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Buses retrofitting with diesel particle filters: Real-world fuel economy and roadworthiness test considerations

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Journal of Environmental Sciences 2018年第5期30卷 273-286页

作者： Rafael Fleischman Ran Amiel Jan Czerwinski ANDreas Mayer Leonid Tartakovsky Faculty of Mechanical Engineering Technion–Israel Institute of Technology Labs for IC-Engines & Exhaust Emission Control(AFHB) Berne University of Applied Sciences VERT Association TTM

Retrofitting older vehicles with diesel particulate filter（DPF） is a cost-effective measure to quickly and efficiently reduce particulate matter emissions. This study experimentally analyzes real-world performance of buses retrofitted with CRT DPFs. 18 in-use Euro III technology urban and intercity buses were investigated for a period of 12 months. The influence of the DPF and of the vehicle natural aging on buses fuel economy are analyzed and discussed. While the effect of natural deterioration is about 1.2%–1.3%, DPF contribution to fuel economy penalty is found to be 0.6% to 1.8%, depending on the bus type. DPF filtration efficiency is analyzed throughout the study and found to be in average 96% in the size range of 23–560 nm. Four different load and non-load engine operating modes are investigated on their appropriateness for roadworthiness tests. High idle is found to be the most suitable regime for PN diagnostics considering particle number filtration efficiency.

关键词： Diesel particle filter Ultrafine particles Diesel bus fuel economy Vehicle natural aging Roadworthiness tests

fuel economy of multiple conditions self-adaptive tractors with hydro-mechanical CVT

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International Journal of Agricultural and Biological Engineering 2018年第3期11卷 102-109页

作者： Maohua Xiao Jing Zhao Yuewen Wang Haijun Zhang Zhixiong Lu Weihua Wei College of Engineering Nanjing Agricultural UniversityNanjing 210031China College of Mechanical and Electronic Engineering Nanjing Forestry UniversityNanjing 210037China

fuel economy is one of the most important properties of wheeled tractors.To improve the fuel economy,it is necessary to improve the engine fuel economy and the tractive efficiency of the driving mechanism.By analyzing the fuel economy evaluation index of wheeled tractors,the correlation among the tractor specific fuel consumption,the engine effective specific fuel consumption and the tractive efficiency of the driving mechanism were clarified in this paper.A tractor with a new type of hydro-mechanical continuously variable transmission(HMCVT)was designed and its whole model was established too.On the basis of each module’s simulation model,the binary speed ratio matching simulation model of the gearbox-engine system was obtained by the control system reconstruction,and the fuel economy simulation analysis of the wheeled tractor with HMCVT was carried out under the two typical working conditions of plowing and sowing.Compared with the current national standard,the fuel economy of the newly designed wheeled tractors with the power of 130 kW was 380 g/(kW·h),specific fuel consumption was reduced by 21.1%.The results showed that the new tractor fuel economy was greatly improved.Further,automatic navigation technology can be combined with the self-adaptive tractors to adapt to different working conditions and achieve the optimal fuel economy,which can be applied in precision agriculture.

关键词： high power wheeled tractor self-adaptive tractor HMCVT fuel economy navigation

Car fuel economy Simulation Forecast Method Based on CVT Efficiencies Measured from Bench Test

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Chinese Journal of Mechanical Engineering 2018年第5期31卷 138-153页

作者： Yu-Long Lei Yu-Zhe Jia Yao Fu Ke Liu Ying Zhang Zhen-Jie Liu State Key Laboratory of Automotive Simulation and Control Jilin University Faw Jiefang Automotive Co. Ltd United Automotive Electronic Systems Co. Ltd China North Vehicle Research Institute

Researchers face di culties in studying the e ects of driveline e ciency on car fuel economy via bench and road tests because of long working periods, high costs, and heavy workloads. To simplify the study process and shorten test cycles, a car fuel economy simulation forecast method for combining computer simulation forecasting with bench tests is proposed. Taking a continuously variable transmission(CVT) as the research object, a transmission e ?ciency model based on a bench test is constructed. An optimal economic variogram based on the original CVT vari?ogram, the boundary conditions of vehicle performance, the road conditions and the driving behavior of the driver is generated in the Gear Shift Program(GSP)?Generation module in AVL Cruise. And on this basis a driveline simulation model that can calculate the fuel consumption based on the driveline data of a test car is built. The model is used to forecast fuel consumption and calculate real?time CVT e ciency under di erent conditions. Contrastive analyses on simulation results and real car drum test results are made. The largest error between simulation results and drum test results in driving cycles is 4.099%, which is 5.449% under constant velocity condition in driver control mode and 4.2% under constant velocity condition in automatic cruise mode. The results confirm the feasibility of the method and the good performance of the driveline simulation model in accurately forecasting fuel consumption. The method can e ciently investigate the e ects of driveline e ciency on car fuel economy. Moreover, this research provides instruc?tion for accurately forecasting fuel economy as well as references for studies on the e ects of drivelines on car fuel economy.

