On-board Diagnostics Research Articles

IoT-based Data Acquisition Unit for aircraft and road vehicle

This paper describes the development of a Data Acquisition Unit (DAU). The device is primarily designed for light aircraft; however, it allows data collection from road vehicles (equipped with Bluetooth module to measure the engine and vehicle data from onboard diagnostics interface). The primary purpose of DAU is the Internet of Things (IoT) based tracking of aircraft operated by the Air Training and Educational Centre (LVVC) of the University of Žilina. If some flight parameters exceed the given limitations, the data file is marked as a non-compliance flight record. LVVC utilises this to get the information if unauthorised manoeuvres with the aeroplane were performed. The device is also used as a real-time tracker and anti-collision system. Several test flights were performed to proof of concept. The acquired data will also be used for analysing localisation algorithms at the Department of control and information systems of the University of Žilina and is the predecessor of the proposed APVV project.

Цифровая трансформация открытых горных работ и новое поколение карьерной техники

The The paper addresses the issues of designing onboard systems and complex digitalization of open-pit mining equipment. A generalized schematic diagram and composition of the On-Board Robotic Control System for open-pit equipment (OBRCS OPE) are provided. The option of the On-Board Robotic Control System for mining dump trucks (OBRCS MDT) and schematics of the on-board robotic system of the BELAZ dump truck are described in more detail. A layout option is suggested for an autonomous ready dump truck, a modification that meets all the requirements for the digital transformation (DT) generation of dump trucks i.e. the ability to operate as part of the mine fleet management systems and robotic control systems, as well as the availability of modern on-board industrial safety systems and remote diagnostics. The DT Generation of mining equipment will allow mining companies to achieve the up-to-date level in digital transformation of surface mining operations and will cut the amount of investments required in upgrading the mining transport system.

How accurately can we measure vehicle fuel consumption in real world operation?

European Union (EU) regulations setting CO2 emission standards forlight-duty (LDV)and heavy-duty vehicles (HDVs) requirethe Commission to monitor in-use fuel consumption (FC) using on-board fuel consumption monitoring (OBFCM) devices. For LDVs, the accuracy to measure fuel consumed is set by regulation to ±5%, while for HDVs no requirements exist so far. In this study, OBFCM data from15 LDVs and 12 HDVs were recorded in lab and on-road trips. Results indicate that the fuel consumed measured by the OBFCM, for the majority of the vehicles, satisfies the accuracy requirementsof ±5% over complete laboratory tests or complete on-road trips (maximum vehicle average uncertainty ~7%). Statistical analysis showed that on-board diagnostics (OBD) accuracy can be impacted by the average vehicle speed, acceleration, and overall trip dynamicity. The lowest accuracy can be expected when vehicles are driven at low speeds under transient conditions. For most of the vehicles the distance calculated from the OBD speed signal has an accuracy within ±1.5% on-road whenGlobal Positioning System (GPS) distance is used as a reference. Both HDV and LDV results, confirmed that OBD accuracy and precision in measuring fuel consumed and distance improved for monitoring over longer periods (complete trips vs short phases). The results of this study could be used to further support the standardization of OBFCM accuracy in vehicles, and the setup of the EU real-world CO2emissions monitoring approach.

Robust Data-Driven Framework for Driver Behavior Profiling Using Supervised Machine Learning

Driver behavior profiling has been gaining increased attention due to its relevance in many applications. For instance, car insurance telematics and fleet management entities have been recently using smartphones’ embedded sensors, On-Board Diagnostics II (OBDII) units and other on-board IoT devices to collect data on vehicles’ behavior and evaluate the risk profile of drivers. In this context, this paper presents a robust data-driven framework for calculating drivers’ risk profile measured in terms of the additive inverse of the predicted risk probability. The Strategic Highway Research Program 2 (SHRP2) naturalistic driving study (NDS) dataset, which is the largest dataset of its kind to date, is utilized to build the risk prediction models. Crash and near-crash events are used to quantify riskiness whereas balanced baseline driving events (i.e., events captured during normal day to day driving episodes) are used to reflect total exposure or driving time per driver. Thirteen mutually exclusive behavioral risk predictors are identified, and the feature matrix is formulated. A sensitivity analysis is then performed to find the best number of balanced baseline events below which drivers are filtered out. Different machine learning models are selected, customized, and compared to achieve best risk prediction performance. Finally, the utilization of the proposed prediction model within an envisioned driver profiling cloud-based framework is briefly discussed.

