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The automobile industry no longer relies on pure mechanical systems; instead, it benefits from many smart features based on advanced embedded electronics. Although the rise in electronics and connectivity has improved comfort, functionality, and safe driving, it has also created new attack surfaces to penetrate the in-vehicle communication network, which was initially designed as a close loop system. For such applications, the Controller Area Network (CAN) is the most-widely used communication protocol, which still suffers from various security issues because of the lack of encryption and authentication. As a result, any malicious/hijacked node can cause catastrophic accidents and financial loss. This paper analyses the CAN bus comprehensively to provide an outlook on security concerns. It also presents the security vulnerabilities of the CAN and a state-of-the-art attack surface with cases of implemented attack scenarios and goes through different solutions that assist in attack prevention, mainly based on an intrusion detection system (IDS).

The work presented introduces a system that encompasses general notions about the dashboard computer and Arduino platform, used to create an automotive diagnostic tool through the OBDII port. This involves establishing a connection between the Arduino platform, a Bluetooth module, and a Bluetooth adapter that communicates with the Engine Control Unit (ECU), transmitting information displayed on an LCD screen. To highlight the system’s performance, a comparison of results was conducted with an authorized automotive tester from an auto service, using two different vehicle models.

The vehicle testing–validation phase is a crucial and demanding task in the automotive development process for vehicle manufacturers. It ensures the correct operation, safety, and efficiency of the vehicle. To meet this demand, some commercial solutions are available on the market, but they are usually expensive, have few connectivity options, and are PC-dependent. This paper presents an IoT-based intelligent low-cost system for vehicle data acquisition during on-road tests as an alternative solution. The system integrates low-cost acquisition hardware with an IoT server, collecting and transmitting data in near real-time, while artificial intelligence (AI) algorithms process the information and report errors and/or failures to the manufacturing engineers. The proposed solution was compared with other commercial systems in terms of features, performance, and cost. The results indicate that the proposed system delivers similar performance in terms of the data acquisition rate, but at a lower cost (up to 13 times cheaper) and with more advanced features, such as near real-time intelligent data processing and reduced time to find and correct errors or failures in the vehicle.

With the help of advanced technology, the automotive industry is in continuous evolution. Modern vehicles are not only comprised of mechanical components but also contain highly complex electronic devices and connections to the outside world. Today's vehicle usually has between 30 and 70 ECUs (Electronic Control Units), which communicate with each other over standard communication protocols. There are different types of in-vehicle network protocols and bus systems, including the Controlled Area Network (CAN), Local Interconnected Network (LIN), FlexRay, Media Oriented System Transport (MOST), and Automotive Ethernet (AE). Modern cars are also able to communicate with other devices through wired or wireless interfaces such as USB, Bluetooth, Wi-Fi or even 5G. Such interfaces may expose the internal network to the outside world and can be seen as entry points for cyber-attacks. In this paper, the main interest is in the AE network protocol. AE is a special Ethernet design that provides the bandwidth needed for today's applications, and the potential for even greater performance in the future. However, AE is a \"best effort\" protocol, which cannot be considered reliable. This implies that it is not trustworthy in terms of reliability and timely deliveries. The focus of this paper is to present a state-of-the-art survey of security threats and protection mechanisms relating to AE. After introducing and comparing the different protocols being used in the embedded networks of current vehicles, we analyze the potential threats targeting the AE network and describe how attackers' opportunities can be enhanced by the new communication abilities of modern cars. Finally, we present and compare the AE security solutions currently being devised to address these problems and propose some recommendations and challenges to deal with security issue in AE protocol.

