On-board Diagnostics Research Articles

Modeling and Predicting Heavy-Duty Vehicle Engine-Out and Tailpipe Nitrogen Oxide (NO x ) Emissions Using Deep Learning.

As emissions regulations for transportation become stricter, it is increasingly important to develop accurate nitrogen oxide (NOx) emissions models for heavy-duty vehicles. However, estimation of transient NOx emissions using physics-based models is challenging due to its highly dynamic nature, which arises from the complex interactions between power demand, engine operation, and exhaust aftertreatment efficiency. As an alternative to physics-based models, a multi-dimensional data-driven approach is proposed as a framework to estimate NOx emissions across an extensive set of representative engine and exhaust aftertreatment system operating conditions. This paper employs Deep Neural Networks (DNN) to develop two models, an engine-out NOx and a tailpipe NOx model, to predict heavy-duty vehicle NOx emissions. The DNN models were developed using variables that are available from On-board Diagnostics from two datasets, an engine dynamometer and a chassis dynamometer dataset. Results from trained DNN models using the engine dynamometer dataset showed that the proposed approach can predict NOx emissions with high accuracy, where R2 scores are higher than 0.99 for both engine-out and tailpipe NOx models on cold/hot Federal Test Procedure (FTP) and Ramped Mode Cycle (RMC) data. Similarly, the engine-out and tailpipe NOx models using the chassis dynamometer dataset achieved R2 scores of 0.97 and 0.93, respectively. All models developed in this study have a mean absolute error percentage of approximately 1% relative to maximum NOx in the datasets, which is comparable to that of physical NOx emissions measurement analyzers. The input feature importance studies conducted in this work indicate that high accuracy DNN models (R2 = 0.92–0.95) could be developed by utilizing minimal significant engine and aftertreatment inputs. This study also demonstrates that DNN NOx emissions models can be very effective tools for fault detection in Selective Catalytic Reduction (SCR) systems.

RideVR: Reducing Sickness for In-Car Virtual Reality by Mixed-in Presentation of Motion Flow Information

Humans spend a significant portion of their daily life in cars. In this study, we investigate a method to reduce motion sickness and allow people to use virtual reality (VR) while riding in cars. As the sickness arises primarily from the sensory conflict between visual and actual (or vestibular) motions, the proposed approach attempts to resolve the mismatch by mixing in and visualizing the estimated information of the actual motion, which is sensed by the on-board diagnostics and inertial measurement unit modules attached to the vehicle. We conduct a pilot experiment to validate our approach by comparing the sickness levels before and after implementing the approach in three in-car VR usage conditions: (1) Default – using the VR content without modification; (2) transparent wall – using the VR content with its background scene changing depending on the car motion; (3) particle flow – mixing in the VR content with the estimated motion flow of the car visualized as moving particles. Our experimental results show that motion sickness is reduced significantly (but not eliminated to a negligible level) using our approach.

Time-frequency analysis application to the evaluation of instantaneous combustion noise

A novel approach to combustion noise has been proposed in the present paper with the time-frequency analysis technique. By evaluating the energy content of the combustion pressure signal, which has been measured as a function of time on Euro 5 and Euro 6 diesel engines, different combustion phases have been identified and the combustion noise due to the difference between in-cylinder pressure and motored trace has been evaluated for each phase. The algorithm for the calculus of this instantaneous combustion noise contribution versus time has been realized through a home-made tool and has been applied to single and double injection schedules.The time-frequency analysis allows more detailed information to be obtained on the contribution of the different combustion phases to the combustion noise and the cause-and-effect relationships between injection schedule and combustion noise to be better highlighted. The research investigation provides a hint for onboard diagnostics and real-time control of combustion noise, but the developed tool also can be provided as a practical validation tool for the refinement of combustion models.

