On-board Diagnostic System Research Articles

Accessing a vehicle’s environmental indicators during technical inspection

Vehicles are one of the biggest sources of environmental pollution in major cities, especially air pollution. Since 2018 Ministry of Natural Resources and Environment of the Russian Federation has proposed a new method of calculating harmful vehicle emissions based on emissions per mileage of certain vehicle types and amount of car fuel sold. It is important to note that this method does not account for faulty vehicles which produce a lot more air pollution than fully operable cars. Controlling and decreasing car emissions have become especially noteworthy since the global trend for lowering carbon footprint, produced by human activity. Authors of this paper show how environmental indicators could be controlled using on-board diagnostic systems during technical inspection and surveying faulty cars (as well as fully operable) for harmful emissions. An on-board diagnostic system (self-diagnosis) is used for finding and identifying faults in the electronic engine management system and other vehicle’s tools and systems which could lead to an increase in optimal toxicity of exhaust gases. This paper contains an analysis of requirements for a vehicle’s on-board diagnostic systems put forward by the Russian legislations, as well as additional requirements for vehicles during technical inspections.

Experimental evaluation of gear-shift and internal-combustion engine variables on fuel consumption, noise and pollutant emissions

Although variability of noise and pollutant emissions are usually associated with vehicular speed and acceleration, driving style, mainly related to gear-shift and internal engine variables such as revolutions per minute (RPM) or engine load (EL), can also play a key role. Moreover, the contribution of each variable for fuel consumption, noise and pollutant emissions can vary for different vehicle-motorization types. However, the effect of such internal variables on noise and pollutant emissions is not fully exploited in the literature. Thus, this work aims to assess the impact of the gear selection, RPM, and EL on fuel consumption, and carbon dioxide (CO2), nitrogen oxides (NOx), and noise in terms of sound power level (Lw) emissions for a diesel passenger vehicle. This is focused on a speed and gear-based controlled on-road environment. Internal observable (fuel consumption, RPM, and EL) and kinematic (speed and acceleration) variables were recorded on a second-by-second time basis using an On-Board Diagnostic System, and noise data were recorded with a Sound Level Meter. Pollutant emissions were estimated using the Vehicle Specific Power (VSP) methodology with a 1Hz frequency. In this study, Clustering and Disjoint Principal Component Analysis is applied to find patterns hidden in data. An Ordered Logit model to predict the gear based on exploring kinetic and internal engine variables, that are influenced by driver’s driving style, is developed. Preliminary results highlight the potential of the developed model and show the potential influence of gear selection for minimizing fuel consumption, noise and pollutant emissions. These findings establish a foundation for developing a sustainability gear-shift indicator not only focused on minimizing fuel consumption, but also noise and pollutant emissions. These are relevant to understand vehicle performance and alleviate the relative impacts of the driving style if integrated into vehicle engine control units.

Data-Driven Models Support a Vision for Over-the-Air Vehicle Emission Inspections

Emissions inspection and maintenance (I/M) programs for light-duty motor vehicles manage ambient air quality by enforcing emissions standards and requiring non-compliant vehicles to be repaired or retired. I/M programs in the United States typically identify over-emitters through on-board diagnostics (OBD) systems and vehicles’ proprietary firmware (i.e., indirect tests), rather than through physical measurements of exhaust gases (i.e., tailpipe tests). Analyzing data from Colorado’s I/M program, this study finds the OBD test to have an accuracy of 87%, but a false pass rate of 50%, when predicting the result of a corresponding tailpipe test. As an alternative, transparent data-driven models—using logistic regression and gradient boosting machines—to consistently identify over-emitting vehicles are proposed. These models were up to 24% more accurate, or 85% more sensitive than the current OBD test in a stratified data sample. A key benefit of transparent statistical models—jurisdictions’ ability to tune the test methods to best suit program needs—is also assessed. Finally, this study shows how these results support a vision for cloud-based, selective I/M programs where statistical models are applied to OBD data—collected over-the-air from vehicles—to identify and require additional inspection for only the most probable over-emitters.

Long-Term In-Use NOx Emissions from London Buses with Retrofitted NOx Aftertreatment.

