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Abstract Identity-Anonymized CAN (IA-CAN) protocol is a secure CAN protocol, which provides the sender authentication by inserting a secret sequence of anonymous IDs (A-IDs) shared among the communication nodes. To prevent malicious attacks from the IA-CAN protocol, a secure and robust system error recovery mechanism is required. This article presents a central management method of IA-CAN, named the IA-CAN with a global A-ID, where a gateway plays a central role in the session initiation and system error recovery. Each ECU self-diagnoses the system errors, and (if an error happens) it automatically resynchronizes its A-ID generation by acquiring the recovery information from the gateway. We prototype both a hardware version of an IA-CAN controller and a system for the IA-CAN with a global A-ID using the controller to verify our concept.

Abstract Present-day vehicles come with a variety of new features like the pre-crash warning, the vehicle-to-vehicle communication, semi-autonomous driving systems, telematics, drive by wire. They demand very high bandwidth from in-vehicle networks. Various ECUs present inside the automotive transmits useful information via automotive multiplexing. Transmission of data in real-time achieves optimum functionality. The high bandwidth and high-speed requirement can be achieved either by using multiple buses or by implementing higher bandwidth. But, by doing so, the cost of the network as well as the complexity of the wiring increases. Another option is to implement higher layer protocol which can reduce the amount of data transferred by using data reduction (DR) techniques, thus reducing the bandwidth usage. The implementation cost is minimal as the changes are required in the software only and not in hardware.

Abstract An energy management strategy (EMS) has an essential role in ameliorating the efficiency and lifetime of the powertrain components in a hybrid fuel cell vehicle (HFCV). The EMS of intelligent HFCVs is equipped with advanced data-driven techniques to efficiently distribute the power flow among the power sources, which have heterogeneous energetic characteristics. Decentralized EMSs provide higher modularity (plug and play) and reliability compared to the centralized data-driven strategies. Modularity is the specification that promotes the discovery of new components in a powertrain system without the need for reconfiguration. Hence, this article puts forward a decentralized reinforcement learning (Dec-RL) framework for designing an EMS in a heavy-duty HFCV. The studied powertrain is composed of two parallel fuel cell systems (FCSs) and a battery pack.

Abstract Nowadays, the need for a more sustainable mobility is fostering powertrain electrification as a way of reducing the carbon footprint of conventional vehicles. On the other side, the presence of multiple energy sources significantly increases the powertrain complexity and requires the development of a suitable Energy Management System (EMS) whose performance can strongly affect the fuel economy potential of the vehicle. In such a framework, this article proposes a novel methodology to reverse engineer the control strategy of a test case P2 Plug-in Hybrid Electric Vehicle (PHEV) through the analysis of experimental data acquired in a wide range of driving conditions. In particular, a combination of data obtained from On-Board Diagnostic system (OBD), Controller Area Network (CAN)-bus protocol, and additional sensors installed on the High Voltage (HV) electric net of the vehicle is used to point out any dependency of the EMS decisions on the powertrain main operating variables.

Abstract The automotive industry is facing a multifaceted problem of supervisory energy management, computational power, and digitalization. In response, this article proposes the use of agents utilizing the belief, desire, and intent (BDI) framework as a means to flexibly create online vehicle management systems (VMSs). Under such proposal, a community of agents form a vehicle configuration. Each agent represents a vehicle subsystem and contains knowledge specific to its respective hardware. With this knowledge and partial observation over its operating environment, each agent uses the BDI framework to deliberate over its actions. An interaction protocol, which implements a distributed constraint satisfaction problem (DCSP) algorithm, is used between the agents to create sensible emergent behavior of the vehicle. This interaction protocol allows independently reasoning components to produce emergent behavior that is flexible, robust, verifiable, and explainable.

Abstract Safety is always a crucial aspect of developing autonomous systems, and the motivation behind this project comes from the need to address the traffic crashes occurring globally on a daily basis. The present work studies the coexistence of the novel rule-based behavioral planning framework with the five key advanced driver assistance system (ADAS) features as proposed in this article to fulfill the safety requirements and enhance the comfort of the driver/passengers to achieve a receding-horizon autopilot. This architecture utilizes data from the sensor fusion and the prediction module for the prediction time horizon of 2 s iteratively, which is continuously moving forward (hence, the receding horizon), and helps the behavior planner understand the intent of other vehicles on the road in advance.

