energy storage subsystem. To demonstrate the usefulness in understanding powertrain losses, the energy use is described in propelling, braking, idle, and charging cases. A simulation focusing on battery sizing to meet power and range requirements shows the impacts of friction brakes, regenerative braking fraction, and average motor efficiency. Vehicle characteristics such as, but not limited to, a range extender application, electric-only vehicle range, and acceleration performance are explained as well. The model is correlated to real world vehicle data for a custom-built plug-in hybrid electric vehicle. By using the Virginia Tech Range Extended Crossover (VTREX) and collecting data from testing, the parameters that the model is based on will be correlated with real world test data. The paper presents a propelling, braking, and net energy weighted drive cycle averaged efficiency that can be used to calculate the losses for a given cycle. In understanding the losses at each component, not just the individual efficiency, areas for future vehicle improvement can be identified to reduce petroleum energy use and greenhouse gases.

scholarly journals Characterization of Real-World Particle Number Emissions during Reignition Events from a 2010 Light-Duty Hybrid Electric Vehicle

2015 ◽  
Vol 2503 (1) ◽  
pp. 137-146 ◽  
Author(s):  
Matt Conger ◽  
Britt A. Holmén
Keyword(s):  
Real World ◽  
Hybrid Electric Vehicle ◽  
Particle Number ◽  
Combustion Engine ◽  
Operating Mode ◽  
Emission Rates ◽  
Engine Operation ◽  
Conventional Vehicle ◽  
Hybrid Electric ◽  
Consumption Benefits

Despite the increasing popularity of hybrid electric vehicles (HEVs), few studies have compared the real-world particle emissions of HEVs from internal combustion engine (ICE) reignition events with that of a conventional vehicle (CV) during real-world driving. Reignition events occur under unstable combustion conditions and frequently result in particle number (PN) emission rates (PNERs) that exceed those for stabilized engine operation. Tailpipe PNERs from a CV and an HEV 2010 Toyota Camry were quantified on a 32-mi route over rural, urban, and freeway roadways in Chittenden County, Vermont, with the total onboard tailpipe emissions measurement system. This study directly compared the CV and HEV PNERs and characterized the operation of the HEV in a new HEV ICE operating mode framework. Mean PNER for reignition events (7.19 pM 11.8 × 1010 particles/s) were on average four times greater than for stabilized HEV operation (1.79 ± 3.99 × 1010 particles/s). Under urban, rural, and freeway driving, HEV reignition event operation accounted for 58.7%, 44.6%, and 5.0%, respectively, of the total PN inventory. Mean HEV PNER was 1.8 times greater than that of the CV in urban driving, while under freeway driving, where the two vehicles operated similarly, average CV PNER was 2.4 times greater than that of the HEV. The data show that the typical fuel consumption benefits of HEVs in urban driving are associated with a trade-off in PN emissions. The HEV ICE operating behavior has implications for the spatial distribution of PN hot spots as well as the associated microscale modeling of alternative vehicle technology emissions.

scholarly journals Development of a Hybrid Electric Vehicle Simulation Tool with a Rule-Based Topology

2021 ◽  
Vol 11 (23) ◽  
pp. 11319
Author(s):  
Hyun Woo Won
Keyword(s):  
Hybrid Electric Vehicle ◽  
Hybrid Simulation ◽  
Rule Base ◽  
Simulation Tool ◽  
Control Logic ◽  
Rule Based ◽  
Vehicle Simulation ◽  
Wide Range ◽  
Hybrid Electric ◽  
And Control

The performance of hybrid electric vehicles (HEVs) greatly depends on the various sub-system components and their architecture, and designers need comprehensive reviews of HEVs before vehicle investigation and manufacturing. Simulations facilitate development of virtual prototypes that make it possible to rapidly see the effects of design modifications, avoiding the need to manufacture multiple expensive physical prototypes. To achieve the required levels of emissions and hardware costs, designers must use control strategies and tools such as computational modeling and optimization. However, most hybrid simulation tools do not share their principles and control logic algorithms in the open literature. With this motivation, the author developed a hybrid simulation tool with a rule-based topology. The major advantage of this tool is enhanced flexibility to choose different control and energy management strategies, enabling the user to explore a wide range of hybrid topologies. The tool provides the user with the ability to modify any sub-system according to one’s own requirements. In addition, the author introduces a simple logic control for a rule-base strategy as an example to show the flexibility of the tool in allowing the adaptation of any logic algorithm by the user. The results match the experimental data quite well. Details regarding modeling principle and control logic are provided for the user’s benefit.

scholarly journals Evaluating the Effects of One-Way Traffic Management on Different Vehicle Exhaust Emissions Using an Integrated Approach

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Jieyu Fan ◽  
Kun Gao ◽  
Yingying Xing ◽  
Jian Lu
Keyword(s):  
Traffic Management ◽  
Integrated Approach ◽  
Exhaust Emissions ◽  
Traffic Emissions ◽  
Specific Power ◽  
Emission Measurement ◽  
Emission Rates ◽  
Vehicle Exhaust ◽  
Emission Data ◽  
The Road

One-way traffic management is a recognized traffic organization to improve traffic efficiency and safety, but its effects on different traffic emissions remains unclear. This paper aims to investigate the impacts of one-way traffic management on three typical vehicle exhaust emissions including Carbonic Oxide (CO), Hydrocarbon Compounds (HC), and Nitrogen Oxides (NOx) in a traffic system using an integrated approach. Field experiment was conducted to collect the vehicular emission data under different traffic conditions using the onboard portable emission measurement system. An instantaneous emission model (i.e., Vehicle Specific Power) is calibrated using the collected field emission data and is incorporated into the microscopic traffic simulation tool VISSIM for quantifying the emissions before and after one-way traffic management through simulation. Two scenarios based on real networks and traffic demands of peak hours in part areas of Shanghai are developed for simulation and evaluation. The results show that in the intersections, the emission rates of COHC, NOx after one-way traffic management is significantly reduced by 20.46%, 21.29% and 21.06%, respectively. In the road sections, the emission rates of CO, HC, NOx in the road sections decrease by 23.38% and 26.29%. The overall CO, HC, NOx emissions in the studied network reduce by 21.34%, 22.29% and 23.77% separately due to one-way traffic management. The results provide insights into the derivative effects of one-way traffic management on traffic emissions in the intersections, road sections and network levels, and thus support scientific traffic management for promoting the sustainability of transport system.

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