Highway Fuel Economy Test Cycle Research Articles

Research and evaluation of the arrangement of heat exchangers in electric vehicle thermal management systems for CO2 refrigerant

The application of transcritical CO2 cycle in the thermal management system of electric vehicles has become increasingly prevalent, but the existing thermal management system architecture has not been optimized to specifically enhance the performance of CO2 refrigerant. This paper analyzes the impact of various heat exchanger arrangements on CO2 thermal management system and evaluates system configurations based on a new performance index, cycle coefficient of performance. The effects of air temperature, ambient temperature, and heat load on system performance are conducted with a high precision model. The chiller is connected in parallel-series with indoor heat exchangers, resulting in a maximum coefficient of performance improvement of 27.89% at an indoor air inlet temperature of 10 °C, and a maximum coefficient of performance improvement of 21.69% at an ambient temperature of 5 °C, under simultaneous heating conditions. The radiator is placed on the windward of gas cooler, leading to a maximum coefficient of performance increase of 4.85% in heating mode, but a maximum coefficient of performance decrease of 9.84% in cooling mode. The evaluation of these configurations across different operating cycles reveals that the parallel-series connection is well-suited for the new European driving cycle, showing an 18.4% improvement in cycle coefficient of performance compared to the series-parallel connection. The windward arrangement of radiator proves effective for highway fuel economy test cycle in winter and the new European driving cycle in summer. This study provides effective instruction for configuration design and evaluation of transcritical CO2 vehicle thermal management systems.

Design of Two Fuel Cell Buses for Public Transport According to Two Different Operating Scenarios: Urban and Motorway

<div>The transport sector is one of the major parties responsible for carbon dioxide (CO<sub>2</sub>) and pollutants emissions in Europe. For this reason, one of the main commitments of the European Commission is its decarbonization by 2035/2040. To achieve this target, during the last decades, different propulsion technologies were developed such as hybrid electric vehicles (HEVs), plug-in electric vehicles (PHEVs), and battery electric vehicles (BEV). The first two proposals can be considered as bridging technology between the internal combustion engine (ICE) and the BEV because they offer at the same time comparable performance as conventional powertrains and improved efficiency. However, both technologies are struggling with the tightening of pollutants and CO<sub>2</sub> limits. On the other hand, the BEV can offer zero emissions at the tailpipe, but it suffers from limited range capabilities and the lack of fast-charging infrastructures. Within this context, the fuel cell vehicle (FCV) appears as an interesting opportunity because it offers zero tailpipe emissions and equivalent refuelling time of the ICE. This article evaluates through mathematical simulations the performance of two fuel cell electric buses (FCEBs), which are supposed to work respectively in urban and highway driving conditions. The urban bus is equipped with a single fuel cell (FC) module of 85 kW-Net and an electric motor (EM) of 225 kW. The intercity bus is equipped with two FC modules with a total power of 170 kW-Net and two EMs of 225 kW each. A sensitivity to the battery capacity from 20 kWh to 40 kWh was performed for both FECBs. The power split between the FC module and the high-voltage battery was optimized with the Equivalent Consumption Minimization Strategy (ECMS). The two FCEBs were tested considering different portfolios of cycles: in the case of the urban bus in Braunschweig and the Standardized On-Road Test Cycles SORT1 and SORT2 were assumed as a reference, while cycles like the Highway Fuel Economy Test (HWFET), European Transient Cycle (ETC), and cruising at 100 km/h were assumed as reference for the intercity. Simulation results highlighted that the increase of battery capacity in the case of the urban bus from 20 kWh to 30 kWh reduces hydrogen (H<sub>2</sub>) consumption by 11% along the Braunschweig cycle. On the other hand, in the case of the intercity bus, the fuel consumption is less affected by the increase of capacity in the same range. In this case a reduction of 4.7% is estimated for the HWFET cycle, and it is less than 1% in the case of cruising conditions.</div>

Repercussion of effect of different drive cycles on evaluation of electrical consumption for electric four‐wheeler to achieve optimal performance of electric vehicle

