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.

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.

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.

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.

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.

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.

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.

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.

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.

Experimental Study on the Combustion and Energy Flows of Vehicle Engine Under NEDC of Cold Start

To enhance the fuel economy of a vehicle powered by a gasoline engine under road conditions,an energy flow test of a vehicle was performed experimentally under the New European Driving Cycle of cold start.The energy distributions and related influencing factors were analyzed using the test data.Results show that the effective power and thermal efficiency are mainly affected by the engine load except in the early stage of the New European Driving Cycle.Because of the retarded CA50 and longer CA10-90,the effective thermal efficiency is lower in the early phase of driving conditions.Initially,the heat transfer loss mainly comprises the loss of the heating,ventilation,and air conditioning system.The radiator then plays the major role,with its percentage affected by the engine load and decreasing under the extra-urban driving cycle.The exhaust gas loss is decided by the temperature and flow rate of the exhaust gas,while its percentage is mainly affected by the temperature of the exhaust gas.In the early phase of driving conditions,the retarded spark advance angle leads to a higher temperature of the exhaust gas and a greater exhaust gas loss.The pumping loss and its percentage are mainly determined by the engine speed under the urban driving cycle,and both decrease under the extra-urban driving cycle except at maximum vehicle speed.

Critical Speeds of Electric Vehicles for Regenerative Braking

Efficient regenerative braking of electric vehicles(EVs)can enhance the efficiency of an energy storage system(ESS)and reduce the system cost.To ensure swift braking energy recovery,it is paramount to know the upper limit of the regenerative energy during braking.Therefore,this paper,based on 14 typical urban driving cycles,proposes the concept and principle of confidence interval of“probability event”and“likelihood energy”proportion of braking.The critical speeds of EVs for braking energy recovery are defined and studied through case studies.First,high-probability critical braking speed and high-energy critical braking speed are obtained,compared,and analyzed,according to statistical analysis and calculations of the braking randomness and likelihood energy in the urban driving cycles of EVs.Subsequently,a new optimized ESS concept is proposed under the frame of a battery/ultra-capacitor(UC)hybrid energy storage system(HESS)combined with two critical speeds.The battery/UC HESS with 9 UCs can achieve better regenerative braking performances and discharging performances,which indicates that a minimal amount of UCs can be used as auxiliary power source to optimize the ESS.After that,the efficiency regenerative braking model,including the longitudinal dynamics,motor,drivetrain,tire,and wheel slip models,is established.Finally,parameters optimization and performance verification of the optimized HESS are implemented and analyzed using a specific EV.Research results emphasize the significance of the critical speeds of EVs for regenerative braking.