Author: Brandon Michael GraverPublisher:
ISBN:
Category :
Languages : en
Pages : 88
Book Description
Measurement, Prediction, and Evaluation of Microscale Energy Use and Emissions for a Plug-In Hybrid Electric Vehicle Based on Real-World Driving Data
Author: Brandon Michael GraverPublisher:
ISBN:
Category :
Languages : en
Pages : 88
Book Description
Measuring, Evaluating, and Modeling Energy Use and Emission Rates for a Plug-In Hybrid Electric Vehicle Based on Real-World Measurement
Author: Xiaohui ZhengUsing GPS Travel Data to Assess the Real World Driving Energy Use of Plug-in Hybrid Electric Vehicles (PHEVs)
Author: Publisher:
ISBN:
Category : Hybrid electric vehicles
Languages : en
Pages : 0
Book Description
On-road Emissions and Energy Efficiency Assessment of a Plug-in Hybrid Electric Vehicle
Author: Publisher:
ISBN: 9789279772160
Category :
Languages : en
Pages :
Book Description
In order to assess potential benefits brought by the electrification of transport it becomes more and more important to evaluate the performance of hybrid electric vehicles (HEVs) in real-driving conditions, measuring on-road air pollutant emissions and energy efficiency. The present report describes a portable system used at JRC for e-measurements in hybrid and electric vehicles, as an upgrade of the classic PEMS (Portable Emission Measurement System). Preliminary results of a test campaign conducted on a Euro-6 Plug-in Hybrid Passenger Car (PHEV) equipped with a Flywheel Alternator Starter (FAS) are reported. The influence of different driving modes as well as of different initial battery state of charge on CO2 and NOx emissions and energy consumption has been evaluated.
Well-to-wheels Analysis of Energy Use and Greenhouse Gas Emissions of Plug-in Hybrid Electric Vehicles
Author: Publisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Plug-in hybrid electric vehicles (PHEVs) are being developed for mass production by the automotive industry. PHEVs have been touted for their potential to reduce the US transportation sector's dependence on petroleum and cut greenhouse gas (GHG) emissions by (1) using off-peak excess electric generation capacity and (2) increasing vehicles energy efficiency. A well-to-wheels (WTW) analysis - which examines energy use and emissions from primary energy source through vehicle operation - can help researchers better understand the impact of the upstream mix of electricity generation technologies for PHEV recharging, as well as the powertrain technology and fuel sources for PHEVs. For the WTW analysis, Argonne National Laboratory researchers used the Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) model developed by Argonne to compare the WTW energy use and GHG emissions associated with various transportation technologies to those associated with PHEVs. Argonne researchers estimated the fuel economy and electricity use of PHEVs and alternative fuel/vehicle systems by using the Powertrain System Analysis Toolkit (PSAT) model. They examined two PHEV designs: the power-split configuration and the series configuration. The first is a parallel hybrid configuration in which the engine and the electric motor are connected to a single mechanical transmission that incorporates a power-split device that allows for parallel power paths - mechanical and electrical - from the engine to the wheels, allowing the engine and the electric motor to share the power during acceleration. In the second configuration, the engine powers a generator, which charges a battery that is used by the electric motor to propel the vehicle; thus, the engine never directly powers the vehicle's transmission. The power-split configuration was adopted for PHEVs with a 10- and 20-mile electric range because they require frequent use of the engine for acceleration and to provide energy when the battery is depleted, while the series configuration was adopted for PHEVs with a 30- and 40-mile electric range because they rely mostly on electrical power for propulsion. Argonne researchers calculated the equivalent on-road (real-world) fuel economy on the basis of U.S. Environmental Protection Agency miles per gallon (mpg)-based formulas. The reduction in fuel economy attributable to the on-road adjustment formula was capped at 30% for advanced vehicle systems (e.g., PHEVs, fuel cell vehicles [FCVs], hybrid electric vehicles [HEVs], and battery-powered electric vehicles [BEVs]). Simulations for calendar year 2020 with model year 2015 mid-size vehicles were chosen for this analysis to address the implications of PHEVs within a reasonable timeframe after their likely introduction over the next few years. For the WTW analysis, Argonne assumed a PHEV market penetration of 10% by 2020 in order to examine the impact of significant PHEV loading on the utility power sector. Technological improvement with medium uncertainty for each vehicle was also assumed for the analysis. Argonne employed detailed dispatch models to simulate the electric power systems in four major regions of the US: the New England Independent System Operator, the New York Independent System Operator, the State of Illinois, and the Western Electric Coordinating Council. Argonne also evaluated the US average generation mix and renewable generation of electricity for PHEV and BEV recharging scenarios to show the effects of these generation mixes on PHEV WTW results. Argonne's GREET model was designed to examine the WTW energy use and GHG emissions for PHEVs and BEVs, as well as FCVs, regular HEVs, and conventional gasoline internal combustion engine vehicles (ICEVs). WTW results are reported for charge-depleting (CD) operation of PHEVs under different recharging scenarios. The combined WTW results of CD and charge-sustaining (CS) PHEV operations (using the utility factor method) were also examined and reported. According to the utility factor method, the share of vehicle miles traveled during CD operation is 25% for PHEV10 and 51% for PHEV40. Argonne's WTW analysis of PHEVs revealed that the following factors significantly impact the energy use and GHG emissions results for PHEVs and BEVs compared with baseline gasoline vehicle technologies: (1) the regional electricity generation mix for battery recharging and (2) the adjustment of fuel economy and electricity consumption to reflect real-world driving conditions. Although the analysis predicted the marginal electricity generation mixes for major regions in the United States, these mixes should be evaluated as possible scenarios for recharging PHEVs because significant uncertainties are associated with the assumed market penetration for these vehicles. Thus, the reported WTW results for PHEVs should be directly correlated with the underlying generation mix, rather than with the region linked to that mix.