AbstractElectric vehicle (EV) is at borne stage and facing lot of challenges at design and development stage. The performance of EV is solely depending on distance travelled by vehicle means range of vehicle and electrical consumed by the vehicle. These two performance parameters affects on sizing of EV component. The electric motor and electric battery are power and electrical source of the vehicle. For sizing, this component electrical consumption (EC) per kilometer (km) plays major role. The correct estimation of EC per km is very much necessary at the design stage for sizing of components. Drive cycles play major role for accurate estimation of EC per km. Many drive cycles (Indian drive cycle [IDC], modified Indian drive cycle [MIDC], urban drive cycle [UDC], worldwide harmonized light vehicles test procedure [WLTP], new European drive cycle [NEDC], and Artemis drive cycle) are currently used by many countries to predict the performance of vehicle. Various standard cycles are studies the effect of drive cycle on energy consumption of an electric four‐wheeler through analytical methods which results the optimal performance of vehicle. Current study investigates the analytical approach for accurate prediction of energy consumption per km to enhance the range of EV, which is major concern of EV buyers. Present work compares the range and electric consumption of electric four‐wheeler by using NEDC, highway fuel economy test cycle (HWFET), UDC, WLTP, and MIDC drive cycle. The range obtained from NEDC, HWFET, UDC, WLTP, and MIDC drive cycles are 114.20, 115.18, 78.16, 103.33, and 93.73 km, respectively. HWFET drive cycle provides higher than other drive cycle due to better road condition. Urban drive cycle provides low range of 64 km because of road condition.

Energetic and Exergetic Performance Investigation of Different Topologies for Hybrid Fuel Cell Vehicles

The use of green energy has increased day by day. Environmentally friendly hybrid electric vehicles with low CO2 emissions have gained public attention. However, most battery electric vehicles are still having range problems, and emission values of hybrid electric vehicles are still not at the desired levels. Thus, fuel cell vehicles have gained some attention as good alternatives. The primary energy source for these vehicles is fuel cells, which are used in conjunction with batteries and supercapacitors to increase system performance. The combination of fuel cell + battery, fuel cell + supercapacitor, and fuel cell + battery + supercapacitor systems are currently the most popular topologies in this technology. The performance of these topologies is related to the overall energy efficiency and exergy efficiency of the vehicle model. Energy and exergy are two basic terminologies used to determine system performance and quality. Exergy determines the thermodynamic losses that cannot be determined using energy formulations alone. Thereby, it is very important to use both terminologies together to examine the performance of topologies and to determine any system losses. For this purpose, in this study, fuel cell + battery, fuel cell + supercapacitor, and fuel cell + battery + supercapacitor topologies were prepared and applied for Urban Dynamometer Driving Schedule (UDDS), Highway Fuel Economy Test Cycle (HWFET), New European Driving Cycle (NEDC), and Federal Test Procedure (FTP) driving cycles. Comparisons of these topologies in fuel consumption, power performance, and energy and exergy efficiencies were performed for the driving cycles. Also, energy flow, during the driving cycle, has showed and interpreted for the fuel cell vehicle that is designed and analyzed.

Hybrid Power Management Strategy with Fuel Cell, Battery, and Supercapacitor for Fuel Economy in Hybrid Electric Vehicle Application

The power management strategy (PMS) is intimately linked to the fuel economy in the hybrid electric vehicle (HEV). In this paper, a hybrid power management scheme is proposed; it consists of an adaptive neuro-fuzzy inference method (ANFIS) and the equivalent consumption minimization technique (ECMS). Artificial intelligence (AI) is a key development for managing power among various energy sources. The hybrid power supply is an eco-acceptable system that includes a proton exchange membrane fuel cell (PEMFC) as a primary source and a battery bank and ultracapacitor as electric storage systems. The Haar wavelet transform method is used to calculate the stress σ on each energy source. The proposed model is developed in MATLAB/Simulink software. The simulation results show that the proposed scheme meets the power demand of a typical driving cycle, i.e., Highway Fuel Economy Test Cycle (HWFET) and Worldwide Harmonized Light Vehicles Test Procedures (WLTP—Class 3), for testing the vehicle performance, and assessment has been carried out for various PMS based on the consumption of hydrogen, overall efficiency, state of charge of ultracapacitors and batteries, stress on hybrid sources and stability of the DC bus. By combining ANFIS and ECMS, the consumption of hydrogen is minimized by 8.7% compared to the proportional integral (PI), state machine control (SMC), frequency decoupling fuzzy logic control (FDFLC), equivalent consumption minimization strategy (ECMS) and external energy minimization strategy (EEMS).