A Deep Transfer NOx Emission Inversion Model of Diesel Vehicles with Multisource External Influence

By installing on-board diagnostics (OBD) on tested vehicles, the after-treatment exhaust emissions can be monitored in real time to construct driving cycle-based emission models, which can provide data support for the construction of dynamic emission inventories of mobile source emission. However, in actual vehicle emission detection systems, due to the equipment installation costs and differences in vehicle driving conditions, engine operating conditions, and driving behavior patterns, it is impossible to ensure that the emission monitoring data of different vehicles always follow the same distribution. The traditional machine learning emission model usually assumes that the training set and test set of emission test data are derived from the same data distribution, and a unified emission model is used for estimation of different types of vehicles, ignoring the difference in monitoring data distribution. In this study, we attempt to build a diesel vehicle NOx emission prediction model based on the deep transfer learning framework with a few emission monitoring data. The proposed model firstly uses Spearman correlation analysis and Lasso feature selection to accomplish the selection of factors with high correlation with NOx emission from multiple sources of external factors. Then, the stacked sparse AutoEncoder is used to map different vehicle working condition emission data into the same feature space, and then, the distribution alignment of different vehicle working condition emission data features is achieved by minimizing maximum mean discrepancy (MMD) in the feature space. Finally, we validated the proposed method with the diesel vehicle OBD data that were collected by the Hefei Environmental Protection Bureau. The comprehensive experiment results show that our method can achieve the feature distribution alignment of emission data under different vehicle working conditions and improve the prediction performance of the NOx inversion model given a little amount of NOx emission monitoring data.

An efficient automatic accident detection system using inertial measurement through machine learning techniques for powered two wheelers

The two-wheeler accidents in most populated and developing countries have become vulnerable and six accidents happen every hour on average. This paper proposes an efficient automatic accident detection system that attempts to detect the occurrences of the accidents in powered two-wheelers (PTW) automatically using vehicle-dependent parameters and the physiological parameters of the rider in real-time. The proposed system builds an accident detection system in PTW using three steps namely, critical event detection system, accident detection system, and severity assessment system. The critical event detection system reads the accelerometer sensor values from the On-Board Diagnostic (OBD) unit mounted on the PTW and classifies the state of the vehicle as normal, fall-like, and fall through the enhanced decision tree algorithm. The enhanced decision tree algorithm uses a tanh function to calculate entropy values. The rules are extracted to fix the threshold by pruning the decision tree to identify the fall of the vehicle and the rider. Due to the unstable nature of PTW and the rider, a novel Adaptive Sequence Window algorithm (ASW) is proposed to substantiate and validate the occurrence of accidents based on the sequence of states identified. Once the accident is detected, the Decision Support System (DSS) running on the OBD mounted on the PTW decides the severity of the accident by combining the three parameters namely fall of the vehicle, fall of the rider, and pulse rate of the rider using the first-order predicate logic rules. The enhanced decision tree algorithm outperforms the other classifiers such as naïve Bayes, artificial neural network, and recurrent neural network with an accuracy of 99.8%. The OBD unit mounted on the PTW and the rider’s helmet is used to detect the occurrence of accidents automatically along with its severity with less time. The ASW algorithm enables the system to detect the fall of the vehicle and rider within five minutes and prevents false positives. Further, the information can be communicated based on the severity of the accident to the emergency medical service for quick response.

Privacy-Preserving Tampering Detection in Automotive Systems

Modern auto-vehicles are built upon a vast collection of sensors that provide large amounts of data processed by dozens of Electronic Control Units (ECUs). These, in turn, monitor and control advanced technological systems providing a large palette of features to the vehicle’s end-users (e.g., automated parking, autonomous vehicles). As modern cars become more and more interconnected with external systems (e.g., cloud-based services), enforcing privacy on data originating from vehicle sensors is becoming a challenging research topic. In contrast, deliberate manipulations of vehicle components, known as tampering, require careful (and remote) monitoring of the vehicle via data transmissions and processing. In this context, this paper documents an efficient methodology for data privacy protection, which can be integrated into modern vehicles. The approach leverages the Fast Fourier Transform (FFT) as a core data transformation algorithm, accompanied by filters and additional transformations. The methodology is seconded by a Random Forest-based regression technique enriched with further statistical analysis for tampering detection in the case of anonymized data. Experimental results, conducted on a data set collected from the On-Board Diagnostics (OBD II) port of a 2015 EUR6 Skoda Rapid 1.2 L TSI passenger vehicle, demonstrate that the restored time-domain data preserves the characteristics required by additional processing algorithms (e.g., tampering detection), showing at the same time an adjustable level of privacy. Moreover, tampering detection is shown to be 100% effective in certain scenarios, even in the context of anonymized data.