Predicting Energy Consumption for Potential Effective Use in Hybrid Vehicle Powertrain Management Using Driver Prediction
Author: Brian MagnusonPublisher:
ISBN:
Category :
Languages : en
Pages : 409
Book Description
A proof-of-concept software-in-the-loop study is performed to assess the accuracy of predicted net and charge-gaining energy consumption for potential effective use in optimizing powertrain management of hybrid vehicles. With promising results of improving fuel efficiency of a thermostatic control strategy for a series, plug-ing, hybrid-electric vehicle by 8.24%, the route and speed prediction machine learning algorithms are redesigned and implemented for real- world testing in a stand-alone C++ code-base to ingest map data, learn and predict driver habits, and store driver data for fast startup and shutdown of the controller or computer used to execute the compiled algorithm. Speed prediction is performed using a multi-layer, multi-input, multi- output neural network using feed-forward prediction and gradient descent through back- propagation training. Route prediction utilizes a Hidden Markov Model with a recurrent forward algorithm for prediction and multi-dimensional hash maps to store state and state distribution constraining associations between atomic road segments and end destinations. Predicted energy is calculated using the predicted time-series speed and elevation profile over the predicted route and the road-load equation. Testing of the code-base is performed over a known road network spanning 24x35 blocks on the south hill of Spokane, Washington. A large set of training routes are traversed once to add randomness to the route prediction algorithm, and a subset of the training routes, testing routes, are traversed to assess the accuracy of the net and charge-gaining predicted energy consumption. Each test route is traveled a random number of times with varying speed conditions from traffic and pedestrians to add randomness to speed prediction. Prediction data is stored and analyzed in a post process Matlab script. The aggregated results and analysis of all traversals of all test routes reflect the performance of the Driver Prediction algorithm. The error of average energy gained through charge-gaining events is 31.3% and the error of average net energy consumed is 27.3%. The average delta and average standard deviation of the delta of predicted energy gained through charge-gaining events is 0.639 and 0.601 Wh respectively for individual time-series calculations. Similarly, the average delta and average standard deviation of the delta of the predicted net energy consumed is 0.567 and 0.580 Wh respectively for individual time-series calculations. The average delta and standard deviation of the delta of the predicted speed is 1.60 and 1.15 respectively also for the individual time-series measurements. The percentage of accuracy of route prediction is91%. Overall, test routes are traversed 151 times for a total test distance of 276.4 km.
Empirical Fuel Consumption and Co Emissions of Plug-In Hybrid Electric Vehicles
Author: Patrick PlötzPublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
Plug-in hybrid electric vehicles (PHEVs) combine electric and conventional propulsion. Official fuel consumption values of PHEVs are based on standardized driving cycles, which show a growing discrepancy with real-world fuel consumption. However, no comprehensive empirical results on PHEV fuel consumption are available, and the discrepancy between driving cycle and empirical fuel consumption has been conjectured to be large for PHEV. Here, we analyze real-world fuel consumption data from 2,005 individual PHEVs of five PHEV models and observe large variations in individual fuel consumption with deviation from test-cycle values in the range of 2% to 120% for PHEV model averages. Deviations are larger for short-ranged PHEVs. Among others, range and vehicle power are influencing factors for PHEV model fuel consumption with average direct carbon dioxide (CO) emissions decreasing by 2% to 3% per additional kilometer (km) of electric range. Additional simulations show that PHEVs recharged from renewable electricity can noteworthily reduce well-to-wheel CO emissions of passenger cars, but electric ranges should not exceed 200 to 300 km since battery production is CO-intense. Our findings indicate that regulations should (1) be based on real-world fuel consumption measurements for PHEV, (2) take into account charging behavior and annual mileages, and (3) incentivize long-ranged PHEV.
