Realization and Analysis of Good Fuel Economy and Kinetic Performance of a Low-cost Hybrid Electric Vehicle

By using high-power and high-efficiency propulsion systems,current hybrid electric vehicles（HEVs） in market can achieve excellent fuel economy and kinetic performance.However,it is the cost of current HEVs that hinders HEVs coming into widespread use.A novel hybrid electric propulsion system is designed to balance HEV cost and performance for developing markets.A battery/supercapacitor-based hybrid energy storage system（HESS） is used to improve energy conversion efficiency and reduce battery size and cost.An all-in-one-controller（AIOC） which integrates engine electronic control unit（ECU）,motor ECU,and HESS management system is developed to save materials and energy,and reduce the influence of distribution parameters on circuit.As for the powertrain configuration,four schemes are presented：belt-driven starter generator（BSG） scheme,four-wheel drive HEV scheme,full HEV scheme,and ranger-extender electric vehicle（EV） scheme.Component selection and parameter matching for the propulsion system are performed,and an energy management strategy is developed based on powertrain configuration and selected components.Forward-facing simulation models are built,comprehending the control strategy based on the optimal engine torque for the low-cost hybrid electric propulsion system.Co-simulation of AVL CRUISE and Matlab/Simulink is presented and the best scheme is selected.The simulation results indicate that,for the best design,fuel consumption in urban driving condition is 4.11 L/（100 km） and 0-50 km/h accelerating time is 10.95 s.The proposed research can realize low-cost concept for HEV while achieving satisfactory fuel economy and kinetic performance,and help to improve commercialization of HEVs.

Car Fuel Economy Simulation Forecast Method Based on CVT Efficiencies Measured from Bench Test

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.

Comparative Effect of Gasoline Formulations on Fuel Economy and Emissions of Modern GDI Engine

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.

Model for evaluation on fuel economy of urban intersection

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.

A Fuel Economy Study in Heavy Duty Diesel Engine Lubricants

Internal combustion engines′ fuel economy is an important role for engine designers,engine manufacturers over the past 30 years,especially passenger car motor oils.In heavy duty diesel engine,over the past 20 years,fuel economy has in some cases been sacrificed for exhaust gas emission optimizations.Now,Heavy Duty Automotive and the related industries have strong interest in fuel economy and the lubricants.It is driven by competitive market forces as well as government mandates and new emission regulations.Japan was the first country in the world to establish and implement heavy duty trucks and buses fuel economy standards.Other countries also have followed either by establishing direct fuel economy standards or greenhouse gas(GHG) emissions standards which are directly tied to fuel economy.This paper is discussing that heavy duty diesel engine lubricants can contribute on fuel economy.The contribution of various aspects of engine oil formulations on fuel economy will be discussed such as lubricant viscosity grade,lubricant additives and friction modifiers.In this paper,the evaluation discussions are based on fuel economy measurements in some bench tests,standardized laboratory engine tests and field tests.

Combining Durability and Fuel Economy in Heavy Duty Diesel Lubricants

Durability has always been the primary function of an engine oil.This continues to be the case,but fuel economy improvement has risen in importance as a feature of a lubricant.This is most easily achieved by lower viscosity lubricants,which could endanger the durability of the engine if not properly formulated.Additionally,fuel economy from the lubricant is also considered over the course of a drain interval and the cost optimal point of servicing the oil explored,independent of other durability concerns.Durability is even more critical when the operating severities of the Chinese market are taken into account.Data are presented which demonstrate the differences in severity and the implications this will have on the lubricant,demonstrating the need for high quality lubricants that will protect Chinese vehicles in operation and,in addition,achieve some level of fuel economy improvement.