Real Driving Cycle-Based State of Charge Prediction for EV Batteries Using Deep Learning Methods

An accurate prediction of the State of Charge (SOC) of an Electric Vehicle (EV) battery is important when determining the driving range of an EV. However, the majority of the studies in this field have either been focused on the standard driving cycle (SDC) or the internal parameters of the battery itself to predict the SOC results. Due to the significant difference between the real driving cycle (RDC) and SDC, a proper method of predicting the SOC results with RDCs is required. In this paper, RDCs and deep learning methods are used to accurately estimate the SOC of an EV battery. RDC data for an actual driving route have been directly collected by an On-Board Diagnostics (OBD)-II dongle connected to the author’s vehicle. The Global Positioning System (GPS) data of the traffic lights en route are used to segment each instance of the driving cycles where the Dynamic Time Warping (DTW) algorithm is adopted, to obtain the most similar patterns among the driving cycles. Finally, the acceleration values are predicted from deep learning models, and the SOC trajectory for the next trip will be obtained by a Functional Mock-Up Interface (FMI)-based EV simulation environment where the predicted accelerations are fed into the simulation model by each time step. As a result of the experiments, it was confirmed that the Temporal Attention Long–Short-Term Memory (TA-LSTM) model predicts the SOC more accurately than others.

Regulating light-duty vehicle emissions: an overview of US, EU, China and Brazil programs and its effect on air quality

This paper reviews the progress and effectiveness of Programs to Control Vehicle Emissions (PCVEs), comparing the experiences in the United States (US), European Union (EU), China, and Brazil. We present a timeline comparison of updates and differences in standards for light-duty vehicle (LDV) compliance. We then review the benefits of controlling LDV emissions on air quality, derived from previous relevant studies. Emission standards have been increasingly restricted in all evaluated PCVEs. However, some technical aspects such as dynamometer test cycles, re-testing structure of environmental protection agency, homogeneity of new and in-use vehicles inspection and maintenance, on-board diagnostics requirements are more consolidated in the US. Previous studies at different scales show the success of PCVEs in reducing vehicle emissions and air pollutant concentrations in the US, EU, China, and Brazil. Despite PCVEs has been achieving relative success, vehicular emissions are still a major threat to air quality around the world, especially in developing countries or ascending economies whose fleet grows dramatically. In places where the air quality standards recommended by the World Health Organization (WHO) are violated, it would be required the implementation of more stringent regulations with a well-designed, and homogeneous compliance policy over regional and national territories. This work contributes to clarifying the current challenges and successful experiences on regulating vehicular emissions worldwide.Graphical abstract Supplementary InformationThe online version contains supplementary material available at 10.1007/s10098-021-02238-1.

Kalman–Hatch dual‐filter integrating global navigation satellite system/inertial navigation system/on‐board diagnostics/altimeter for precise positioning in urban canyons

For improved positioning in urban canyons, this study proposes an efficient dual-filter method integrating multi-constellation global navigation satellite system (GNSS), an inertial navigation system (INS), a barometric altimeter and an on-board diagnostics (OBD) module. The proposed method consists of a position-domain (PD) Hatch filter and velocity Kalman filter. The Hatch filter is operated as the main positioning filter and the Kalman filter is operated as a sub-filter to aid the main filter for the occasional GNSS outages. The Hatch filter combines barometric altimeter measurements with GNSS pseudorange and carrier phase measurements. The Kalman filter integrates the inertial measurement unit (IMU) measurements with GNSS Doppler shift and OBD speed measurements. A semi-simulation method is applied for the accurate performance evaluation of the proposed method compared with the several conventional methods under deficient satellite visibility, multipath errors and cycle slips caused by urban canyons. By the test results, it is demonstrated that the proposed method can bound the positioning errors effectively by utilising low-cost all-weather sensors. The RMSEs of the proposed Kalman–Hatch dual-filter algorithm were shown as 0.05, 0.19 and 0.11 m, respectively, and the maximum errors were shown as 0.16, 0.26 and 0.40 m, respectively, in the east, north and upward directions.