Buses constitute a significant source of air pollutant emissions in cities. In this study, we present real-world NOx emissions from 97 diesel-hybrid buses measured using on-board diagnostic systems over 44 months and 6.35 million km in London. Each bus had previously been retrofitted with a selective catalytic reduction (SCR) aftertreatment system to reduce emissions of nitrogen oxides (NOx). On average, parallel hybrid (PH) and series hybrid (SH) buses emitted 3.80 g of NOx/km [standard deviation (SD) of 1.02] and 2.37 g of NOx/km (SD of 0.51), respectively. The SCR systems reduced engine-out emissions by 79.8% (SD of 5.0) and 87.2% (SD of 2.9) for the PHs and SHs, respectively. Lower ambient temperatures (0-10 °C) increased NOx emissions of the PHs by 24.2% but decreased NOx emissions of the SHs by 27.9% compared to values found at more moderate temperatures (10-20 °C). To improve emissions inventories, we provide new distance-based NOx emissions factors for different ranges of ambient temperature. During the COVID-19 pandemic, the emissions benefits of reduced congestion were largely offset by more frequent route layovers leading to lower SCR temperatures and effectiveness. This study shows that continuous in-service measurements enable quantification of real-world vehicle emissions over a wide range of operations that complements conventional testing approaches.

Evaluation of heavy-duty vehicle emission controls with a decade of California real-world observations

ABSTRACT Over the past decade, efforts to reduce emissions of particulate matter (PM) and oxides of nitrogen (NO + NO2, or NO x ) from heavy-duty diesel vehicles (HDDVs) have led to the widespread adoption of both Diesel Particulate Filters (DPFs) to control PM and Selective Catalytic Reduction (SCR) to control NO x . We evaluated the performance of DPFs and SCR with 13,327 real-world fuel-based Black Carbon (BC) and NO x emission factors from 9,167 unique heavy-duty vehicles (primarily HDDVs) measured at four sites in California (two ports, two highways) from 2011 to 2018. BC emission factors have decreased by 90% during the past decade. At the same time, BC distributions have become increasingly skewed toward “high-emitters” – e.g., the portion of the HDDV fleet responsible for half of all BC emissions has decreased from ~16% to ~3%. NO x emission factors have also decreased over the past decade, but by only 31%. They remain roughly five times greater than in-use thresholds.We examined changes in BC and NO x emissions with engine age. BC emissions from DPF-only trucks decreased slightly but insignificantly, by 6 ± 15 mg/kg fuel per year, while for DPF+SCR trucks they increased by 5 ± 3. These changes are less than 5% of in-use thresholds. The annual increase in NO x emissions with age was much greater: 1.44 ± 0.28 g/kg for older SCR trucks without on-board diagnostic (OBD) capabilities and 0.48 ± 0.35 for newer trucks with OBD, roughly 20– 50% of in-use thresholds. Paired t-tests on the over 600 vehicles that were observed in multiple campaigns were consistent with these results. Observed changes in BC emissions with age were best fit with a “gross emitter” model assuming an annual DPF failure rate of 0.83 ± 0.01% for DPF-only trucks and 0.56 ± 0.01% for DPF+SCR trucks.Implications: These observations of real-world HDV emission factors have several major implications for regulatory efforts to reduce them. The increasing importance of a relatively small number of high BC emitters suggests that widespread sampling of the on-road fleet will be necessary to identify these vehicles. On the other hand, the much more ubiquitous deterioration in NO x control measures may be better addressed by incorporating on-board diagnostic systems, with telematic data transfer when possible, into inspection and maintenance programs. These NO x observations also highlight the need for strengthening heavy-duty SCR durability demonstration requirements.

