Characterization of vehicle driving patterns and development of driving cycles in Chinese cities

ABSTRACTAzithromycin resistance (AZM-R) ofNeisseria gonorrhoeaeis emerging as a clinical and public health challenge. We determined molecular characteristics of recent AZM-R Nanjing gonococcal isolates and tracked the emergence of AZM-R isolates in eastern Chinese cities in recent years. A total of 384N. gonorrhoeaeisolates from Nanjing collected from 2013 to 2014 were tested for susceptibility to AZM and six additional antibiotics; all AZM-R strains were characterized genetically for resistance determinants by sequencing and were genotyped usingN. gonorrhoeaemultiantigen sequence typing (NG-MAST). Among the 384 isolates, 124 (32.3%) were AZM-R. High-level resistance (MIC, ≥256 mg/liter) was present in 10.4% (40/384) of isolates, all of which possessed the A2143G mutation in all four 23S rRNA alleles. Low- to mid-level resistance (MIC, 1 to 64 mg/liter) was present in 21.9% (84/384) of isolates, 59.5% of which possessed the C2599T mutation in all four 23S rRNA alleles. The 124 AZM-R isolates were distributed in 71 different NG-MAST sequence types (STs). ST1866 was the most prevalent type in high-level AZM-R (HL-AZM-R) isolates (45% [18/40]). This study, together with previous reports, revealed that the prevalence of AZM-R inN. gonorrhoeaeisolates in certain eastern Chinese cities has risen >4-fold (7% to 32%) from 2008 to 2014. The principal mechanisms of AZM resistance in recent Nanjing isolates were A2143G mutations (high-level resistance) and C2599T mutations (low- to mid-level resistance) in the 23S rRNA alleles. Characterization of NG-MAST STs and phylogenetic analysis indicated the genetic diversity ofN. gonorrhoeaein Nanjing; however, ST1866 was the dominant genotype associated with HL-AZM-R isolates.

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Methodology for the Energy Characterization of Type-Approval and Real-World Driving Cycles for Passenger Vehicles

Volume 6A: Energy ◽

10.1115/imece2015-53669 ◽

2015 ◽

Cited By ~ 1

Author(s):

Luis A. S. B. Martins ◽

Bruno J. O. Araujo ◽

Jorge J. G. Martins ◽

Francisco C. P. Brito

Keyword(s):

Electric Vehicles ◽

Real World ◽

Hybrid Electric Vehicles ◽

Management Strategies ◽

Specific Power ◽

Traffic Conditions ◽

Driving Cycles ◽

Type Approval ◽

Hybrid Electric

The development of electric and hybrid electric vehicles is motivated by the high prices of fossil fuels, the need for better efficiency and the minimization of pollutants and greenhouse gas emissions. There are several possible technologies for these vehicles but Plug-in Hybrid Electric Vehicles (PHEV) and Fully Electric Vehicles (FEV) are becoming popular. They both require advanced energy storage and management systems. In the design of these powertrains it is of capital importance to evaluate, not only the required traction energy, but also the energy involved in braking and that has the possibility of being regenerated, in real-world routes and traffic conditions. Type-approval driving cycles are insufficient for this purpose, as they do not include parameters that substantially affect the vehicle dynamics, such as road slope and additional friction due to road winding. This work presents a methodology for the energy characterization of driving cycles, based on the numerical integration of specific power, including new parameters such as specific traction and braking energies, cumulative uphill and downhill slopes and cornering friction energy, as well as energy-power distributions. The methodology will help in the comparison of the available type-approval driving cycles and in the definition of more realistic ones that can be used for better assessment of fuel consumption and emissions of vehicles. With input data from real routes, the procedure will be useful in the design of advanced electrical or hybridized powertrain systems, both to size the components and to define appropriate energy management strategies, with the final goal of an improved efficiency. The methodology will also be valuable in the energy classification of European roads. The paper describes the mathematical model, which allows the quantification of all the important energy flows involved in the evolution of a reference vehicle, following a route. This model was developed in the MatLab/Simulink environment and was applied to the characterization of three type-approval cycles and to three real routes. The results indicate that the type-approval cycles are too soft to adequately emulate present day aggressive traffic conditions. Driving cycles simulating significant road slopes and sinuosity should be used in the future, both for consumption and emissions certification and in the development of new powertrains.

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Comparison of Three Methods for Constructing Real Driving Cycles

Energies ◽

10.3390/en12040665 ◽

2019 ◽

Vol 12 (4) ◽

pp. 665 ◽

Cited By ~ 5

Author(s):

José Huertas ◽

Luis Quirama ◽

Michael Giraldo ◽

Jenny Díaz

Keyword(s):

Fuel Consumption ◽

Normal Operation ◽

Mcmc Methods ◽

Characteristic Parameters ◽

The Real ◽

Stochastic Nature ◽

Driving Patterns ◽

Driving Cycles ◽

Relative Differences ◽

Fuel Consumption And Emissions

This work compares the Micro-trips (MT), Markov chains–Monte Carlo (MCMC) and Fuel-based (FB) methods in their ability of constructing driving cycles (DC) that: (i) describe the real driving patterns of a given region and (ii) reproduce the real fuel consumption and emissions exhibited by the vehicles in that region. To that end, we selected four regions and monitored simultaneously the speed, fuel consumption and emissions of CO2, CO and NOx from a fleet of 15 buses of the same technology during eight months of normal operation. The driving patterns exhibited by drivers in each region were described in terms of 23 characteristic parameters (CPs) such as average speed and average positive kinetic energy. Then, for each region, we constructed their DC using the MT method and evaluated how close it describes the observed driving pattern in each region. We repeated the process using the MCMC and FB methods. Given the stochastic nature of MT and MCMC methods, the DCs obtained changed every time the methods were applied. Hence, we repeated the process of constructing the DCs up to 1000 times and reported their average relative differences and dispersion. We observed that the FB method exhibited the best performance producing DCs that describe the observed driving patterns. In all the regions considered in this study, the DCs produced by this method showed average relative differences smaller than 20% for all the CPs considered. A similar performance was observed for the case of fuel consumption and emission of pollutants.

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