NOx Emission of a Correlation between the PEMS and SEMS over Different Test Modes and Real Driving Emission

The aim of this study is to verify the reliability of NOx emissions measured using Smart Emissions Measurement System (SEMS) equipment in comparison with the NOx emissions measured using certified Portable Emissions Measurement System (PEMS) equipment. The SEMS equipment is simple system, and it is less expensive than the PEMS equipment, as it comprises an On-Board Diagnostics (OBD) signal from the test vehicle and a NOx sensor. The SEMS equipment based on low-cost sensors has an advantage of building big data, but there are insufficient previous studies comparing of NOx emissions with certified the PEMS equipment. Therefore, this study is important in verifying the suitability of the SEMS equipment by comparing the NOx emissions measured by the various test modes and RDE using the two types of equipment. To analyze the correlation between the PEMS and SEMS equipment, the advanced diesel vehicle was equipped with the two types of equipment to simultaneously measure NOx emissions. After installing the equipment on the test vehicle, it was conducted under various test modes in the laboratory and the Real Driving Emission (RDE) test to verify the correlation of NOx emissions measured by the SEMS equipment. The correlation analysis for the NOx emissions measured by the PEMS and SEMS equipment under various test conditions and the RDE test indicated that the slope of the NOx emissions was approximately equal to 1, and the coefficient of determination was 0.9 or higher. Based on these test results, it was concluded that NOx emissions measured by the PEMS and SEMS equipment are highly similar.

Noninvasive Methods for Fault Detection and Isolation in Internal Combustion Engines Based on Chaos Analysis.

The classic monitoring methods for detecting faults in automotive vehicles based on on-board diagnostics (OBD) are insufficient when diagnosing several mechanical failures. Other sensing techniques present drawbacks such as high invasiveness and limited physical range. The present work presents a fully noninvasive system for fault detection and isolation in internal combustion engines through sound signals processing. An acquisition system was developed, whose data are transmitted to a smartphone in which the signal is processed, and the user has access to the information. A study of the chaotic behavior of the vehicle was carried out, and the feasibility of using fractal dimensions as a tool to diagnose engine misfire and problems in the alternator belt was verified. An artificial neural network was used for fault classification using the fractal dimension data extracted from the sound of the engine. For comparison purposes, a strategy based on wavelet multiresolution analysis was also implemented. The proposed solution allows a diagnosis without having any contact with the vehicle, with low computational cost, without the need for installing sensors, and in real time. The system and method were validated through experimental tests, with a success rate of 99% for the faults under consideration.

A Study on Soot and Ash Accumulation Characteristics of Diesel Particulate Filter Substrate Using Nondestructive Computed Radiography X-ray Imaging Technique

The diesel particulate filter of a diesel engine is an after-treatment device that removes particulate matter from exhaust emissions by filtering and oxidating them through a regeneration process. When the diesel particulate filter is damaged, a vehicle inspection is usually performed; if the cause is not found through using on-board diagnostics, then the diesel particulate filter is removed, and a visual test is conducted. However, it is not easy to find the exact cause of the diesel particulate filter being damaged, and a visual test takes a long time as the diesel particulate filter substrate is covered by a canister. In this study, using the computed radiography X-ray imaging technique, X-ray images were taken after placing an accumulated amount of carbon powder, similar to soot and ash powder in the substrate. Results confirmed that carbon powder and ash powder were shown in white in X-ray images, leading to a conclusion that distinguishing between carbon powder and ash powder is possible by analyzing the pixel value through the image processing technique. However, since pixel values alone are insufficient for exact quantitative evaluation, various studies and analyses are necessary for quantitative evaluation.

Development and analysis of electric vehicle driving cycle for hilly urban areas

• Utilized digital surface model in driving cycle creation process. • Developed Islamabad driving cycle with road slope profile. • Powertrain simulation of different vehicular technologies. • Composite analysis of energy consumption and range. • Distance based grid charging required and CO 2 emission factors. In this work, a real-world urban driving cycle with road slope profile is developed for the hilly urban terrain of Islamabad city. A hybrid data collection methodology is adopted which employed global positioning system , on-board diagnostics, and digital surface model. The driving cycle was constructed using the Markov chain Monte Carlo method which considered the weights of different road types in the geographic area under study. The developed driving cycle is compared with different driving cycles using 15 characteristic, 4 distribution parameters, and the speed acceleration probability distribution. Powertrain simulations were performed on 24 vehicle models of different vehicular technologies operating under 8 different driving cycles to determine core factors (i.e.) energy consumption, range, grid charging energy requirement factors, and carbon dioxide emission factors. Results with validation indicate that without road slope profile, errors ranging from 10.2 to 22.2% accumulated in the powertrain simulation besides substantial impacts on considered factors.