A study on vehicle Noise Emission Modelling: Correlation with air pollutant emissions, impact of kinematic variables and critical hotspots

This work proposes a methodology suitable for analysing the sound power levels (Lw), and carbon dioxide (CO2) and nitrogen oxides (NOx) emissions along a travel, and consequentially assessing the related critical hotspots. The estimation of noise and pollutant emissions from six vehicles driven along three different routes (one National Road and two highways) was conducted, in combined way, through seven Noise Emissions Models (NEMs) and Vehicle Specific Power (VSP) methodology, respectively. The inputs required by the models (namely, vehicle speed and acceleration and road grade) were extrapolated from On-Board Diagnostic (OBD) system and Global Positioning System (GPS) data recorded during monitoring campaigns. The specificities of each model were analysed, and the role played by the kinematic variables in noise and exhaust emissions assessment was highlighted. Results show that all the tested NEMs estimated higher noise levels on the highways, while VSP predicted higher emissions on the National Road. This happens because speed is the main input variable in NEMs, while acceleration has an impact on noise estimation in the low-speed range (below 50 km/h). For pollutant emissions evaluation, acceleration plays a fundamental role also at high-speed range, where a transition from a cruising condition to an acceleration phase leads to significant variations in terms of VSP values. Lw values, estimated with NEMs that use acceleration correction terms, present positive moderate-to-high correlation with VSP ones. Moreover, the models that neglect acceleration in noise estimation fail to recognize traffic control treatments as critical hotspots.

Sensitivity of light duty vehicle tailpipe emission rates from simplified portable emission measurement systems to variation in engine volumetric efficiency

ABSTRACT Light-duty gasoline vehicle (LDGV) tailpipe emission rates can be quantified based on pollutant concentrations measured using portable emission measurement systems (PEMS). Emission rates depend on exhaust flow. For simplified and micro-PEMS, exhaust flow is inferred from engine mass air flow (MAF) and air-to-fuel ratio. For many LDGVs, MAF is broadcast via the on-board diagnostic (OBD) interface. For some vehicles, only indirect indicators of MAF are broadcast. In such cases, MAF can be estimated using the speed-density method (SDM). The SDM requires an estimate of the engine volumetric efficiency (VE), which is the ratio of actual to theoretical MAF. VE is affected by intra-vehicle variability in the engine load and inter-vehicle variability in engine characteristics (e.g., the type of valvetrain). The suitability of SDM-based estimates of MAF in conjunction with simplified and micro-PEMS has not been adequately evaluated. Therefore, the objectives are to: (1) quantify VE accounting for intra- and inter-vehicle variability; and (2) evaluate the accuracy of SDM-based vehicle emission rate estimation approaches. Seventy-seven naturally-aspirated LDGVs were measured using PEMS. For each vehicle, VE was estimated using three approaches: (1) constant VE calibrated to actual fuel use; (2) average estimates of VE for Vehicle Specific Power modes imputed from OBD data; and (3) modeled VE using multilinear regression (MLR). The MLR models were developed based on engine load and engine characteristics. The best model was selected based on various statistical diagnostics. When engines were under load, variability in VE was most sensitive to variations in engine load. During idling, VE differed between engines depending on engine characteristics. The constant and modeled VE estimation approaches enable the accurate estimation of microscale and mesoscale emission rates, with errors typically within ±10% compared to values imputed from OBD data. Thus, accurate emission rates can be obtained from simplified and micro-PEMS. Implications: Simplified and micro portable emission measurement systems (PEMS) enable widespread measurement of vehicle exhaust emission. As a cost saving measure, they estimate exhaust flow indirectly rather than via measurement, typically based on engine mass air flow (MAF). For some vehicles, MAF is not reported by the on-board diagnostic (OBD) system but can be inferred from other reported variables and volumetric efficiency (VE). However, VE is typically proprietary. Methods demonstrated here for estimating VE enable accurate quantification of emission rates, thereby enabling use of these PEMS for policy-relevant applications such as technology assessments, trends analysis, and emissions inventories.