Health Checking Autonomous Attitude Control System of Earth-Observing Miniature Satellite in Initial Orientation Modes

The problems of autonomous digital control of a spacecraft (SC) orientation and checking the operability of its attitude control system (ACS) in the initial orientation modes are considered. The developed methods, algorithms, and simulation results for a miniature Earth-surveying satellite in a Sun-synchronous orbit are presented. Autonomous angular guidance and modularly limited vector digital control using a vector of the modified Rodriguez parameters are applied to bring the SC's orientation from completely arbitrary to the required one. We briefly discuss the health check of the mini-satellite's ACS, as well as our discrete algorithms for onboard diagnostics of faults based on computer processing of available measurements and explicit relations.

Development of a Practical Method to Estimate the Eco-Level of Driver Performance

Motor vehicle’s fuel consumption is one of the main sources of energy consumption in road transportation and is highly influenced by driver performance in the process of driving. Eco-driving behavior has been proved to be an effective way to improve the fuel efficiency of vehicles. Essential to the efforts towards saving vehicle fuel is the need to estimate the eco-level of driver performance accurately and practically. Depending on on-board diagnostics and Global Position devices, individual vehicle’s instantaneous fuel consumption, engine revolution and torque, speed, acceleration, and dynamic location were collected. Back-propagation network was adopted to explore the relationship between vehicle fuel consumption and the parameters of driver performance. Taking 700 data samples in basic segments of urban expressways as our training set and 100 data samples as validation test, we found the optimal model structure and parameters through repeated simulation experiments. In addition to the average and standard deviation value, the fluctuation frequency of driver performance data was also viewed as influence factors in eco-level estimation model. The average estimation accuracy of our developed model has been tested to be 96.37%, which is quite higher than that of linear regression model. The study results provide a practical way to evaluate drivers’ performance from the perspective of fuel consumption and thus give basis for rewarding best drivers within eco-driving programs.

On Rigorous Model-Order Reduction of the Thermal and Oxygen Storage Dynamics of Three Way Catalytic Converters

Abstract We present a reduced-order model (ROM) for the temperature and oxygen storage dynamics of three way catalysts (TWCs). The thermal and oxygen storage dynamics are described using a set of coupled, nonlinear partial differential equations (PDEs) developed and experimentally validated in previous research. Advancements in on-board diagnostic (OBD) design are moving in the direction of using physics-based models that would retain as much physical insights as possible. Retaining the one-dimensional (1D) evolution of the internal storage dynamics along the device length is key for the development of accurate emission control strategies. In this work, we adopt the numerical projection orthogonal approach combined with the analytical features of Galerkin reduction method to define a set of ordinary differential equations (ODEs) to describe the oxygen storage and temperature dynamics throughout the device life. Using experimental data collected over three TWC devices, each of different age, and under the excitation of different real drive cycles, we validate the model and quantify the relation between the number of reduced-order states versus model accuracy for devices both new and at different stages of life. The input dependent characteristics of the developed reduced-model model is also investigated using a power spectral density (PSD) analysis. Finally, we show that an initial tuning of the reduced model parameters for a fresh catalyst guarantees satisfactory modeling performance throughout the device life, regardless of the driving scenario.

Predictive Maintenance of Bus Fleet by Intelligent Smart Electronic Board Implementing Artificial Intelligence

This paper is focused on the design and development of a smart and compact electronic control unit (ECU) for the monitoring of a bus fleet. The ECU system is able to extract all vehicle data by the on-board diagnostics-(ODB)-II and SAE J1939 standards. The integrated system Internet of Things (IoT) system, is interconnected in the cloud by an artificial intelligence engine implementing multilayer perceptron artificial neural network (MLP-ANN) and is able to predict maintenance of each vehicle by classifying the driver behavior. The key performance indicator (KPI) of the driver behavior has been estimated by data mining k-means algorithm. The MLP-ANN model has been tested by means of a dataset found in literature by allowing the correct choice of the calculus parameters. A low means square error (MSE) of the order of 10−3 is checked thus proving the correct use of MLP-ANN. Based on the analysis of the results, are defined methodologies of key performance indicators (KPIs), correlating driver behavior with the engine stress defining the bus maintenance plan criteria. All the results are joined into a cloud platform showing fleet efficiency dashboards. The proposed topic has been developed within the framework of an industry research project collaborating with a company managing bus fleet.

