Influence of driving cycles on exhaust emissions and fuel consumption of gasoline passenger car in Bangkok

The influence of different driving cycles on their exhaust emissions and fuel consumption rate of gasoline passenger car was investigated in Bangkok based on the actual measurements obtained from a test vehicle driving on a standard chassis dynamometer. A newly established Bangkok driving cycle （BDC） and the European driving cycle （EDC） which is presently adopted as the legislative cycle for testing automobiles registered in Thailand were used. The newly developed BDC is constructed using the driving characteristic data obtained from the real on-road driving tests along selected traffic routes. A method for selecting appropriate road routes for real driving tests is also introduced. Variations of keyed driving parameters of BDC with different driving cycles were discussed. The results showed that the HC and CO emission factors of BDC are almost two and four times greater than those of EDC, respectively. Although the difference in the NOx emission factor is small, the value from BDC is still greater than that of EDC by 10%. Under BDC, the test vehicle consumes fuel about 25% more than it does under EDC. All these differences are mainly attributed to the greater proportion of idle periods and higher fluctuations of vehicle speed in the BDC cycle. This result indicated that the exhausted emissions and fuel consumption of vehicles obtained from tests under the legislative modal-type driving cycle （EDC） are significantly different from those actually produced under real traffic conditions especially during peak periods.

Driving Range Parametric Analysis of Electric Vehicles Driven by Interior Permanent Magnet Motors Considering Driving Cycles

This paper presents parametric analysis of driving range of electric vehicles driven by V-type interior permanent magnet motors aiming at maximum driving range,i.e.,minimal total energy consumption of the motors over a driving cycle.Influence of design parameters including tooth width,slot depth,split ratio(the ratio of inner diameter to outer diameter of the stator),and V-type magnet angle on the energy consumption of the motors and driving range of electric vehicles over a driving cycle is investigated in detail.The investigation is carried out for two typical driving cycles with different characteristics to represent different conditions:One is high-speed,low-torque cycle-Highway Fuel Economy Test and the other is low-speed,high-torque cycle-Artemis Urban Driving Cycle.It shows that for both driving cycles,the same parameters may have different influence on the energy consumption of the motors,as well as driving range of electric vehicles.

A model-based driving cycle test procedure of electric vehicle batteries

The battery test methods are the key issues to investigate the energy-storage characteristics and dynamic characteristics of electric vehicle(EV) batteries.In this paper,the research advances of existing battery test methods as well as driving cycles are reviewed.An electric vehicle model that consists of EV dynamics model,battery model and electric motor model is built.The dynamic characteristics of the battery in frequency domain are analyzed.Based on the EV model and the frequency domain characteristics of the battery,a driving cycle test procedure of EV battery is proposed.The battery test procedure is able to reflect the real-world characteristics of EV batteries,and can be used as a universal EV battery test method.

Genetic-fuzzy HEV control strategy based on driving cycle recognition

A genetic-fuzzy HEV control strategy based on driving cycle recognition （DCR） was built. Six driving cycles were selected to represent different traffic conditions e.g. freeway, urban, suburb. A neural algorithm was used for traffic condition recognition based on ten parameters of each driving cycle. The DCR was utilized for optimization of the HEV control parameters using a genetic-fuzzy approach. A fuzzy logic controller （FLC） was designed to be intelligent to manage the engine to work in the vicinity of its optimal condition. The fuzzy membership function parameters were optimized using the genetic algorithm （GA） for each driving cycle. The result is that the DCR_ fuzzy controller can reduce the fuel consumption by 1. 9%, higher than only CYC _ HWFET optimized fuzzy （0.2%） or CYC _ WVUSUB optimized fuzzy （0.7%）. The DCR_ fuzzy method can get the better result than only optimizing one cycle on the complex real traffic conditions.

Statistical Analysis for the Driving Cycle of Beijing's Bus

According to the test data of the driving model for Beijing's bus routes, 9 parameters and the actual values of Beijing bus are confirmed to evaluate the driving cycle, 2 ways of establishing driving cycle model are analyzed, the formula of calculating driving cycle is acquired, and the calculating driving cycle model and the statistical driving cycle model for the buses in Beijing urban areas are set up. This study provides scientific basis for selecting the bus type and confirming the design parameters and the running method in Beijing.