On the Expansion of On-Board Diagnostics (OBD) to Electric Propulsion Systems in Battery Electric Vehicles

<div class="section abstract"><div class="htmlview paragraph">Currently the On-Board Diagnostics (OBD) requirements enforced by government agencies do not cover electric vehicles. Although the California Air Resources Board (CARB) mandates all light and medium duty vehicles and heavy duty engine dynamometer certified engines equipped with fossil fuel-powered engines, including all hybrid vehicles, must follow the OBD requirements in California Code of Regulation (CCR) 1968.2 and 1971.1, Battery Electric vehicles (BEVs), are exempted from OBD requirements. The legislators, such as CARB, have started to make proposals for on-board systems to monitor electric propulsion system health. In addition, there may be customer needs to obtain standard vehicle service information and the Original Equipment Manufacturers (OEMs) may also have the desire for common diagnostic strategies across different vehicle applications to lower the development costs. This paper, different from other published technical papers or proposals from OEMs or government agencies, analyzes the feasibility and compatibility of the existing OBD system design requirements, and aligns them with the need of industrial-common OBD system architecture and existing/upcoming SAE standards to propose the monitoring and reporting requirements on the identified primary propulsion system components/systems from technical point of view. This study makes some key technical proposals/solutions for the possible use of OBD-like monitoring and reporting requirements for Electric Propulsion Systems, including, what are the monitoring requirements, what is to be monitored, what are the malfunction indicator light (MIL) illumination requirements, and what are the generic scan tool (GST) interface requirements that are based on J1979-2 (i.e., Unified Diagnostic Services -UDS) to facilitate the repair service by technicians, including from independent service shops.</div></div>

Development of on-board Diagnostic Systems for Vehicles to Improve their Reliability

The article is dedicated to various questions of control and diagnostics of automobile technical conditions by applying on-board diagnostic system. The structure of automated diagnostic system was analyzed. Various types of sensors, signals and required types of their transfigurations were enlisted . The relationship between the structural and diagnostic parameters of the car is given, where all systems and car units are divided into groups. The first group consists of systems, units and machine parts that provide traffic safety, the second group contains the remaining functional systems. A structural and investigation scheme for diagnosing automobile’s brake gear with an air pressure brake system was developed.

A Novel Driving Behavior Learning and Visualization Method With Natural Gaze Prediction

Driving behavior analysis is vital for the advanced driving assistance system, aiming to improve driving behavior and decrease traffic accidents. Most existing driving behavior learning methods focus on either vehicle sensor information or driver's attention information, and provide a classification result on the current time data samples. The visualization of driving behavior on time series data samples could give an understanding and review of the driver's continuous actions. However, there has been little progress in combining the multi-modal vehicle and driver information on driving behavior learning and visualization. A multi-information driving behavior learning and visualization method with natural gaze prediction is proposed in this paper, which automatically integrates driver's gaze direction estimated from face camera, and various vehicle sensor data collected from on-board diagnostics (OBD) system. To accurately estimate the eye gaze under large head movement, a novel head pose-free eye gaze prediction method without calibration is proposed based on global and local scale sparse encoding, which treats the direction mapping as small gaze region classification. To understand driving behavior more intuitively, the latent features that represent different driving behaviors are extracted by FastICA from the fused time series data, and mapped into RGB color space for distinguished visualization. Experimental results demonstrate the effectiveness of the proposed method, and show that the proposed method performs better than the compared methods.

Research on low-emission vehicle powered by LPG using innovative hardware and software

LPG is a cheap and ecological fuel for spark ignition engines. The sequential gas injection system can be installed at the factory and is then the Original Equipment of the Manufacturer. A vehicle with a spark ignition engine can also be converted to gas in an authorized workshop. In both cases, the vehicle must meet the same exhaust emission standards when running on alternative fuel as it does with the original fuel. Conversion of vehicles to LPG and CNG is regulated by law at the European Union level. The article describes the conversion of a low-emission gasoline vehicle that meets the Euro 6 emission standard to LPG. The configuration and calibration of the LPG system is described in detail. The compatibility of the gas system with the vehicle's on-board diagnostic system was then checked. Finally, road tests of the vehicle were carried out to compare the performance with the original fuel and the alternative fuel.

