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It is recognized that the heavier vehicles, the more emissions, thus the more imperative to electrify. In this study, long haul heavy-duty trucks are referred as HDTs, which are recognized as one of the hard-to-electrify vehicle segments, though the automotive industry has gained trending advantages of electrifying both light-duty cars and SUVs. Since big rigs such as Class 8 HDTs have significant road-block challenges for electrification due to the demanding long-hour work cycles in all weathers, this study focuses on quantifying those electrification challenges by taking advantage of the public data of Class 8 tractors & trailers. Tesla Semi is the research target though its vehicle spec data is sorted out with fragmentary information in the public domain. The key task is to analyze the battery capacity requirements due to environmental temperature and inherent aging over the lifespan.

Cellular Vehicle-to-Everything (C-V2X) is considered an enabler for fully automated driving. It can provide the needed information about traffic situations and road users ahead of time compared to the onboard sensors which are limited to line-of-sight detections. This work presents the investigation of the effectiveness of utilizing the C-V2X technology for a valet parking collision mitigation feature. For this study a LiDAR was mounted at the FEV North America parking lot in a hidden intersection with a C-V2X roadside unit. This unit was used to process the LiDAR point cloud and transmit the information of the detected objects to an onboard C-V2X unit. The received data was provided as input to the path planning and controls algorithms so that the onboard controller can make the right decision while approaching the hidden intersection. FEV’s Smart Vehicle Demonstrator was utilized to test the C-V2X setup and the developed algorithms.

The tightening emissions regulations across the globe pose significant challenges to vehicle OEMs. As a result, OEMs are diversifying their powertrain solutions e.g., CNG/Propane based conventional powertrains, BEVs, H2 ICE, FCEV, etc. to meet these regulations. More recently, the ‘CARB Advanced Clean Trucks’ and ‘EPA GHG Phase 3’ regulations are forcing manufacturers to increasingly adopt zero tailpipe emission solutions. While passenger vehicle applications are trending towards a single consensus i.e., BEVs, the heavy-duty on-road applications are challenged with unique requirements of high payload capacity, higher range, lower sales volumes, higher durability, short refueling time, etc. These requirements are driving manufacturers to consider FCEV as an alternative powertrain solution to BEV specifically for higher payload capacity, and range applications.

Autonomous farming has gained a vast interest due to the need for increased farming efficiency and productivity as well as reducing operating cost. Technological advancement enabled the development of Autonomous Driving (AD) features in unstructured environments such as farms. This paper discusses an approach of utilizing satellite images to estimate the drivable areas of agriculture fields with the aid of LiDAR sensor data to provide the necessary information for the vehicle to navigate autonomously. The images are used to detect the field boundaries while the LiDAR sensor detects the obstacles that the vehicle encounters during the autonomous driving as well as its type. These detections are fused with the information from the satellite images to help the path planning and control algorithms in making safe maneuvers. The image and point cloud processing algorithms were developed in MATLAB®/C++ software and implemented within the Robot Operating System (ROS) middleware.

Autonomous vehicles must be able to function safely in complex contexts, involving unpredictable situations and interactions. To ensure this, the system must be tested at various stages as described by the V-model. This process iteratively tests and validates distinct parts of the system, starting with small components to system level assessment. However, this framework presents challenges when adapted to deal with testing problems that face autonomous vehicles. Open road testing is an effective way to expose the system to real world scenarios in combination with specific driving situations described by the Operational Design Domain (ODD). The task of finding a path between two points that maximizes the ODD exposure is not a trivial task, without mentioning that in most cases, the developers must design routes in unfamiliar regions. This represents a significant effort and resources consumption, which makes it important to optimize this task.

Although SAE level 5 autonomous vehicles are not yet commercially available, they will need to be the most intelligent, secure, and safe autonomous vehicles with the highest level of automation. The vehicle will be able to drive itself in all lighting and weather conditions, at all times of the day, on all types of roads and in any traffic scenario. The human intervention in level 5 vehicles will be limited to passenger voice commands, which means level 5 autonomous vehicles need to be safe and capable of recovering fail operational with no intervention from the driver to guarantee the maximum safety for the passengers. In this paper a LiDAR-based fail-safe emergency maneuver system is proposed to be implemented in the level 5 autonomous vehicle.

The Environmental Protection Agency (EPA) and California Air Resources Board (CARB) have recently announced rulemakings focused on tighter emission limits for oxides of nitrogen (NOx) from heavy-duty trucks. As part of the new rulemaking CARB has proposed a Low Load Cycle (LLC) to specifically evaluate NOx emission performance over real-world urban and vocational operation typically characterized by low engine loads, thereby demanding the implementation of continuous active thermal management of the engine and aftertreatment system. This significant drop in NOx levels along with continued reduction in the Green House Gas (GHG) limits poses a more significant challenge for the engine developer as the conventional emission reduction approaches for one species will likely result in an undesirable increase in the other species.

Over the past decade there has been significant development in Automated Driving (AD) with continuous evolution towards higher levels of automation. Higher levels of autonomy increase the vehicle Dynamic Driving Task (DDT) responsibility under certain predefined Operational Design Domains (in SAE level 3, 4) to unlimited ODD (in SAE level 5). The AD system should not only be sophisticated enough to be operable at any given condition but also be reliable and safe. Hence, there is a need for Automated Vehicles (AV) to undergo extensive open road testing to traverse a wide variety of roadway features and challenging real-world scenarios. There is a serious need for accurate Ground Truth (GT) to locate the various roadway features which helps in evaluating the perception performance of the AV at any given condition. The results from open road testing provide a feedback loop to achieve a mature AD system.