Investigation of Automobile Driving Pattern on Real-Road Condition in Tianjin

Using a set of measuring system installed on a testing vehicle, 15 criterion numbers, which describe the details of vehicle′s driving pattern and emission characteristic on real road condition of Tianjin, are obtained from a large quantity of raw data. The results show that the characteristic of driving pattern in Tianjin is very different from that of ECE-15 and FTP-75. That is to say, neither of these two emission testing procedures is suitable in China. A new driving cycle is developed which is accordance with the driving pattern of Tianjin.This cycle can be used to evaluate the emission levels of vehicles under real-road condition in laboratory, and can be recommended as a testing procedure used in China.

Optimization and Performance Comparison of Hairpin?Winding PMSM for Electric Vehicles Under Drive Cycle

Aiming at the problem of large AC copper loss caused by skin effects and proximity effects,and low efficiency at high speed of the hairpin-winding permanent magnet synchronous motor(PMSM)for electric vehicles(EVs),this paper firstly established the electromagnetic analytical model of the hairpin winding to calculate AC resistance.And the finite element model(FEM)of the hairpin-winding driving motor is established to calculate the AC characteristic of the hairpin winding at different speeds and temperatures.Then,combining modified particle swarm optimization(MPSO)and FEM,a 60 k W hairpin-winding PMSM is optimized under driving cycle conditions,and the electromagnetic performance and heat dissipation performance are compared with that of the traditional strand-winding motor.Finally,a prototype is made and an experimental platform is built to test the efficiency Map and temperature rise of the hairpin-winding motor over the whole speed range and verify the accuracy of the proposed optimization design method.The results show that the hairpin-winding PMSM not only has higher slot filling rate,high?efficiency range and power density,but also has better heat dissipation performance,which is suitable for application in the field of electric vehicles.

Modeling and Control of Parallel Hybrid Electric Vehicle Using Sea-Lion Optimization

This paper develops a parallel hybrid electric vehicle(PHEV)propor-tional integral controller with driving cycle.To improve fuel efficiency and reduce hazardous emissions in hybrid electric vehicles(HEVs)combine an electric motor(EM),a battery and an internal combustion engine(ICE).The electric motor assists the engine when accelerating,driving longer highways or climbing hills.This enables the use of a smaller,more efficient engine.It also makes use of the concept of regenerative braking to maximize energy efficiency.In a Hybrid Electric Vehicle(HEV),energy dissipated while braking is utilized to charge the battery.The proportional integral controller was used in this paper to analyze engine,motor performance and the New European Driving Cycle(NEDC)was used in the vehicle driving test using Matlab/Simulink.The proportional integral controllers were designed to track the desired vehicle speed and manage the vehi-cle’s energyflow.The Sea Lion Optimization(SLnO)methods were created to reduce fuel consumption in a parallel hybrid electric vehicle and the results were obtained for the New European Driving Cycle.

Real-world vehicle emission factors in Chinese metropolis city—Beijing

The dynamometer tests with different driving cycles and the real-world tests are presented. Results indicated the pollutants emission factors and fuel consumption factor with ECE15+EUDC driving cycle usually take the lowest value and with real world driving cycle occur the highest value, and different driving cycles will lead to significantly different vehicle emission factors with the same vehicle. Relative to the ECE15+EUDC driving cycle, the increasing rate of pollutant emission factors of CO, NOx and HC are -0.42—2.99, -0.32 —0.81 and -0.11—11 with FTP75 testing, 0.11—1.29, -0.77—0.64 and 0.47—10.50 with Beijing 1997 testing and 0.25—1.83, 0.09—0.75 and -0.58—1.50 with real world testing. Compared to the carburetor vehicles, the retrofit and MPI+TWC vehicles' pollution emission factors decrease with different degree. The retrofit vehicle(Santana) will reduce 4.44%—58.44% CO, -4.95%—36.79% NOx, -32.32%—33.89% HC, and -9.39%—14.29% fuel consumption, and especially that the MPI+TWC vehicle will decrease CO by 82.48%—91.76%, NOx by 44.87%—92.79%, HC by 90.00%—93.89% and fuel consumption by 5.44%—10.55%. Vehicles can cause pollution at a very high rate when operated in high power modes; however, they may not often operate in these high power modes. In analyzing vehicle emissions, it describes the fraction of time that vehicles operate in various power modes. In Beijing, vehicles spend 90% of their operation in low power modes or decelerating.