The role of real power output from farm tractor engines in determining their environmental performance in actual operating conditions

The paper discusses the determination of engine torque to be used to calculate work performed by a non-road vehicle when determining specific exhaust emissions. During investigations related to the environmental indexes under actual operating conditions, the value of torque is pulled from the vehicle OBD system. It is estimated based on the pressure and duration of the fuel injection. The work of an engine calculated in this way is overrated. It allows for the engine internal losses such as aerodynamic resistance and internal friction losses. The paper presents tests on a farm tractor, in which a portable chassis dynamometer and a portable exhaust emissions measurement and data recording systems were applied. In the performed tests, the engine operated at steady work points: defined engine speeds and loads. Based on the Willans method, the data from the vehicle on-board diagnostic system and the calculated values of the fuel consumption a correction curve was determined. It should be used when correcting the engine torque during measurements under actual operating conditions. Based on the recorded concentrations of the emission components, specific emission was calculated allowing for the corrective calculation. This was used to perform a comparative analysis with the emission limits specified by the Stage IIIB standard that the test object was compliant with. The presented research methodology may be successfully applied in testing other non-road machinery whose environmental indexes are defined based on specific emissions. In research of machines in actual condition, necessary is to determine the engine internal resistance and take it into account when determining the engine torque in the field tests. This will allow a determination of the real environmental indexes.

Development of Kuala Lumpur driving cycle for the estimation of fuel consumption and vehicular emission

Driving cycles is a series of data points representing the speed of vehicle verses time sequence profile developed for certain road, route, specific area or city. Generally, driving cycle is used to estimate the fuel consumption (FC) and vehicular exhaust emission. The common factors that affected the FC and exhaust emission are the engine operating conditions such as cold start, low engine loads and high engine loads which are found during actual driving. The research presents the measurement and analysis of engine conditions based on actual driving behaviours in an attempt to formulate representative driving cycle for Kuala Lumpur. The methodology used for this research is adapted from the Worldwide Harmonized Light-duty Test Cycle (WLTC) introduced by the working party on pollution and energy (GRPE) under the United Nations Economic Commission for Europe (UNECE). This research covered a wide area of paved road in Kuala Lumpur, about 62.34% by surveying the road condition on the eight selected routes. The used of On-board Diagnostic (OBD) and Global Positioning System (GPS) equipment are used to record the vehicle travel speed during data collection activity. The driving characteristics are then analysed from speed profile and its target statistic parameters are defined to develop representative driving cycle. The characteristics of the newly developed driving cycle are compared to the European driving cycle which is currently being used in Malaysia for new vehicle certification and registration. Therefore, the correlation of FC is derived to indicate the impacts of real-world driving conditions relative to domestic conditions.

Vehicle Fuel Consumption Prediction Method Based on Driving Behavior Data Collected from Smartphones

Transportation is an important factor that affects energy consumption, and driving behavior is one of the main factors affecting vehicle fuel consumption. The purpose of this paper is to improve fuel consumption monitoring databases based on mobile phone data. Based on the mobile phone terminals and on-board diagnostic system (OBD) installed in taxis, driving behavior data and fuel consumption data are extracted, respectively. By matching the driving behavior data collected by a mobile phone with the fuel consumption data collected by OBD, the correlation between driving behavior and fuel consumption is explored, so that vehicle fuel consumption could be predicted based on mobile phone data. The fuel consumption prediction models are built using back propagation (BP) neural network, support vector regression (SVR), and random forests. The results show that the average speed, average speed except for idle (ASEI), average acceleration, average deceleration, acceleration time percentage, deceleration time percentage, and cruising time percentage are important indicators for fuel consumption evaluation. All three models could predict fuel consumption accurately, with an absolute relative error less than 10%. The random forest model is proved to have the highest accuracy and runs faster, making it suitable for wide application. This method lays a foundation for monitoring database improvement and fine management of urban transportation fuel consumption.

Research on Production Vehicle Evaluation Method of China VI OBD for Light-Duty Vehicles

With the gradual tightening of light-duty vehicle emission regulations, the fault diagnosis function of the on-board diagnostic (OBD) system is constantly improving and the monitoring items are also increasing. The production vehicle evaluation (PVE) test problem of latest OBD certification in the China VI emission regulations for light vehicles is studied. Firstly, the working principle and development process of OBD system is introduced. Secondly, the PVE testing regulations and testing process of OBD system is analyzed. It covers standardized verification (J1), monitoring requirement verification (J2) and in-use monitoring performance verification (J3). Finally, in order to improve the efficiency of PVE testing for such a large number of faults, a faults classification method based on fault type and function is proposed. OBD faults can be simplified into the six categories, which include circuit fault, tool performance fault, hardware performance fault, bus communication fault and exemption test fault. The simulation method of corresponding fault is introduced and analyzed as well.