Abstract The innovations of vehicle connectivity have been increasing dramatically to enhance the safety and user experience of driving, while the rising numbers of interfaces to the external world also bring security threats to vehicles. Many security countermeasures have been proposed and discussed to protect the systems and services against attacks. To provide an overview of the current states in this research field, we conducted a systematic mapping study (SMS) on the topic area “security countermeasures of in-vehicle communication systems.” A total of 279 papers are identified based on the defined study identification strategy and criteria. We discussed four research questions (RQs) related to the security countermeasures, validation methods, publication patterns, and research trends and gaps based on the extracted and classified data. Finally, we evaluated the validity threats and the whole mapping process.

Abstract The acidification of lubricating oils during engine operation, and the subsequent additive neutralization, is an important challenge for Original Equipment Manufacturers and end-users. Often the decline in Total Base Number (TBN) and increase in Total Acid Number (TAN) is measured during engine operation as an indication of the oil’s condition and lifetime. This is clearly an oversimplification given that no consideration is given to the type of acid, how corrosive it is, or the type of base and how effective it is at neutralizing. Acids can be broadly categorized into mineral acids such as sulfuric/nitric and organic acids such as acetic. Traditionally, research has focused on understanding the effects of mineral acids such as sulfuric, which can be formed during the combustion of sulfur-containing fuel.

Abstract This article presents a survey on active safety control of X-by-wire electric vehicles. The steer-by-wire (SBW) system, brake-by-wire (BBW) system, and electric drive system are three critical techniques for X-by-wire electric vehicles. As to the three systems, the structure comparison and performance analysis of existing products and prototypes are conducted. Then two kinds of X-by-wire chassis configuration, i.e., the centralized type and the distributed type, are introduced. For active safety control, various control models are summarized, including vehicle dynamics model, single-track model, path tracking model, and wheel dynamics model. Based on the proposed model, different active safety control algorithms are introduced involving longitudinal dynamics control, handling stability control, rollover prevention control, path tracking control, and active fault-tolerant control.

Abstract The development of connected-vehicle technology, which includes vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications, opens the door for unprecedented active safety and driver-enhanced systems. In addition to exchanging basic traffic messages among vehicles for safety applications, a significantly higher level of safety can be achieved when vehicles and designated infrastructure locations share their sensor data. In this article, we propose a new system where cameras installed on multiple vehicles and infrastructure locations share and fuse their visual data and detected objects in real time. The transmission of camera data and/or detected objects (e.g., pedestrians, vehicles, cyclists, etc.) can be accomplished by many communication methods. In particular, such communications can be accomplished using the emerging Dedicated Short-Range Communications (DSRC) technology.

Abstract In this experimental study, a novel combustion technique, “reactivity-controlled compression ignition” (RCCI), has been investigated using alcohols acting as low-reactivity fuel (LRF) and mineral diesel acting as high-reactivity fuel (HRF). Combustion experiments were performed in a single-cylinder research engine at a constant engine speed of 1500 rpm and a low engine load of 3 bar brake mean effective pressure (BMEP). RCCI combustion is a practical low-temperature combustion (LTC) concept, which was achieved using three primary alcohols: Methanol, Ethanol, and Butanol in different premixed ratios (rp = 0.25, 0.50, and 0.75) with mineral diesel. Results showed a relatively superior performance and emissions characteristics of RCCI combustion compared to conventional compression ignition (CI) combustion. The influence of LRF was visible in RCCI combustion, which exhibited a more stable combustion compared to the baseline CI combustion.

Abstract Autonomous driving faces the difficulty of securing the lowest possible software execution times to allow a safe and reliable application. One critical variable for autonomous vehicles is the latency from the detection of obstacles to the final actuation response of the vehicle, especially in the case of high-speed driving. A prerequisite for autonomy software is that it enables low execution times to achieve superhuman reaction times. This article presents an in-depth analysis of a full self-driving software stack for autonomous racing. A modular software stack especially developed for high-speed autonomous driving is used and the latency of the software is analyzed in four main autonomy modules: perception, prediction, planning, and control. With the help of a trace point measurement method, it is possible to investigate the end-to-end latency and runtimes of the individual modules.

Abstract The automotive industry intends to create new services that involve sharing vehicle control information via a wide area network. In modern vehicles, an in-vehicle network shares information between more than 70 electronic control units (ECUs) inside a vehicle while it is driven. However, such a complicated system configuration can result in security vulnerabilities. The possibility of cyber-attacks on vehicles via external services has been demonstrated in many research projects. As advances in vehicle systems (e.g., autonomous drive) progress, the number of vulnerabilities to be exploited by cyber-attacks will also increase. Therefore, future vehicles need security measures to detect unknown cyber-attacks. We propose anomaly-based intrusion detection to detect unknown cyber-attacks for the Control Area Network (CAN) protocol, which is popular as a communication protocol for in-vehicle networks.