Driving-Cycle-Aware Energy Management of Hybrid Electric Vehicles Using a Three-Dimensional Markov Chain Model

This study developed a new online driving cycle prediction method for hybrid electric vehicles based on a three-dimensional stochastic Markov chain model and applied the method to a driving-cycle-aware energy management strategy.The impacts of different prediction time lengths on driving cycle generation were explored.The results indicate that the original driving cycle is compressed by 50%,which significantly reduces the computational burden while having only a slight effect on the prediction performance.The developed driving cycle prediction method was implemented in a real-time energy management algorithm with a hybrid electric vehicle powertrain model,and the model was verified by simulation using two different testing scenarios.The testing results demonstrate that the developed driving cycle prediction method is able to efficiently predict future driving tasks,and it can be successfully used for the energy management of hybrid electric vehicles.

Simulation of automobile fuel consumption and emissions for various driver's manual shifting habits

The effects of different habits of the drivers on gear shifting strategies for manual powertrain were investigated. For the realization of simulation, the shifting habits of the drivers were conducted in the Advisor software to investigate and compare the emission rates. Simulation was developed based on the optimal gear shifting strategy and criteria and was validated both in fuel economy and emissions by analyzing the results in the various driving cycle and driving styles. To explore an optimal gear shifting strategy with best fuel economy and lowest emission for a manual transmission, a strategy was designed with a highest possible gear criterion as long as the torque requirement can be satisfied. Based on two different criteria, namely the engine working conditions and the driver's intention, the governing parameters in decision making for gear shifting of manual transmission in conventional engine were discussed. It is also shown that the optimum gear shifting strategy is based on that both the engine state and the driver's intention eliminates unnecessary shiftings that are present when the intention is overlooked. The optimum shifting habit and the best driving cycle in terms of minimum emissions and fuel consumption were proposed.

Online power management strategy for plug-in hybrid electric vehicles based on deep reinforcement learning and driving cycle reconstruction

This paper proposes a novel power management strategy for plug-in hybrid electric vehicles based on deep reinforcement learning algorithm.Three parallel soft actor-critic(SAC)networks are trained for high speed,medium speed,and low-speed conditions respectively;the reward function is designed as minimizing the cost of energy cost and battery aging.During operation,the driving condition is recognized at each moment for the algorithm invoking based on the learning vector quantization(LVQ)neural network.On top of that,a driving cycle reconstruction algorithm is proposed.The historical speed segments that were recorded during the operation are reconstructed into the three categories of high speed,medium speed,and low speed,based on which the algorithms are online updated.The SAC-based control strategy is evaluated based on the standard driving cycles and Shenyang practical data.The results indicate the presented method can obtain the effect close to dynamic programming and can be further improved by up to 6.38%after the online update for uncertain driving conditions.

Performance Development Target Setting of Passenger Car Diesel Engine

Setting engine emission targets to meet diesel car requirements is particularly important in engine performance development phase. Many researches are focused on associating vehicle performance with engine targets, but most work is done by testing, which is time and cost consuming, furthermore, the relationship of vehicle and engine will change when either engine or vehicle changes. A GT-Drive model to simulate New European Driving Cycle (NEDC) for passenger car is developed and calibrated by testing data, model precision is controlled within 5%. Time distribution of engine operating conditions when car running NEDC cycle has been analyzed, 10 critical major engine operating points are summarized according to running time proportion. Emission of NOx and smoke control regions containing these 10 points for target engine are set. Vehicle emissions are simulated and evaluated during engine development after engine performance test data are got, and engine combustion system layout and calibration are adjusted until vehicle targets are met. Vehicle is tested in chassis dynamometer finally, the testing results show a good agreement with the simulated results with an error of less than 5%, which proves that the emission value exchange of vehicle and engine is reliable. Performance target decomposition method for passenger car diesel presented can greatly shorten the development cycle and save costs.