How much information is lost when sampling driving behavior data? Indicators to quantify the extent of information loss

Purpose Individuals’ driving behavior data are becoming available widely through Global Positioning System devices and on-board diagnostic systems. The incoming data can be sampled at rates ranging from one Hertz (or even lower) to hundreds of Hertz. Failing to capture substantial changes in vehicle movements over time by “undersampling” can cause loss of information and misinterpretations of the data, but “oversampling” can waste storage and processing resources. The purpose of this study is to empirically explore how micro-driving decisions to maintain speed, accelerate or decelerate, can be best captured, without substantial loss of information. Design/methodology/approach This study creates a set of indicators to quantify the magnitude of information loss (MIL). Each indicator is calculated as a percentage to index the extent of information loss (EIL) in different situations. An overall information loss index named EIL is created to combine the MIL indicators. Data from a driving simulator study collected at 20 Hertz are analyzed (N = 718,481 data points from 35,924 s of driving tests). The study quantifies the relationship between information loss indicators and sampling rates. Findings The results show that marginally more information is lost as data are sampled down from 20 to 0.5 Hz, but the relationship is not linear. With four indicators of MILs, the overall EIL is 3.85 per cent for 1-Hz sampling rate driving behavior data. If sampling rates are higher than 2 Hz, all MILs are under 5 per cent for importation loss. Originality/value This study contributes by developing a framework for quantifying the relationship between sampling rates, and information loss and depending on the objective of their study, researchers can choose the appropriate sampling rate necessary to get the right amount of accuracy.

Real-world fuel consumption of light-duty passenger vehicles using on-board diagnostic (OBD) systems

An increasing discrepancy between real-world and type-approval fuel consumption for light-duty passenger vehicles (LDPVs) has been reported by several studies. Normally, real-world fuel consumption is measured primarily by a portable emission measurement system. The on-board diagnostic (OBD) approach, which is flexible and offers high-resolution data collection, is a promising fuel consumption monitoring method. Three LDPVs were tested with a laboratory dynamometer based on a type-approval cycle, the New European Driving Cycle (NEDC). Fuel consumption was measured by the OBD and constant-volume sampling system (CVS, a regulatory method) to verify the accuracy of the OBD values. The results of the OBD method and the regulatory carbon balance method exhibited a strong linear correlation (e.g., R2 = 0.906–0.977). Compared with the carbon balance results, the fuel consumption results using the OBD were 7.1%±4.3% lower on average. Furthermore, the real-world fuel consumption of six LDPVs was tested in Beijing using the OBD. The results showed that the normalized NEDC real-world fuel consumption of the tested vehicles was 13%±17% higher than the type-approval-based fuel consumption. Because the OBD values are lower than the actual fuel consumption, using a carbon balance method may result in a larger discrepancy between real-word and type-approval fuel consumption. By means of the operating mode binning and micro trip methods, a strong relationship (R2 = 0.984) was established between the average speed and relative fuel consumption. For congested roads (average vehicle speed less than 5 km/h), the fuel consumption of LDPVs is highly sensitive to changes in average speed.

Real-world fuel efficiency and emissions from an urban diesel bus engine under transient operating conditions