Vehicle Health Monitoring System Using CAN

Modern vehicles are integrated with more number of ECUs and sensors which make them smarter in terms of engine decisions, performance, fuel efficiency, security and stability. ECUs also provide useful data in the form of diagnostic codes via OBDII (on-board diagnostics) protocol, these codes determine a specific issue in the vehicular system which is used by service technicians to address the issues occurred. This paper deals with the exploring the potential of OBDII codes from engine ECU and compute the data on an Android device to monitor the vehicle health status in real-time. The whole idea is to provide portable diagnosis solutions and integration of such system into in-vehicle entertainment like Google’s Android Auto and Apple’s CarPlay which can be one of key component for V2X and IOVs (Internet of Vehicles).

Technologies for congestion pricing

Congestion pricing of high-demand roadways seeks to influence travelers’ route choices, trip timing, modes, and destination choices, to keep vehicles moving and avoid excessive congestion. This paper describes the use of various technologies to enable more advanced and cost-effective congestion pricing applications, and a credit-based policy to ensure equitable network access for all travelers.Video-based systems require cameras to capture the state of traffic, plus some form of communication back to users. Both DSRC and cellular-based systems use GPS data to price roads and toll users based on traffic conditions. DSRC employs roadside units (RSUs) to receive and send messages to in-vehicle DSRC units. A cellular-based system could use communications from cellular towers in combination with a smartphone, on-board diagnostics port (OBD-II), or pre-installed cellular chip. DSRC is a valuable technology to pilot congestion pricing at highly congested locations, such as bridges and major highways, while cellular communications enable congestion pricing across entire networks.VMT fees can be relatively simple, or variable in space and time, facilitating transportation-agency cost recovery. A next step for roadway management is congestion pricing (CP), to better reflect the marginal delay costs of one's travel choices. When coupled with travel credits, CP can better ensure welfare gains for most travelers.

Physics-guided Energy-efficient Path Selection Using On-board Diagnostics Data

Given a spatial graph, an origin and a destination, and on-board diagnostics (OBD) data, the energy-efficient path selection problem aims to find the path with the least expected energy consumption (EEC). Two main objectives of smart cities are sustainability and prosperity, both of which benefit from reducing the energy consumption of transportation. The challenges of the problem include the dependence of EEC on the physical parameters of vehicles, the autocorrelation of the EEC on segments of paths, the high computational cost of EEC estimation, and potential negative EEC. However, the current cost estimation models for the path selection problem do not consider vehicles’ physical parameters. Moreover, the current path selection algorithms follow the “path + edge” pattern when exploring candidate paths, resulting in redundant computation. Our preliminary work introduced a physics-guided energy consumption model and proposed a maximal-frequented-path-graph shortest-path algorithm using the model. In this work, we propose an informed algorithm using an admissible heuristic and propose an algorithm to handle negative EEC. We analyze the proposed algorithms theoretically and evaluate the proposed algorithms via experiments with real-world and synthetic data. We also conduct two case studies using real-world data and a road test to validate the proposed method.

Real time localization solution for land vehicle application using low-cost integrated sensors with GPS

The technique proposed in this research demonstrates a real time nonlinear data fusion solution based on extremely low-cost and grade inertial sensors for land vehicle navigation. Here, the utilized nonlinear multi-sensor data fusion (MSDF) is based on the combination between extremely low-cost micro electrical mechanical systems (MEMS) inertial, heading, pressure, and speed sensors in addition to satellite-based navigation system. The integrated navigation system fuses position and velocity states from the Global Positioning System (GPS), the velocity measurements from an odometer, heading angle observation from a magnetometer and navigation states from an inertial navigation system (INS). The implemented system performance is assessed through the post-processing of collected raw measurements and real time experimental work. The system that runs the real-time experiments is established on three connected platforms, two of them are based on a 32-bit ARMTM core and the third one is based 16-bit AVR ATMEL microcontroller. This microcontroller is connected to an on-board diagnostics (OBD) shield to collect the vehicle speed measurements. The raw data obtained from the integrated sensors is saved and post processed in MATLAB®. In normal conditions, the estimated position errors are reduced through the usage of INS/GPS integration with heading observation angle from a magnetometer. In GPS-denied environments, the integrated system uses the observations from INS, magnetometer in addition to the velocity from odometer to ensure a continuous and accurate navigation solution. A complementary filter (CF) is implemented to estimate and improve the pitch and roll angles calculations. In addition to that, an unscented Kalman filter (UKF) is used cascaded with the designed CF to complete the designed sensors fusion algorithm. Experimental results show that the designed MSDF can achieve a good level of accuracy and a continuous localization solution of a land vehicle in different GPS availability cases and can be implemented on the available in the market processors to be run in real time.