Study and Development of a Screener Test for Fuel Economy Passenger Car Motor Oil

Fuel economy regulations have been issued in many countries to save energy,and it is one of the most essential performance requirements for Passenger Car Motor Oil(PCMO) this decade.The performance is also important to reduce green house gas(GHG) emissions.Automotive,Oil and Additive industries have been developing fuel economy tests in fired engine for PCMO.The ASTM 5 Car test was started in 1982 and ASTM Sequence Ⅵ series test was also developed by the industries as one of fuel economy tests after the 5 Car test.However,the fired engine(combustion) test condition is more complicated and the combustion mode in engine introduce high variability into a fuel economy measurement.Screening by bench testing is complicated by the difficulty to reproduce friction conditions of all of engine parts.Based on the background,a motored engine friction torque test(MEFT) was developed as one of the solutions for fuel economy screening test.Using a newly developed MEFT,key additive chemistries were evaluated and compared in the test,and it distinguished the differences in additive chemistries,in addition to those in viscosity and friction modifiers.The Sequence Ⅵ-D FEI 1(Seq.Ⅵ-D FEI 1) and chassis dynamometer vehicle tests were also conducted in this study,and the test data has shown an excellent correlation among MEFT,Seq.Ⅵ-D FEI 1 and chassis dynamometer vehicle tests.

Development of Fuel Economy Gear Oil Technology

Vehicle fuel economy will continue to increase in importance as world vehicle production grows and fuel supplies become more limited year by year.As OEMs strive to produce cars and trucks with greater fuel efficiency and extended durability,additive technology developers are increasingly being asked to contribute to these goals from the lubricant side.Axle inefficiency can account for as much as 10% of the overall losses in an automotive driveline so improvements in axle efficiency can contribute greatly to improving vehicle fuel economy.For good durability,low axle oil operating temperatures are also needed to minimize oxidative and thermal degradation of the oil,reduce deposits and sludge formation,and extend oil drain intervals.To develop gear oils that can increase axle efficiency significantly while maintaining stable operating temperatures requires rig tests that are fast,precise and reproducible.This paper documents the development of a new axle test rig and test procedures and presents test results on several gear oils.The test results show the contributions of base oil viscosity,base oil chemistry,and additive chemistry on the fuel economy and temperature of the various oils.Having a dependable tool is enabling the development of new fuel-efficient and durable gear oil technology.

Improving Winter Traction and Fuel Economy of Car Tires Using Naphthenic Tire Oils

Naphthenic tire oils were used in winter tire tread compounding. Properties of compounds were compared with similar compounds made of other safe tire oils. Retreaded passenger car winter tires were prepared using the compounds. Traction and rolling resistance of the tires were determined in different weather conditions.It was shown that naphthenie oils may lead to improvement of winter traction and rolling resistance without compromising other tire properties.

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

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.

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

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.

Evaluation on the Fuel Economy of Automated Vehicles with Data-Driven Simulation Method

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.

The High Value CVT Concept--Cost Effective and Powerful

Based on the comprehensive comparison of vehicle performance in economy,engine power,driving smoothness,and efficiency cost as well as pollutant emission etc,the paper discussed the high value CVT concept from an angle of the cost effective and powerful for vehicle.In the paper,it researched the related technical detail in CVT.By means of realizing the continuous change in transmission ratio,it could obtain the optimal matching between transmission system and engine operating mode,and enhance the characteristic of fuel oil in economy,and also improve the convenience in manipulation for driver and make passenger comfortable.For easy to understand the concept,the paper made the comparison analysis in many aspects such as performance,transmission specification,high value CVT hybrid,orifice torque sensor,hydraulic system,high value CVT em,new chain portfolio and assessment of the high value CVT on the NEDC.Finally it showed the potential advantages of CVT technology development,and proposed future developing trends to realize technical scheme of high value CVT.

Using Less Oil Despite the unquenchable thirst for oil to fuel nina's fast growing economy,the oil consumption growth rate is dropping

At first glance, the official figures of China's oil consumption in 2005 seem a bit confusing. With a robust economic growth and rising annual oil imports, China made the surprise announcement that its oil consumption growth rate was dropping sharply, from 15.3 percent in 2004 to 2.1 percent in 2005. Earlier, China's Ministry

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

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.