CAN-D: A Modular Four-Step Pipeline for Comprehensively Decoding Controller Area Network Data

Controller area networks (CANs) are a broadcast protocol for real-time communication of critical vehicle subsystems. Original equipment manufacturers of passenger vehicles hold secret their mappings of CAN data to vehicle signals, and these definitions vary according to make, model, and year. Without these mappings, the wealth of real-time vehicle information hidden in the CAN packets is uninterpretable, severely impeding vehicle-related research, including CAN cybersecurity and privacy studies, aftermarket tuning, efficiency and performance monitoring, and fault diagnosis to name a few. Guided by the four-part CAN signal definition, we present CAN-D (CAN-Decoder), a modular, four-step pipeline for identifying each signal's boundaries (start bit and length), endianness (byte ordering), signedness (bit-to-integer encoding), and by leveraging diagnostic standards, augmenting a subset of the extracted signals with meaningful, physical interpretation. En route to CAN-D, we provide a comprehensive review of the CAN signal reverse engineering research. All previous methods ignore endianness and signedness, rendering them incapable of decoding many standard CAN signal definitions. Incorporating endianness grows the search space from 128 to 4.72E21 signal tokenizations and introduces a web of changing dependencies. In response, we formulate, formally analyze, and provide an efficient solution to an optimization problem, allowing identification of the optimal set of signal boundaries and byte orderings. In addition, we provide two novel, state-of-the-art signal boundary classifiers—both of which are superior to previous approaches in precision and recall in three different test scenarios—and the first signedness classification algorithm, which exhibits a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&gt;$</tex-math></inline-formula> 97% F-score. Overall, CAN-D is the only solution with the potential to extract any CAN signal that is also the state of the art. In evaluation on 10 vehicles of different makes, CAN-D's average <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell ^1$</tex-math></inline-formula> error is five times better (81% less) than all previous methods and exhibits lower average error, even when considering only signals that meet prior methods’ assumptions. Finally, CAN-D is implemented in lightweight hardware, allowing for an on-board diagnostic (OBD-II) plugin for real-time in-vehicle CAN decoding.

Предиктивная аналитика технического состояния систем тепловозов с использованием нейросетевых прогнозных моделей

Objective: Study of the possibility of carrying out predictive analysis of the technical condition of locomotive equipment using neural network predictive models enabling to plan the scope of equipment maintenance for routine types of maintenance and repair. Methods: A comparative assessment of the accuracy of forecasts made using a feedforward neural network and a recurrent network with an LSTM layer (Long Short-Term Memory) has been carried out. For training and test-ing of predictive models, we used the results of monitoring the parameters of the lubrication sys-tem of the 2TE116 (2ТЭ116) diesel locomotive by means of on-board diagnostics. Results: The aver-age interval for preventive inspections (TO-3) of locomotives in the existing locomotive mainte-nance system is 25–30 days, and therefore it is this interval that determines the minimum duration of the lead-in period, which the predictive model should provide. We have established that a mod-el based on a feedforward neural network provides sufficient accuracy only for short-term fore-casts with a lead period of no more than 1–3 days. With a further increase in the lead-in period, the error of the model res¬ponse increases to 10–15 %, which prevents it from being effectively used for solving practical problems associated with planning the operation of service locomotive depots. At the same time, the ave¬rage response error of the predictive model based on a recurrent net-work with an LSTM layer does not exceed 3,5–5 % over a 30-day lead-in period, so it can be used to plan the scope and timing of locomotive maintenance procedures. Practical importance: The possi-bility of using time-series analysis methods for predictive analytics of the technical condition of units and systems of a locomotive is shown. Predictive models based on recurrent neural networks with LSTM layers provide prediction accuracy and lead-in period sufficient for solving practical prob-lems that are associated with planning the scope and timing of locomotive maintenance.