关键词： fuel economy CVT efficiency Simulation forecast Driveline

Evaluation on the fuel economy of Automated Vehicles with Data-Driven Simulation Method

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能源与人工智能(英文)

能源与人工智能(英文) 2021年第1期3卷 192-203页

作者： Yueqi Luo Dang Xiang Song Zhang Weiming Liang Jian Sun Lei Zhu Intelligent Driving Center SAIC Motor Cooperation201 Anyan RoadShanghai 201804China Key Laboratory of Road and Traffic Engineering in the Ministry of Education Tongji University4800 Caoan RoadShanghai 201804China Key Laboratory of Power Machinery and Engineering Ministry of EducationShanghai Jiao Tong University800 Dongchuan RoadShanghai 200240China

With more commercialized automated vehicles(AVs)shortly entering the market,evaluating their fuel economy has become an important topic.The traditional fixed-profile test methods only indicate the effect of powertrain efficiency on fuel economy and cannot reflect the influences of control algorithms or driving behaviors on fuel consumption under real-world traffic conditions.Therefore,a data-driven simulation method for evaluating the real driving fuel economy of automated vehicles is developed.It utilizes naturalistic driving data to reconstruct test cycles.This method can inspect the performance of automated vehicle control algorithms under realistic traffic conditions in terms of fuel economy.The naturalistic driving data collected on urban expressways and freeways were used to model the longitudinal driving scenarios.Then,the fuel consumption of automated and human-driven vehicle was evaluated under the simulated scenarios via the Monte Carlo integration approach.It was found that the fuel consumption rate of the automated vehicle was 11.944 L/100 km under the car-following scenarios.The human-driven vehicle had a fuel consumption rate of 10.124 L/100 km under the same traffic conditions.The tested automated vehicle control algorithm is tuned to achieve better performance in terms of safety and travel efficiency and hence it tends to maintain a relatively steady time headway to the leading vehicle.It applies more frequent accelerating/braking cycles and higher average speed compared to typical human drivers.These characteristics lead to a higher fuel consumption rate of the automated vehicle.The presented method provides an accurate and efficient way to analyze the fuel economy performance of automated vehicles under practical conditions.This method can easily be scaled for large-scale traffic flow analyses.It can also be used to study the effects of human driving styles on fuel economy.

关键词： Automated vehicles fuel economy Data-driven evaluation Driving behaviors Monte Carlo simulation

Comparative Effect of Gasoline Formulations on fuel economy and Emissions of Modern GDI Engine

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China Petroleum Processing & Petrochemical Technology 2020年第4期22卷 10-20页

作者： Han Lu Tian Huayu Li Bo Guo Xin SINOPEC Research Institute of Petroleum Processing Beijing 100083

In this paper the effect of gasoline formulations on fuel economy and emissions were studied,aiming at exploring the optimized fuel formulation that can alleviate energy crisis and greenhouse effect to some extent.Five gasoline blends with same research octane number(RON)were designed and tested on a calibrated gasoline direct injection(GDI)engine under the mapped characteristic conditions.Test results illustrate that the optimized fuel formulation shows good superiority in fuel economy,and reduces carbon dioxide(CO2)emissions at low engine speeds with medium loads.The brake-specific fuel consumption(BSFC)decreased by a maximum value of 3.26%mainly because of the improvement of combustion velocity and the optimization of low heating value.The optimized fuel formulation simultaneously increases total hydrocarbon(THC)emissions.Nevertheless,it also markedly reduces CO2 emissions,reaching the maximum value of 2.34%.The research results can be applied practically by refineries to reduce the CO2 emissions and to alleviate the greenhouse effect.

关键词： gasoline formulations aromatic hydrocarbons fuel economy GDI engine

Model for evaluation on fuel economy of urban intersection

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Journal of Harbin Institute of Technology(New Series) 2011年第3期18卷 79-83页

作者：冯雨芹张亚平冷军强孙庚冯艳红 School of Transportation Science and Engineering Harbin Institute of Technology School of Automobile and Transportation Engineering Heilongjiang Institute of Technology School of Automobile Engineering Harbin Institute of Technology at Weihai School of Information Engineering Dalian Ocean University

To provide theoretical foundation for reducing fuel consumption at urban intersection,the relationship among traffic state,signal control parameters and fuel economy was studied.The concept of economic saturation was introduced,and evaluation parameter of traffic fuel economy of intersection was proposed.With a large number of field observation data of fuel consumption,models parameters of evaluation on fuel economy in VISSIM were calibrated.The evaluation models were established separately for lane group,approach and intersection with the fuel consumption data under different saturations and splits.In order to demonstrate the performance of the proposed model,a practical intersection was chosen as an example to evaluate.The results showed that it can accurately describe the relationship among traffic state,signal control parameters and fuel economy.It might provide the theoretical basis for improvement traffic management from the perspective of reducing energy consumption.