The transportation industry's focus on improving performance and reducing costs has driven the integration of IoT and machine learning technologies. The correlation between driving style and behavior with fuel consumption and emissions has highlighted the need to classify different driver's driving patterns. In response, vehicles now come equipped with sensors that gather a wide range of operational data. The proposed technique collects critical vehicle performance data, including speed, motor RPM, paddle position, determined motor load, and over 50 other parameters through the OBD interface. The OBD-II diagnostics protocol, the primary diagnostic process used by technicians, can acquire this information via the car's communication port. OBD-II protocol is used to acquire real-time data linked to the vehicle's operation. This data are used to collect engine operation-related characteristics and assist with fault detection. The proposed method uses machine learning techniques, such as SVM, AdaBoost, and Random Forest, to classify driver's behavior based on ten categories that include fuel consumption, steering stability, velocity stability, and braking patterns. The solution offers an effective means to study driving behavior and recommend corrective actions for efficient and safe driving. The proposed model offers a classification of ten driver classes based on fuel consumption, steering stability, velocity stability, and braking patterns. This research work uses data extracted from the engine's internal sensors via the OBD-II protocol, eliminating the need for additional sensors. The collected data are used to build a model that classifies driver's behavior and can be used to provide feedback to improve driving habits. Key driving events, such as high-speed braking, rapid acceleration, deceleration, and turning, are used to characterize individual drivers. Visualization techniques, such as line plots and correlation matrices, are used to compare drivers' performance. Time-series values of the sensor data are considered in the model. The supervised learning methods are employed to compare all driver classes. SVM, AdaBoost, and Random Forest algorithms are implemented with 99%, 99%, and 100% accuracy, respectively. The suggested model offers a practical approach to examining driving behavior and suggesting necessary measures to enhance driving safety and efficiency.

The development of electric vehicles (EVs) has been incremental because EVs satisfy a significant demand for energy sources. Electronic control unit (ECU) is an important component that processes the electric signals received from various sensors for generating the control signals for the actuators. Automotive control systems were initially operated manually throughout the automotive revolution based on the responses of input signals received from ECUs and drivers. Most of the functions in EV are controlled by the ECU and every ECU consumes power at all times even if it is not in use. The larger power consumption of passive ECUs like adaptive lighting systems (ALS), automatic wiper systems (AWS) brake light systems (BLS), etc., affect the life of ECUs and the range of EVs. This article is primarily concerned with limiting power consumption by switching the power supply to the passive ECUs based on their requirements. Hence, to achieve the objective, the intelligent zone (i-zone) based master ECU is triggered to activate the slave ECUs. Designing suites including Proteus and KiCAD were used for designing the circuits including master as well as slave ECU. This prototype is built using three secondary ECUs such as ALS & AWS and BLS which are controlled using i-zone-based master ECU. The performance of this implemented design is evaluated, and it is discovered that almost 40% of the battery consumption is reduced. This i-zone-based master ECU and all its slave ECUs manage power while ensuring the safety and reliability of EVs.

To meet future goals of energy sustainability and carbon neutrality, disruptive changes to the current energy mix will be required, and it is expected that renewable fuels, such as hydrotreated vegetable oil (HVO), will play a significant role. To determine how these fuels can transition from pilot scale to the commercial marketplace, extensive research remains needed within the transportation sector. It is well-known that cold engine thermal states, which represent an inevitable portion of a vehicle journey, have significant drawbacks, such as increased incomplete combustion emissions and higher fuel consumption. In view of a more widespread HVO utilization, it is crucial to evaluate its performance under these conditions. In the literature, detailed studies upon these topics are rarely found, especially when HVO is dealt with. Consequently, the aim of this study is to investigate performance and exhaust pollutant emissions of a compression ignition engine running on either regular (petroleum-derived) diesel or HVO at different engine thermal states. This study shows the outcomes of warm-up/cool-down ramps (from cold starts), carried out on two engine operating points (low and high loads) without modifying the original baseline diesel-oriented calibration. Results of calibration parameter sweeps are also shown (on the same engine operating points), with the engine maintained at either high or low coolant temperature while combustion phasing, fuel injection pressure, and intake air flow rate are varied one-factor at a time, to highlight their individual effect on exhaust emissions and engine performance. HVO proved to produce less engine-out incomplete combustion species and soot under all examined conditions and to exhibit greater tolerance of calibration parameter changes compared to diesel, with benefits over conventional fuel intensifying at low coolant temperatures. This would potentially make room for engine recalibration to exploit higher exhaust gas recirculation, delayed injection timings, and/or lower fuel injection pressures to further optimize nitrogen oxides/thermal efficiency trade-off.