GPS Signal Correction to Improve Vehicle Location and Related Emission Evaluation

In recent years the progress of the automobile industry, in the field of reduction of emissions values, is very remarkable. Nevertheless their evaluation and reduction is a key problem, especially in the cities, that account for more than 50% of world population. So a correct evaluation of pollutant emissions and fuel consumption by vehicles in real use and precisely geolocated in a road is an important activity and it is still open in the international scientific contexts. A particular attention was given to the slope variability along the streets during each journey performed by the instrumented vehicle. In this paper we deal with the problem of describing a quantitatively approach for the reconstruction of GPS coordinates and altitude, in the context of correlation study between driving cycles / emission / geographical location.

Constructing representative driving cycle for heavy duty vehicle based on Markov chain method considering road slope

Electrification of heavy duty vehicles(HDVs)is critical to realization of the target of carbon neutralization in the future.For most HDVs,the influence of road slope on vehicle power usually cannot be ignored due to significant road slope variation during long driving mileages.In order to design the powertrain system for electrified HDVs effectively,it is necessary to construct representative driving cycles with road slope information.There are two difficulties for this task.(1)Road slope measuring devices are usually costly.A cheaper yet effective method for measuring road slope needs to be developed.(2)A 3D(three dimension)Markov chain method is usually utilized for constructing cycles with velocity and road slope.This method is complex and time consuming,and needs to be improved.In this paper,a 2D(two dimension)Markov chain method for addressing these issues is proposed.A road slope observation is designed based on normal GPS(Global Positioning System)signals and a high order Butterworth filter.The effectiveness of the method is validated.Driving velocity and road slope are collected and observed for the area between Beijing and Zhangjiakou in northern China.Representative cycles with road slope are constructed using a 2D Markov chain method and a matching algorithm based on average speed.With the introduced technology,three representative driving cycles with road slope for urban,suburban and highway routes are designed.Statistic results on vehicle power show that,the representative driving cycles are effective with relative errors less than 4%compared to the real driving conditions.These driving cycles will be utilized in designing electric HDVs,such as hydrogen fuel cell vehicles in the future.

Connectivity-Enhanced Route Selection and Adaptive Control for the Chevrolet Volt

The National Renewable Energy Laboratory and General Motors evaluated connectivity-enabled efficiency enhancements for the Chevrolet Volt. A high-level model was developed to predict vehicle fuel and electricity consumption based on driving characteristics and vehicle state inputs. These techniques were leveraged to optimize energy efficiency via green routing and intelligent control mode scheduling, which were evaluated using prospective driving routes between tens of thousands of real-world origin/destination pairs. The overall energy savings potential of green routing and intelligent mode scheduling was estimated at 5% and 3%, respectively. These represent substantial opportunities considering that they only require software adjustments to implement.

Real drive cycles analysis by ordered power methodology applied to fuel consumption,C0_(2),NO_(x) and PM emissions estimation

In this work three fuel consumption and exhaust emission models,ADVISOR,VT-MICRO and the European Environmental Agency Emission factors,have been used to obtain fuel consumption(FC)and exhaust emissions.These models have been used at micro-scale,using the two signal treatment methods presented.The manuscript presents:1)a methodology to collect data in real urban driving cycles,2)an estimation of FC and tailpipe emissions using some available models in literature,and 3)a novel analysis of the results based on delivered wheel power.The results include Fuel Consumption(FC),CO_(2),NO_(x) and PM_(10) emissions,which are derived from the three simulators.In the first part of the paper we present a new procedure for incomplete drive cycle data treatment,which is necessary for real drive cycle acquisition in high density cities.Then the models are used to obtain second by second FC and exhaust emissions.Finally,a new methodology named Cycle Analysis by Ordered Power(CAbOP)is presented and used to compare the results.This method consists in the re-ordering of time dependant data,considering the wheel mechanical power domain instead of the standard time domain.This new strategy allows the 5 situations in drive cycles to be clearly visualized:hard breaking zone,slowdowns,idle or stop zone,sustained speed zone and acceleration zone.The complete methodology is applied in two real drive cycles surveyed in Barcelona(Spain)and the results are compared with a standardized WLTC urban cycle.