Fuel efficient model predictive control strategies for a group of connected vehicles incorporating vertical vibration

This paper presents a decentralized fuel efficient model predictive control(MPC) strategy for a group of connected vehicles incorporating vertical vibration. To capture the vehicle vibration dynamics, the dynamics of the suspension system is integrated with the longitudinal dynamics of the vehicle. Furthermore, a MPC framework with finite time horizon is formulated to calculate the optimal velocity profile that compromises fuel economy, mobility and ride comfort for every individual vehicle with the safety and physical constraints considered. In the MPC framework, the target velocity is calculated using signal phase and timing(SPAT)information to reduce the number of stoppage at red lights, and the vertical acceleration is calculated parallel to the calculation of the fuel consumption. The MPC optimal problem is solved with fast-MPC approach which enhances the computational efficiency via exploiting the structure of the control system and approximate methods. Simulation studies are conducted over different SPATs and connectivity penetration rates and the results validate the advantages of the proposed control architecture.

Highly Fuel-Efficient Transmission and Propulsion Concepts

The integration of one or more electricmachines into the drivetrain has resulted inmany different powertrain concepts in recent years,ranging from the P2 hybrid to dedicated hybrid transmission(DHT).Two types of DHT with different characteristics are investigated.The first type is the power split hybrid(PS-DHT),which has very low mechanical complexity but needs high electrical effort in the transmission.The second type is multi-mode DHT(MM-DHT),which has a slightly higher mechanical complexity but much less electrical effort when compared with the PS-DHT.A transmission synthesis is used to determine the concept of the MM-DHT.The three different powertrain concepts(i.e.,P2,PS-DHT,and MM-DHT)are analyzed and evaluated regarding fuel economy and performance.Legal driving cycles(e.g.,Worldwide Harmonized Light vehicles Test Procedure)and the 3D method(driver,driven vehicle,driving environs)are used to investigate the drivetrain in the context of real driving operation.Results show that the two DHT concepts offer better fuel economy than the P2 hybrid drivetrain while still providing the same or even better driving performance.The study also shows that new hybrid concepts created with transmission synthesis can lead to further improvements in hybrid powertrains.

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

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.

A machine learning pipeline for fuel-economical driving model

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 detection using kernel quantile estimator remains within 98%-100%,whereas the one-class support vectormachine approach performs within the range of 99.3%-100%.Research limitations/implications-The proposed approach does not consider socio-demographics or physiological information of drivers due to privacy concerns.However,it can be extended to correlate driver’s physiological state such as fatigue,sleep and stress to correlate with driving behaviour and fuel economy.The anomaly detection approach here is limited to providing feedback to driver,it can be extended to give contextual feedback to the steering controller or throttle controller.In the future,a controller-based system can be associated with an anomaly detection approach to control the acceleration and braking action of the driver.Practical implications-The suggested approach is helpful in monitoring and reinforcing fuel-economical driving behaviour among fleet drivers as per different environmental contexts.It can also be used as a training tool for improving driving efficiency for new drivers.It keeps drivers engaged positively by issuing a relevant warning for significant contextual anomalies and avoids issuing a warning for minor operational errors.Originality/value-This paper contributes to the existing literature by providing anMLpipeline approach to track and monitor instantaneous fuel economy rather than relying on average fuel economy values.The approach is further extended to detect contextual driving behaviour anomalies and optimises fuel economy.The main contributions for this approach are as follows:(1)a prediction model is applied to fine-grained time series driving data to predict instantaneous fuel consumption.(2)Anomalous fuel economy is detected by comparing prediction error against a threshold and analysing error patterns based on contextual information.

Closed-form solution to the dynamic programming for a heavy-duty parallel hybrid vehicle energy management

Dynamic programming(DP)is frequently used to obtain the optimal solution to the hybrid electric vehicle(HEV)energy management.The trade-off between the accuracy and the computational effort is the biggest problem for the DP method.The closed-form solution to the DP is proposed to solve this problem.Firstly,the affine linear model of the engine fuel rate is obtained based on engine test data.The piecewise linear approximation of the motor power demand is obtained considering the different energy flows in the charging and discharging stages of the battery.Then,the second-order Taylor expansion for the cost matrix at each time and state grid point is introduced to get the closed-form solution of the optimal torque split.The results show that this method can greatly reduce the computing burden by 93%while ensuring near-optimal fuel economy compared with the conventional DP method.