Fuzzy inference system design for promoting an eco-friendly driving style in IoV domain

With the rise of both embedded technologies and the Internet of Things (IoT) integration, it is increasingly possible to realize complex systems in which, for example, vehicles are connected to the IoT domain capable of interacting directly with applications on mobile or embedded devices. In this work, a Machine to Machine (M2M) based platform able to recognize the driving style will be presented. An embedded device (On-Board Diagnostic (OBD)) has been considered to implement the Internet of Vehicles (IoV) paradigm exporting recognized data to other systems to promote an eco-friendly driving style by suggesting corrective actions to drivers. The architecture consists of the following elements: an on board device able to obtain information regarding speed, acceleration, jerk and fuel consumption, a cloud able to store all collected data, an M2M protocol such as Message Queuing Telemetry Transport (MQTT) to provide scalability to the architecture, and a Fuzzy Inference System to classify user behavior. All these elements are harmonized to achieve the common target of informing users about an Aggressive or Very-Aggressive driving style with the aim of reducing both speed and acceleration. The smart alerting system will promote a more eco-friendly driving style, contributing a reduction in CO2 emissions and fuel consumption. Performance evaluation confirms the efficiency of the proposed classifier and the overall benefits for drivers in fuel consumption reduction.

Quantifying the impact of driving style changes on light-duty vehicle fuel consumption

• We examine a set of four driving style heuristics implementable by human drivers. • These improvements are applied to a large set of representative drive cycles. • They can save the average US driver 6% fuel with a 1.5% increase in travel time. • Percentage fuel savings are consistent across locations and vehicle types. Modifying driving styles can help to reduce the energy use and emissions of driving without requiring changes to infrastructure or vehicle technology. Here, we evaluate the energy consumption and duration of trips before and after driving style changes. These modifications are made using emissions-friendly driving style heuristics that are easily implementable by drivers and do not require real-time feedback or on-board diagnostics. We use a data-driven approach to apply these heuristics to a representative baseline of U.S. drive cycles. The simulated driving-style improvements provide an average fuel savings per trip of 6%, alongside a 1.5% increase in trip duration. Decelerating early and reducing highway speeds can each contribute substantially to fuel savings. Accelerating more gradually contributes less. The percentage fuel savings are relatively consistent across locations and vehicle classes. These findings can inform several decision-makers, including drivers aiming to reduce fuel consumption, car manufacturers or software developers designing driving style feedback, and policy makers examining emissions savings opportunities.

Parallel attention-based LSTM for building a prediction model of vehicle emissions using PEMS and OBD

Portable emission measurement system (PEMS) testing, which is the most accurate measurement method for vehicle emissions, has been included into the regulations of vehicle emission standards in various countries. However, PEMS is expensive, and in the long-term measurement process, the monitoring data will exhibit outliers, which is a drift phenomenon. In addition, as a measurement method, it cannot prevent the occurrence of high vehicle emissions. To solve the above problem, this study proposes a parallel attention-based long short-term memory (PA-LSTM) for building an emission prediction model using PEMS and on board diagnostics (OBD). According to the characteristics of the real vehicle road test and bench test data, the PA-LSTM model adopts a parallel spatial attention coding mechanism, combined with a temporal attention decoding mechanism. Qualitative and quantitative experimental results show that the PA-LSTM model can achieve a more accurate prediction of vehicle emissions compared with other popular models, and the proposed model can eliminate outliers and restrain the offset of the zero levels in the PEMS data. The most significant thing is that the PA-LSTM model can foresee about the possible high vehicle emissions in the future and provide timely feed-back to the emission control system of the vehicle engine, so as to make corresponding control measurements in time and avoid the occurrence of high emissions.

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.

Empirical Capacity Measurements of Electric Vehicles Subject to Battery Degradation From V2G Services

Accelerated battery degradation, resulting in a reduced capacity, is the main concern when discussing vehicle-to-grid (V2G) services. This paper gives a unique empirical insight into the long-term effects on batteries performing an energy demanding V2G service. To this end, an extensive method for measuring the battery capacity of series produced electric vehicles (EVs) via the DC charge port was developed. The reproducibility of the method is demonstrated with repeated tests on new and aged vehicles and the results are compared with the EVs internal estimates, read via the on-board diagnostics port. The method is also validated with a state-of-the-art battery degradation model. The capacity test method is used to evaluate the battery degradation of a fleet of commercially owned EVs which, as a part of the world's first commercial operation, have been used for V2G services for five years. The EVs have, together with ±10 kW V2G chargers, been delivering primary frequency regulation for 15 hours per day with a daily energy throughput of 50.6 kWh. The usable battery capacity, initially found to be 23 kWh, after two years is reduced on average to 20.7 kWh and after five years is reduced to 18.9 kWh.