Itinerary-Dependent Degradation Analysis of a Lithium-Ion Battery Cell for E-Bike Applications in Rwanda

There are obstacles to the widespread use of small electric vehicles(EVs)in Rwanda,including concerns regarding the battery range and lifespan.Lithium-ion batteries(LIBs)play an important role in EVs.However,their performance declines over time because of several factors.To optimize battery management systems and extend the range of EVs in Rwanda,it is essential to understand the influence of the driving profiles on lithium-ion battery degradation.This study analyzed the degradation patterns of a lithium-ion battery cell that propels an E-bike using various real-world E-bike driving cycles that represent Rwandan driving conditions under deep discharge(>80%).By being aware of these variables,battery failure can be slowed and improved battery performance can be achieved to promote the transition to cleaner transportation in Rwanda for the productive use of energy.The analyzed parameters that affect battery performance are temperature,driving cycles,and state of charge.It was found that the higher the temperature,the higher was the rate of fading.On the other hand,the EVs that operate in the region with higher elevation(hilly region)combined with a flat surface where the riders use their physical forces to propel the E-bike and their batteries lose their capacity rapidly compared to those operating in regions where the energy from the lithium-ion battery assists for the entire mileage.By draining the battery to 10%and charging it to 90%of its initial capacity,the capacity fading decreased by 5%.

Multi-objective parameter optimization for a single-shaft series-parallel plug-in hybrid electric bus using genetic algorithm

Recently, the single-shaft series-parallel powertrain of Plug-in Hybrid Electric Bus （PHEB） has become one of the most popu- lar powertrains due to its alterable operating modes, excellent fuel economy and strong adaptability for driving cycles. Never- theless, for configuring the PHEB with single-shaft series-parallel powertrain in the development stage, it still faces greater challenge than other configurations when choosing and matching the main component parameters. Motivated by this issue, a comprehensive multi-objectives optimization strategy based on Genetic Algorithm （GA） is developed for the PHEB with the typical powertrain. First, considering repeatability and regularity of bus route, the methods of off-line data processing and mathematical statistics are adopted, to obtain a representative driving cycle, which could well reflect the general characteristic of the real-world bus route. Then, the economical optimization objective is defined, which is consist of manufacturing costs of the key components and energy consumption, and combined with the dynamical optimization objective, a multi-objective op- timization function is put forward. Meanwhile, GA algorithm is used to optimize the parameters, for the optimal components combination of the novel series-parallel powertrain. Finally, a comparison with the prototype is carried out to verify the per- formance of the optimized powertrain along driving cycles. Simulation results indicate that the parameters of powertrain com- ponents obtained by the proposed comprehensive multi-objectives optimization strategy might get better fuel economy, meanwhile ensure the dynamic performance of PHEB. In contrast to the original, the costs declined by 18%. Hence, the strat- egy would provide a theoretical guidance on parameter selection for PHEB manufacturers.

Overall efficiency optimization of controllable mechanical turbo-compounding system for heavy duty diesel engines

A controllable mechanical turbo-compounding(CMTC) system including continuously variable transmission(CVT) and power turbine bypass valve is proposed to recover waste heat from engine exhaust. The combined matching principle considering swallowing capacity of both charging turbine and power turbine, main gear ratio is investigated at first based on the analysis of individual influence. Then the effects and strategies of CVT and power turbine bypass valve are studied for better performance under off-design conditions. At last, the transient response of intake pressure of engine with CMTC system is researched and the fuel saving potential is tested under driving cycle conditions. The results indicate that the overall fuel efficiency elevates at the off-design conditions if CVT is adopted due to the improvement of power turbine operating efficiency by speed modulation. The diversion of exhaust through power turbine bypass valve under the low load condition is necessary. The back pressure of the charging turbine infuences the transient response of intake pressure for a fixed CMTC configuration. A method featured by the assistance of power turbine bypass valve is tested to improve the transient response of the intake pressure. The fuel consumption reduces by 2% and 3.4% under highway fuel economy test(HWFET) and Tianjin 503(TJ503) driving cycles respectively.