Until recently, heavy-duty vehicle (HDV) engines were subjected to pollutant emissions type-approval tests, which were developed on engine test benches under standardised driving cycles. However, these results do not reflect engine behaviour under real-world driving conditions. In this context, experimental measurements were conducted on a diesel Euro V bus under urban off-cycle conditions in Madrid (Spain). The main objective was to create efficiency and emissions engine maps by combining transient-state engine data obtained directly from portable emission measurement system (PEMS) and on-board diagnostic (OBD) data acquisition systems. The engine maps were used to evaluate engine performance in real-world conditions, and to create a model that allows for second-by-second prediction of the engine fuel consumption and emissions. Owing to the variability of transient-state engine data, this work proposed an engine map development method, consisting of grouping the measured data into grids by engine speed and torque ranges, and then averaging them to obtain a single value per grid.The results showed that the Euro V diesel bus engine operates in urban off-cycle conditions with a brake thermal efficiency (BTE) of 41%, a brake-specific fuel consumption (BSFC) of 205 g/kWh, and a carbon dioxide (CO2) energy-emission factor of 637 g/kWh. The NOx energy-emission factor was 80% higher than the levels in the Euro V Standard.This work demonstrated that the grid engine maps model has the potential to predict second-by-second fuel consumption and CO2 emissions, with a relative total error of less than 5%. Therefore, this approach could be useful for accurately simulating engine or vehicle performance for any operation scenarios at a microscopic level, provided engine torque and engine speed data from the bus are available.

Newer Generation of Scooters: Polychlorinated Dibenzo-p-dioxin and Dibenzofuran and Polychlorinated Biphenyl Reductions

ABSTRACT Scooter emissions have attracted attention in recent years because of human exposure to their direct effects in urban areas. Trace toxics, such as polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) and polychlorinated biphenyls (PCBs) have thus become important in scooter emissions. In this work, ten Tier 5 and 6 scooters were tested using a 100-second model to analyze their PCDD/F and PCB emissions and compare the results with previous Tier 3 studies. Tier 5 and 6 scooters emitted 1.86–2.91 and 0.133–0.298 pg WHO-TEQ Nm-3 of PCDD/Fs and PCBs, respectively. It was interesting to find that the PCDD/Fs were reduced by 94.6–97.4% and 99.4–99.6% in Tier 5 and 6 motors, respectively. The congener profiles of PCDD/Fs were affected by improving the emission control. The domination of highly chlorinated congeners shown in Tier 3 was reduced in Tier 5 with increases in low chlorinated PCDFs. This showed that de novo synthesis occurred and could be inhibited by the OBD system in Tier 6. The tailpipe renews reduced 60.0–93.8% of PCDD/Fs and 85.3–97.7% of PCB emissions, but several cases would still exhibit a delay for stable operation of a catalytic converter. The annual emissions of PCDD/F TEQ was calculated based on the statistics in 2019 and tested as 1.63 g WHO-TEQ. It could be 99.7% reduced to 3.55 mg by replacing all scooters with Tier 6. Consequently, the improvement of electronic fuel injection and on-board diagnostics systems from a carburetor without feedback control not only reduced the regulated pollutants but effectively reduced PCDD/F emissions.

Цифровизация ремонтного производства тягового подвижного состава

The article considers the approach to the mastering of modern digital technologies in the locomotive complex with regard to the tasks of increasing the controllability and, as a result, the efficiency of technological processes of maintenance and repair of traction rolling stock. The organizational, technical and technological solutions implemented in the first digital depot, created on the basis of the locomotive depot Vikhorevka of the East Siberian Traction Directorate of JSC «Russian Railways» (service locomotive depot Bratskoe of OOO «LokoTeh-Servis») are presented. Joint application of the complex of implemented solutions ensures implementation of the new technology of organization of locomotives maintenance and repair, feasibility of reaching the targets for readiness of the traction rolling stock for operation. Achieving efficiency of the technology built within the framework of the Digital Depot project is ensured by the universality of the repair position - all technological operations on locomotives maintenance and repair are performed at the digital universal repair position, including supercycle unscheduled works, are carried out in a single place without additional maneuvering. The universality of repair positions together with application of radio-controlled self-propelled traction unit with hybrid power plant increases the depot energy efficiency, reduces the expenses for maintenance of shunting locomotives stock and their total number, as well as allows to increase rhythmicity of locomotive repair. Implementation of transparent comprehensive information exchange between the process equipment of repair position, the systems for collecting, processing, analyzing data and information obtained in real time from on-board locomotive systems, monitoring and diagnostics systems applied during locomotive acceptance for repair, input and operation control, as well as at the stage of output control, provides the efficiency of control actions, objectivity of personnel performance discipline control and compliance with technical processes, increases the practical value of diagnostic data, which form the basis of qualitative repair and reliability of locomotives in operation.