关键词： fuel economy intersection traffic state saturation split

fuel consumption reduction effect of pre-acceleration before gliding of a vehicle with free-wheeling

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Control Theory and Technology 2022年第2期20卷 235-247页

作者： Wenjing Cao Taketoshi Kawabe Tsuyoshi Yuno Xiaoliang Huang Department of Engineering and Applied Sciences Faculty of Science and TechnologySophia UniversityTokyoJapan Graduate School and Faculty of Information Science and Electrical Engineering Kyushy UniversityFukuokaJapan Department of Electrical Engineering Chalmers University of TechnologyGothenburgSweden

Advanced fuel economy strategies are expected to reduce the fuel consumption of vehicles.An internal combustion engine(ICE)driving vehicle equipped with free-wheeling turns off the fuel injection and decouples the engine from the drivetrain when the driving force is not required.This paper proposes a method to reduce the fuel consumption of a vehicle equipped with free-wheeling.First,an optimization problem is formulated to minimize the fuel consumption of a vehicle with free-wheeling when the traveling distance,the initial and final speed are specified and the vehicle needs to glide before arriving at the end point for fuel economy.The speed profile of the vehicle,engine operating point,and engine on/off timing are obtained as the results of the optimization.The analytical and numerical analyses results demonstrate the effectiveness and the fuel-saving mechanism of the obtained speed profile.The main finding of the analyses is that rather than starting a gliding stage immediately after an acceleration or a constant speed stage,adding a pre-acceleration stage before the gliding stage is more fuel-economic under some conditions independent of the complexity of the vehicle model.The obtained speed profile including a pre-acceleration stage is applied to a driving scenario including traffic congestions.The results demonstrate the effectiveness of the pre-acceleration stage in reducing fuel consumption for a vehicle equipped with free-wheeling.

关键词： Powertrain Free-wheeling Speed profile Operational mode of engine fuel economy Nonlinear optimization

A machine learning pipeline for fuel-economical driving model

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International Journal of Intelligent Computing and Cybernetics 2022年第4期15卷 473-496页

作者： Neetika Jain Sangeeta Mittal Jaypee Institute of Information Technology NoidaIndia

Purpose-A cost-effective way to achieve fuel economy is to reinforce positive driving behaviour.Driving behaviour can be controlled if drivers can be alerted for behaviour that results in poor fuel economy.fuel consumption must be tracked and monitored instantaneously rather than tracking average fuel economy for the entire trip duration.A single-step application of machine learning(ML)is not sufficient to model prediction of instantaneous fuel consumption and detection of anomalous fuel economy.The study designs an ML pipeline to track and monitor instantaneous fuel economy and detect anomalies.Design/methodology/approach-This research iteratively applies different variations of a two-step ML pipeline to the driving dataset for hatchback cars.The first step addresses the problem of accurate measurement and prediction of fuel economy using time series driving data,and the second step detects abnormal fuel economy in relation to contextual information.Long short-term memory autoencoder method learns and uses the most salient features of time series data to build a regression model.The contextual anomaly is detected by following two approaches,kernel quantile estimator and one-class support vector machine.The kernel quantile estimator sets dynamic threshold for detecting anomalous behaviour.Any error beyond a threshold is classified as an anomaly.The one-class support vector machine learns training error pattern and applies the model to test data for anomaly detection.The two-step ML pipeline is further modified by replacing long short term memory autoencoder with gated recurrent network autoencoder,and the performance of both models is compared.The speed recommendations and feedback are issued to the driver based on detected anomalies for controlling aggressive behaviour.Findings-A composite long short-term memory autoencoder was compared with gated recurrent unit autoencoder.Both models achieve prediction accuracy within a range of 98%-100%for prediction as a first step.Recall and accuracy metrics for anomaly det

关键词： Eco-driving fuel economy Contextual model Naturalistic driving Deep learning Anomaly detection Long short term memory Gated recurrent unit

A multivariable output neural network approach for simulation of plug-in hybrid electric vehicle fuel consumption

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Green Energy and Intelligent Transportation 2023年第2期2卷 66-84页

作者： Bukola Peter Adedeji Department of Industrial Systems Engineering University of ReginaSKS4S 0A2Canada

This study is laser focused on the simulation of fuel consumption and fuel economy label parameters of plug-in hybrid electric vehicles.While fuel economy is a key factor in the design of plug-in hybrid electric vehicles,a fuel economy label can educate customers about the economic advantage of purchasing a particular car.The fuel economy label of a PHEV consists of parameters like driving range,electrical energy consumption,fuel economy for city,highway,and combined use,battery recharge time,and fuel consumption rates.The study used an inverse function model of an artificial neural network to simulate and calculate the parameters of the fuel economy labels of PHEVs.Firstly,the selected parameters of the fuel economy label of plug-in hybrid electric vehicles were used to develop a single output model.The output variable of the single output model was then merged with dummy functions to form input variables for the inverse function model.The output variables simulated were engine size in litres;estimated driving range when the battery is fully charged in km,battery recharged time in hours,city fuel consumption(L/100 km),highway fuel consumption(L/100 km),combined fuel consumption(L/100 km),estimated driving range when the tank is full,carbon dioxide(CO_(2))emission in grams/km,electric motor power in kW,number of cylinders,and electrical charges consumed in kWh/100 km.Different cases of input variables were considered for the inverse function model.The accuracy of the model was 29.1 times greater than that of the conventional inverse artificial neural network model.

关键词： Plug-in hybrid electric vehicles fuel economy fuel economy label Artificial neural network Simulation