Abstract Greenhouse gas emissions reduction from the light-duty transportation fleet is urgent and should address both electric and conventional powertrain technologies. Internal combustion engines will continue to be employed for vehicle propulsion and fleet turnover is slow, encouraging reduction of carbon content in gasoline. Currently ethanol, a renewable fuel, is blended at the 10% level into petroleum to produce finished market gasoline. Ethanol enables a less carbon-intensive petroleum blendstock composition, providing for additional reduction, but this is often overlooked in studies. Carbon intensity, as a ratio of CO2 mass to heat released upon combustion, is a measure of well-to-wheels greenhouse gas production. The well-to-wheels carbon intensity of ethanol does not include its chemical carbon content because it arises from a renewable source, but does consider all upstream farming, production, and transportation carbon impacts.

Abstract Closed field testing of Connected and Automated Vehicles (CAV) is an essential pillar for verifying the functionality and performance of CAV and promoting its large-scale deployment. Recently, many closed test fields in the world have been newly built or rebuilt for testing CAV such as M-City in USA and AstaZero in Sweden. However, few construction methods, standards, and specifications of closed test fields for testing CAV were reported with details. We propose the construction practice of the Closed Connected and Automated Vehicles Test Field (CAVTest) of Chang’an University in China. The CAVTest has a multilayer architecture, from down to the top, which consists of an application scenario layer, a perception and a physical communication layer, a network link layer, and a management service layer, respectively.

Abstract This article focuses on a comparative research of the emissions discharged from four vehicles equipped with SI engines, which comply with different emission control systems (Euro 6, Euro 5, and Euro 3). The vehicles used for this work were installed with two different fuel injection technologies (direct injection and port fuel injection) and were operated with three different types of fuels (RON 95, M15, and E10). The tests were performed at the Joint Research Center (JRC) in Ispra using a state-of-the-art emissions test facility according to the European emissions legislation. The test bench included a chassis dynamometer and two different driving cycles were used: NEDC and US06.

Abstract ICVs are expected to make the transportation safer, cleaner, and more comfortable in the near future. However, the trend of connectivity has greatly increased the attack surfaces of vehicles, which makes in-vehicle networks more vulnerable to cyberattacks which then causes serious security and safety issues. In this article, we therefore systematically analyzed cyberattacks and corresponding countermeasures for in-vehicle networks of intelligent and connected vehicles (ICVs). Firstly, we analyzed the security risk of ICVs and proposed an in-vehicle network model from a hierarchical point of view. Then, we discussed possible cyberattacks at each layer of proposed network model.

Abstract The rapid development of connected and automated vehicle technologies together with cloud-based mobility services is transforming the transportation industry. As a result, huge amounts of consumer data are being collected and utilized to provide personalized mobility services. Using big data poses serious challenges to data privacy. To that end, the risks of privacy leakage are amplified by data aggregations from multiple sources and exchanging data with third-party service providers, in face of the recent advances in data analytics. This article provides a review of the connected vehicle landscape from case studies, system characteristics, and dataflows. It also identifies potential challenges and countermeasures.

Abstract Electrification of vehicles can improve energy efficiency and reduce emissions in vehicle operations. Studies have focused on designing powertrains of several topologies, including serial, parallel, and power-split hybrid powertrains. In this study, a design and control co-optimization is demonstrated for a novel mixed-powertrain architecture. The component sizes of the mixed powertrain are optimized to minimize the fuel and component costs. Nested optimization is applied with a surrogate integrated operation and control model that evaluates powertrain performance in the inner loop. The surrogate model is trained to capture the powertrain performance under a near-optimal power management approach, namely, the equivalent consumption minimization strategy (ECMS). Using sequential quadratic programming, optimal results are obtained and verified using a high-fidelity powertrain model which is equipped with ECMS control.

Abstract This article addresses the architecture development for a commercial vehicle fuel cell electric powertrain by establishing a clear multi-step formalized workflow that employs a unique technoeconomic solution for architecture selection. The power capability of the fuel cell, the energy capacity and chemistry of the electrical energy storage (battery), the DC-DC converter (including the input current rating and isolation resistance requirements), the traction drive solution, the on-board hydrogen storage solution, and the real-time power-split management of the fuel cell and the battery are all considered and developed in this effort. The methods were used to select architecture for Class 8 urban, regional, and line haul applications. When compared to traditional load-following power-split controllers, an energy management power-split controller can increase system energy efficiency by up to 19.5%.