Influences of alternative diesel fuels on tailpipe emissions and particulate matter under the highway fuel economy test cycle

Diesel engine vehicles are widely used worldwide in community and goods transport sectors due to their engine’s high efficiency and durability. Despite continuous improvements in various aspects, the combustion of compression ignition in conventional diesel engine is yet an important source of pollution and particulate matter that causes issues to human health and the environment. The objective of this research work is to study the use of conventional diesel (B7), biodiesel (B10) and premium diesel (DHi) fuels that affects to the release of exhaust gas and particulate matter emissions from tailpipes during a Highway Fuel Economy Test (HWFET) cycle. The experimental results have shown that the combustion of B7 and B10 reduced particulate matter by 0.38% and 51.0%, reduced total unburned hydrocarbon by 25.3% and 28.3%, reduced carbon monoxide by 43.3% and 64.0%, and reduced nitric oxide by 6.0% and 16.5%, respectively compared to DHi baseline on mass basis. The vehicle running on B10 releases a higher particle concentration with a smaller size compared to DHi.

Comparative Study of Physics-Based Modeling and Neural Network Approach to Predict Cooling in Vehicle Integrated Thermal Management System

Vehicle integrated thermal management system (VTMS) is an important technology used for improving the energy efficiency of vehicles. Physics-based modeling is widely used to predict the energy flow in such systems. However, physics-based modeling requires several experimental approaches to get the required parameters. The experimental approach to obtain these parameters is expensive and requires great effort to configure a separate experimental device and conduct the experiment. Therefore, in this study, a neural network (NN) approach is applied to reduce the cost and effort necessary to develop a VTMS. The physics-based modeling is also analyzed and compared with recent NN techniques, such as ConvLSTM and temporal convolutional network (TCN), to confirm the feasibility of the NN approach at EPA Federal Test Procedure (FTP-75), Highway Fuel Economy Test cycle (HWFET), Worldwide harmonized Light duty driving Test Cycle (WLTC) and actual on-road driving conditions. TCN performed the best among the tested models and was easier to build than physics-based modeling. For validating the two different approaches, the physical properties of a 1 L class passenger car with an electric control valve are measured. The NN model proved to be effective in predicting the characteristics of a vehicle cooling system. The proposed method will reduce research costs in the field of predictive control and VTMS design.

Design condition and operating strategy analysis of CO2 transcritical waste heat recovery system for engine with variable operating conditions

Waste heat recovery by means of a CO2 transcritical power cycle (CTPC) is suitable for dealing with high-temperature heat sources and achieving miniaturization. Considering the variable operating conditions of engines, the object of current work is to reveal the influence of design condition selection on CTPC systems. Two different engine operating conditions are chosen for system design. System performance has been predicted by a dynamic model and compared by net power output at off-design conditions. Constraints on temperatures, pressures and pump rotational speed have been taken into account. The results show that system designed under a partial load condition possesses a broad range of operation which will be beneficial to operate continuously when engine condition varies. The operating condition determined by driving cycles is recommended for system design of waste heat recovery for gasoline engines. Optimal performance can be obtained by adopting the mass flow rate guided operation strategy. Moreover, the average fuel consumption reduction during the Highway Fuel Economy Test Cycle over the original is 2.84% if system is designed under a partial condition. These preliminary results give reference to system design and optimization for waste heat recovery of engines based on thermodynamic cycles.

Neural Network Technique for Hybrid Electric Vehicle Optimization

Large inaccuracies exist in the car speed forecast due to the driver actions, the vehicle, and r2016oad conditions, which are not known a priori. Hence, real time scheduling using optimization methods is not feasible in general. So, an energy management system based on artificial neural network looking one step ahead is presented to minimize the cost of hydrogen and battery degradation. The optimum results of dynamic programming are used to provide a data set used to train an artificial neural network offline which would help solve the problem of real-time implementation. The inputs of the artificial neural network are fuel cell power, the battery state of charge, and the demand forecast; whereas, the output is the fuel cell power. The results obtained by the artificial neural network are compared to those obtained by dynamic programming and found to be very close. The artificial neural network is trained using the standard Urban Dynamometer Driving Schedule, and is able to provide charge sustaining and charge depletion operations. It is also tested on ten percent faster and ten percent slower variations of the same cycle, as well as on the Highway Fuel Economy Test Cycle and New European Driving cycle. The tests show a very good generalization capability of the developed artificial neural network on the different drive cycles.