Securing the On-Board Diagnostics Port (OBD-II) in Vehicles

Securing the On-Board Diagnostics Port (OBD-II) in Vehicles

Discovering CAN Specification Using On-Board Diagnostics

The work presented in this article leverages on-board diagnostics for reverse-engineering CAN traffic data. The analysis of these data can be utilized to improve in-vehicle network security. Nicola Nicolici, McMaster University.

Impacts of roundabouts in suburban areas on congestion-specific vehicle speed profiles, pollutant and noise emissions: An empirical analysis

Increasing concern about global warming and air quality has meant an increasing use of energetic and environmental indicators in roundabout design. This research compares different suburban roundabouts in terms of traffic performance, pollutant and noise emissions through an integrated empirical assessment. Field measurements were carried out with a light duty vehicle in single-lane (SL), compact two-lane (CTL) and multi-lane (ML) roundabouts using Portable Emission Measurements Systems, On-Board Diagnostic scan tool and Sound Level Meter, to measure real-world exhaust emissions, engine activity and acoustic data, respectively. Afterwards, predictive discrete choice models that correlate the probability of occurrence of speed profiles (no stop, stop once and multiple stops) with roundabout operational parameters were developed. Although SL yielded the lowest carbon dioxide (CO2) per vehicle, a high equivalent continuous A-weighted sound level (LAeq) was returned because vehicles drove at moderate speeds in the approach and low conflicting traffic was identified when compared to the other layouts. CTL was the worst option in terms of both CO2 and nitrogen oxides (NOx). The proposed methodology can be used to quantify the performance of roundabout layouts in suburban areas by simply identifying their traffic volumes, noise level, pollutant emission and representative speed profiles. This can help researchers, traffic planners or practitioners to reduce congestion and emissions, and enhance road traffic management near urban areas.

Acquisition, Modeling, and Evaluating Method of Driving Behavior Based on OBD-II: A Literature Survey

In this paper, we present the results of a literature survey related to driving behavior that aims to provide an overview of recent research related to driving behavior, including how to obtain data from onboard diagnostics (OBD-II), analyze data, model data, and evaluate models or systems. Driving behavior is closely related to driving habits that can be used for driver identification, driving characterization, improve fuel efficiency, and any other things. The contents of the discussion are arranged based on the research objectives, then will discuss the methods used and the evaluation results. Through this research is expected to provide insight into the acquisition and utilization of driving behavior for various things. Furthermore, provide an overview of research opportunities in the future.

Information control and diagnostic systems of modern vehicles

Problem. The article analyzes displays and work units that communicate with all connected components through the on-board communication controller to control and display information on the display. Particular attention is paid to control and measuring devices and on-board diagnostics, information systems of hybrid vehicles. Goal. The object of research is the process of analyzing the functionality of information control and diagnostic systems. The purpose of the study is to increase the competitiveness of vehicles by expanding the functionality of information systems. Methodology. The research method is based on the use of a systematic approach to the analysis and synthesis of information vehicles, on the rational combination of theoretical and experimental developments and the generalization of scientific results. Results. The basic functions of most vehicle combinations are the same, although some functional units, including microcontrollers, program-based storage integrated circuits, and standard external devices, sometimes differ significantly. Leading companies that produce modern automotive and tractor equipment, currently use extensive microprocessor on-board control systems that monitor the condition of the battery, clutch, shock absorbers, compression in the cylinders of the ignition system, etc. Features of information systems of hybrid vehicles are that in them the function of the assistant with recommendations to the driver concerning fuel saving is realized. Practical value. The information control and diagnostic systems of modern vehicles are considered. Particular attention is paid to instrumentation and onboard diagnostics, information systems of hybrid vehicles. The information systems Honda CR-Z and Chevrolet Volt are shown.Key words: vehicle, information system, control and diagnostic systems, measuring instruments, on-board diagnostics