An Activity-based Assessment of the Potential Impacts of Plug-in Hybrid Electric Vehicles (PHEVs) on Energy and Emissions Using One-day Travel Data
Author: Jee Eun KangRecommended Practice for Measuring the Exhaust Emissions and Fuel Economy of Hybrid-Electric Vehicles, Including Plug-in Hybrid Vehicles
Author: Light Duty Vehicle Performance and Economy Measure CommitteePublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
This Society of Automotive Engineers (SAE) Recommended Practice establishes uniform chassis dynamometer test procedures for hybrid-electric vehicles (HEVs) that are designed to be driven on public roads. The procedure provides instructions for measuring and calculating the exhaust emissions and fuel economy of HEVs driven on the Urban Dynamometer Driving Schedule (UDDS) and the Highway Fuel Economy Driving Schedule (HFEDS), as well as the exhaust emissions of HEVs driven on the US06 Driving Schedule (US06) and the SC03 Driving Schedule (SC03). However, the procedures are structured so that other driving schedules may be substituted, provided that the corresponding preparatory procedures, test lengths, and weighting factors are modified accordingly.Furthermore, this document does not specify which emissions constituents to measure (e.g., HC, CO, NOx, CO2); instead, that decision will depend on the objectives of the tester. The emissions calculations for plug-in hybrid-electric vehicle (PHEV) operation are provided as inventory results, weighted in the same manner as fuel and electrical energy consumption. Decisions for on-board versus off-board emissions, relative benefits of emissions-free driving, and how best to weight a "cold-start" cycle in charge-depleting (CD) mode must first be made before a certification methodology can be determined. Thus, calculations or test methodology intended to certify a PHEV for compliance of emissions standards is beyond the scope of this document.For purposes of this test procedure, an HEV is defined as a road vehicle that can draw propulsion energy from both of the following sources of stored energy: (1) a consumable fuel and (2) a rechargeable energy storage system (RESS) that is recharged by the on-board hybrid propulsion system, an external electric energy source, or both. Consumable fuels that are covered by this document are limited to petroleum-based liquid fuels (e.g., gasoline and Diesel fuel), alcohol-based liquid fuels (e.g., methanol and ethanol), and hydrocarbon-based gaseous fuels (e.g., compressed natural gas). The RESSs that are covered by this document include batteries, capacitors, and electromechanical flywheels. Procedures are included to test CD operating modes of HEVs designed to be routinely charged off-board, and calculations are provided that combine the CD and charge-sustaining (CS) behavior according to in-use driving statistics.The HEVs shall have an RESS with a nominal energy >2% of the fuel consumption energy of a particular test cycle to qualify to be tested with the procedures contained in this document.Single-roll, electric dynamometer test procedures are specified to minimize the test-to-test variations inherent in track testing and to conform to standard industry practice for exhaust emissions and fuel economy measurements.This document does not include test procedures for recharge-dependent (RD) operating modes or vehicles (see 3.1.2 for the definition).This document does not address the methods or equations necessary to calculate the adjusted U.S. Environmental Protection Agency (EPA) label miles per gallon (MPG) (sometimes referred to "EPA 5-Cycle" calculations). Hybrid-electric vehicle (HEV) technology has progressed significantly since the original publication of SAE standard J1711. The HEV has been in production for over a decade and parts of the original procedure have successfully addressed charge-sustaining HEVs. However, at the time of this revision, plug-in hybrid technology has experienced rapid development. As such, the procedures to address this technology needed to be revisited and modified to accommodate the operational possibilities demonstrated by the diverse set of working prototypes and simulated vehicles in the literature. Also, the list of standard test procedures addressed in SAE J1711 has been expanded to cover all five major test cycle procedures (UDDS, HFEDS, US06, SC03, and Cold FTP) now being used to evaluate vehicle fuel economy.
An Activity-based Assessment of the Potential Impacts of Plug-in Hybrid Electric Vehicles on Energy and Emissions Using One-day Travel Data
Author: Wilfred W. ReckerPublisher:
ISBN:
Category : Energy consumption
Languages : en
Pages : 48
Book Description