Energy and mobility are currently powered by conventional fuels, and for the specific case of spark ignition (SI) engines, gasoline is dominant. Converting these power-units to hydrogen is an efficient and cost-effective choice for achieving zero-carbon emissions. The use of this alternative fuel can be combined with a circular-economy approach that gives new life to the existing fleet of engines and minimizes the need for added components. In this context, the current work scrutinizes specific aspects of converting a small-size passenger car to hydrogen fueling. The approach combined measurements performed with gasoline and predictive 0D/1D models for correctly including fuel chemistry effects; the experimental data were used for calibration purposes. One particular aspect of H2 is that it results in lower volumetric efficiency compared to gasoline, and therefore boosting requirements can feature significant changes. The results of the 0D/1D simulations show that one of the main conclusions is that only stoichiometric operation would ensure the reference peak power level; lean fueling featured relative air–fuel ratios too low for ensuring the minimum value of 2 that would allow mitigating NOx formation. Top speed could be instead feasible in lean conditions, with the same gearbox, but with an extension of the engine speed operating range to 7000 rpm compared to the 3700 rpm reference point with gasoline.

This study examines the effects on combustion, engine performance and exhaust pollutant emissions of a modern Euro 6, dual-loop EGR, compression ignition engine running on regular EN590-compliant diesel and hydrotreated vegetable oil (HVO). First, the potential of HVO as a “drop-in” fuel, i.e., without changes to the original, baseline diesel-oriented calibration, was highlighted and compared to regular diesel results. This showed how the use of HVO can reduce engine-out emissions of soot (by up to 67%), HC and CO (by up to 40%), while NOx levels remain relatively unchanged. Fuel consumption was also reduced, by about 3%, and slightly lower combustion noise levels were detected, too. HVO has a lower viscosity and a higher cetane number than diesel. Since these parameters have a significant impact on mixture formation and the subsequent combustion process, an engine pre-calibrated for regular diesel fuel could not fully exploit the potential of another sustainable fuel. Therefore, the effects of the most influential calibration parameters available on the tested engine platform, i.e., high-pressure and low-pressure EGR, fuel injection pressure, main injection timing, pilot quantity and dwell-time, were analyzed along single-parameter sweeps. The substantial reduction in engine-out soot, HC and CO levels brought about by HVO could give the possibility to implement additional measures to limit NOx emissions, combustion noise and/or fuel consumption compared to diesel. For example, higher proportion of LP EGR and/or smaller pilot quantity could be exploited with HVO, at low load, to reduce NOx emissions to a greater extent than diesel, without incurring penalties in terms of incomplete combustion species. Conversely, at higher load, delayed main injection timings and reduced rail pressure could reduce combustion noise without exceeding soot levels of the baseline diesel case.

This paper assesses the application of the second-law of thermodynamics in a military Environmental Control Unit (ECU) to evaluate the exergy destruction (or irreversibility) in each component when operating at high ambient temperature. Experimental testings were conducted on three ECUs, 1.5 (5.3 kW), 3 (10.6 kW), and 5 (17.6 kW) tons of refrigeration (RT), to assess the potential contribution of each component to enhance the overall energy efficiency of the system, and to determine the feasibility of the thermodynamic model presented herein. The analysis provided for extreme high ambient conditions up to 51.7 °C (125 °F). The results yielded that the highest irreversibility was associated with compressors (32.4% to 42.5%). This is followed by the heat exchanges (19.6% to 32.9%) in the case of 1.5-RT and 3-RT units, whereas for the 5-RT unit, the highest irreversibility was associated with the evaporator followed by the one of the compressors. In the 3-RT ECU, the condenser’s second-law efficiency enhanced due to an additional fan, yet the working refrigerant increased the irreversibility in the expansion device. The second-law analysis recognized the components with the highest exergy destruction and identified the direction to enhance the exergetic efficiency of any ECU operating at high-temperature climate.