TrajData: On Vehicle Trajectory Collection With Commodity Plug-and-Play OBU Devices

For years, vehicle trajectory data have increasingly been important for a wide range of applications, from driver behavior investigation/classification, travel time/distance estimation, and routing in vehicular networks, to vehicle energy/emission evaluation. This article presents TrajData, the first systematic solution to reliable vehicle trajectory data collection, with only reliance on commercial-off-the-shelf (COTS) onboard unit (OBU) devices that utilize lightweight GPS modules and low-cost onboard diagnostics (OBD) readers. In the practical use of trajectory collection, GPS outages inevitably occur in urban environments thereby leading to large trajectory errors as well as missing vehicle location data. To resolve this, we propose a novel data-fusion-enabled deep learning approach with the purpose of achieving reliable vehicle trajectory collection in various urban road conditions. Specifically, we leverage motion information retrieved from OBD readers in TrajData to help reconstruct the trajectory data during GPS outages. By investigating the changes of direction angle from the OBD readings, we can identify different types of road sections. Furthermore, we integrate the neural arithmetic logic units (NALUs) into our trajectory reconstruction model to tame the challenges when GPS outages take place in various road sections. Experimental results from realistic data have demonstrated the effectiveness and reliability of the proposed method. In the road test, TrajData achieves an average position error below 15-m around a 60-s GPS outage, even in complex road sections, i.e., continuous turns and driving with accelerations/decelerations resulting in frequent changes of direction and speed.

Hybrid-electric passenger car energy utilization and emissions: Relationships for real-world driving conditions that account for road grade

Past research showed on-road emissions patterns unique to hybrid electric vehicles (HEVs), indicating the need to account for them in emissions models as projected HEV sales increase over the coming decades. This work defines and characterizes a variable that quantifies HEV operating behavior to inform future development of new HEV emissions models based on current knowledge of conventional vehicle (CV) emissions patterns. Instantaneous hybridization factor (IHF), was quantified using on-road data collected from a 2010 Toyota Camry HEV. IHF is the ratio of electric system power to total system power and accounts for energy storage in the high voltage battery (IHF ranges from −1 to +1). Relationships between IHF and vehicle specific power (VSP), road type and road grade were examined. Negative VSP resulted in regenerative braking operation (IHF = −0.01 to −1) 90% of the time. IHF identified the VSP range where HEV operation was highly variable (VSP = −1 to 8 kW/ton) when driving at speeds below the ICE-off threshold (42 mph). VSP and IHF together account for 76–86% of the variability in HEV CO2 emissions in this study. CO2 model results using VSP computed with the measured real-world road grade (R2 = 0.86) gave improved fits over the no-grade VSP model (R2 = 0.69). This study establishes one framework for calculating the instantaneous HEV power split, confirms the need to include road grade in VSP for accurate modeling of vehicle emissions, and identified the need for significant improvements in on-board diagnostic (OBD) scantool measurement requirements for HEVs in three areas: (1) temporal resolution (sub-second to capture transient events such as ICE restarts); (2) simultaneous data logging capability for multiple controller area networks (i.e., engine and HEV parameters together); and (3) improved data precision.

Design of special vehicle condition monitoring system based on J1939

The special vehicle is one of the important equipment in China’s military and aerospace fields, and its condition is closely related to the success of the mission. In view of the lack of real-time condition monitoring capability of special vehicles, a condition monitoring system based on J1939 protocol is designed in this paper. The system communicate with the Electronic Control Unit (ECU) in Controller Area Network (CAN) through the On Board Diagnostics (OBD) interface to obtain the real-time condition data of the vehicle and improve the active safety of the vehicle. The test results show that the system realizes the data exchange with the Electronic Control Unit (ECU), outputs the data to the personal computer (PC) through the serial port, realizes the protocol data visualization, and meets the expected design purpose.