Effects of the sulfur content of liquefied petroleum gas on regulated and unregulated emissions from liquefied petroleum gas vehicle

A more stringent standard for sulfur in liquefied petroleum gas (LPG) is currently being considered by the Ministry of Environment of Korea. Therefore, we investigated the characteristics of exhaust emissions from liquid phase LPG injection passenger car depending on the sulfur content of the LPG. Using a chassis dynamometer test with exhaust gas analyzers and Fourier-transform infrared spectroscopy, regulated emissions of carbon monoxide, non-methane hydrocarbons, and nitrates were investigated, as well as unregulated emissions of CH4, N2O, NH3, and SO2. Vehicle tests were carried out using Federal Test Procedure 75 (FTP-75) and Highway Fuel Economy Test (HWFET) cycles and time-resolved exhaust emissions and mass emissions were analyzed. The concentrations of regulated and unregulated time-resolved emissions increased during the cold start phase (Phase 1) of the FTP-75 cycle, in which the catalyst does not reach the light-off temperature. In the FTP-75 and HWFET cycles, most of the regulated and unregulated mass emissions increased as the sulfur content of the LPG increased. The SO2 concentration in the emissions increased in proportion to the sulfur content in the LPG, particularly in the high-speed portion of the FTP-75 cycle.

Developed Driving Cycles for a Passenger Vehicle in Khon Kaen

Traction forces that vehicles use to propel their wheels depend on efficiencies of motors, power transmissions, and vehicle constructions. Behavior of driver on acceleration or deceleration varies responding to road conditions, traffic lights, and driving styles. Average speed estimation of the vehicle under known road conditions can be obtained from the standard driving cycles such as the urban driving cycle by NEDC and the highway fuel economy test cycle (HWFET) by EPA. An urban area, Khon Kaen province located in Northeastern part of Thailand, mostly has flat and rough road conditions that have not been well recorded. In this study a GPS device was installed on a midsize vehicle to record speed and time on roundtrip test drives. In this paper, the data on speed of the vehicle traveled from Department of Electrical Engineering, Khon Kaen University to four nearby locations were investigated than compared with standard driving cycles. These data offer greater knowledge and development in power consumption of electric vehicles and transportation sector.

NMOG Emissions Characterizations and Estimation for Vehicles Using Ethanol-Blended Fuels

Ethanol is a biofuel commonly used in gasoline blends to displace petroleum consumption; its utilization is on the rise in the United States, spurred by the biofuel utilization mandates put in place by the Energy Independence and Security Act of 2007 (EISA). The United States Environmental Protection Agency (EPA) has the statutory responsibility to implement the EISA mandates through the promulgation of the Renewable Fuel Standard. EPA has historically mandated an emissions certification fuel specification that calls for ethanol-free fuel, except for the certification of flex-fuel vehicles. However, since the U.S. gasoline marketplace is now virtually saturated with E10, some organizations have suggested that inclusion of ethanol in emissions certification fuels would be appropriate. The test methodologies and calculations contained in the Code of Federal Regulations for gasoline-fueled vehicles have been developed with the presumption that the certification fuel does not contain ethanol; thus, a number of technical issues would require resolution before such a change could be accomplished. This report makes use of the considerable data gathered during the mid-level blends testing program to investigate one such issue: estimation of non-methane organic gas (NMOG) emissions. The data reported in this paper were gathered from over 600 cold-start Federal Testmore » Procedure (FTP) tests conducted on 68 vehicles representing 21 models from model year 2000 to 2009. Most of the vehicles were certified to the Tier-2 emissions standard, but several older Tier-1 and national low emissions vehicle program (NLEV) vehicles were also included in the study. Exhaust speciation shows that ethanol, acetaldehyde, and formaldehyde dominate the oxygenated species emissions when ethanol is blended into the test fuel. A set of correlations were developed that are derived from the measured non-methane hydrocarbon (NMHC) emissions and the ethanol blend level in the fuel. These correlations were applied to the measured NMHC emissions from the mid-level ethanol blends testing program and the results compared against the measured NMOG emissions. The results show that the composite FTP NMOG emissions estimate has an error of 0.0015 g/mile {+-}0.0074 for 95% of the test results. Estimates for the individual phases of the FTP are also presented with similar error levels. A limited number of tests conducted using the LA92, US06, and highway fuel economy test cycles show that the FTP correlation also holds reasonably well for these cycles, though the error level relative to the measured NMOG value increases for NMOG emissions less than 0.010 g/mile.« less