As the number of electric vehicles (EVs) within society rapidly increase, the concept of maximizing its efficiency within the electric smart grid becomes crucial. This research presents the impacts of integrating EV charging infrastructures within a smart grid through a vehicle to grid (V2G) program. It also observes the circulation of electric charge within the system so that the electric grid does not become exhausted during peak hours. This paper will cover several different case studies and will analyze the best and worst scenarios for the power losses and voltage profiles in the power distribution system. Specifically, we seek to find the optimal location as well as the ideal number of EVs in the distribution system while minimizing its power losses and optimizing its voltage profile. Verification of the results are primarily conducted using GUIs created on MATLAB.

Today’s advanced vehicles have high degree of interaction due to numerous sensors, actuators and also with complex communication within the control units. In order to hack a vehicle, it has to be within a certain range of communication. Here, we discuss the On-Board Diagnostic (OBD) regulations for next generation BEV/HEV, its vulnerabilities and cybersecurity threats that come with hacking. We propose three cybersecurity attack detection and defense methods: Cyber-Attack detection algorithm, Time-Based CAN Intrusion Detection Method and, Feistel Cipher Block Method. These control methods autonomously diagnose a cybersecurity problem in a vehicle’s onboard system using an OBD interface, such as OBD-II when a fault caused by a cyberattack is detected, All of this is achieved in an internal communication network structure. The results discussed here focus on the first detection method that is Cyber-Attack detection algorithm.

This paper presents experimental investigations of diagnosing and analyzing the low-frequency, low- SNR (Signal to Noise Ratio) noise sources of three motorcycles using a hybrid technology that consists of a passive SODAR (Sonic Detection And Ranging) and modified HELS (Helmholtz Equation Least Squares) methods. The former enables one to determine the precise locations of multiple sound sources in 3D space simultaneously over the entire frequency range that is consistent with a measurement microphone in non-ideal environment, where there are random background noise and unknown interfering signals. The latter enables one to reconstruct all acoustic quantities such as the acoustic pressure, acoustic intensity, time-averaged acoustic power, radiation patterns, and sound transmission paths through arbitrarily shaped vibrating structures.

The automotive industry continues to develop new powertrain and vehicle technologies aimed at reducing overall vehicle-level fuel consumption. Specifically, the use of innovative drivetrain technologies including conventional and electrified propulsion systems is expected to play an increasingly important role in helping OEMs meet fleet CO2 reduction targets for 2025 and beyond. NVH development for vehicles with electrified powertrains introduces new challenges, which need to be understood and solved. The electrified vehicle space spans variants from micro and mild hybrids all the way through plug-in hybrids and fully electric vehicles. In addition to conventional NVH development methodologies, active sound design (ASD) can play a crucial role to enhance the interior sound perception of such vehicles and hence, improve customer acceptance of new technologies. This paper will begin with an introduction to the NVH challenges posed by electrified vehicles.

Increased levels of autonomy requires an increasing amount of sensory data for the vehicle to make appropriate decisions. Sensors like camera, Lidar and Radar will help to perceive the surroundings, exposing nearby objects and blind spots within the line of sight. With V2X connectivity, vehicles communicate to other vehicles and infrastructure using wireless communications even in non-line of sight conditions. This paper presents an approach to utilize a V2X system to develop Automated Driving (AD) features in a Robot Operating System (ROS) environment. This was achieved by developing ROS drivers and creating custom messages to enable the communication between the V2X system and vehicle sensors. The developed algorithms were tested on FEV’s Smart Vehicle Demonstrator. The test results show that the proposed V2X automated driving approach has increased reliability compared to that of camera, Radar and Lidar based autonomous driving.

Electrification of heavy-duty trucks has received significant attention in the past year as a result of future regulations in some states. For example, California will require a certain percentage of tractor trailers, delivery trucks and vans sold to be zero emission by 2035. However, the relatively low energy density of batteries in comparison to diesel fuel, as well as the operating profiles of heavy-duty trucks, make the application of electrified powertrain in these applications more challenging. Heavy-duty vehicles can be broadly classified into two main categories; long-haul tractors and vocational vehicles. Long-haul tractors offer limited benefit from electrification due to the majority of operation occurring at constant cruise speeds, long range requirements and the high efficiency provided by the diesel engine.

The adoption of lithium-ion batteries in vehicle electrification is fast growing due to high power and energy demand on hybrid and electric vehicles. However, the battery overall performance changes with time through the vehicle life. This paper investigates the electric vehicle battery cell aging under different usages. Battery cell experimental data including open circuit voltage and internal resistance is utilized to build a typical electric vehicle model in the AVL-Cruise platform. Four driving cycles (WLTP, UDDS, HWFET, and US06) with different ambient temperatures are simulated to acquire the battery cell terminal currents. These battery cell terminal current data are inputs to the MATLAB/Simulink battery aging model. Simulation results show that battery degrades quickly in high ambient temperatures. After 15,000 hours usage in 50 degrees Celsius ambient temperature, the usable cell capacity is reduced up to 25%.

Electrification of heavy-duty trucks has received significant attention in the past year as a result of future regulations in some states. For example, California will require a certain percentage of tractor trailers, delivery trucks and vans sold to be zero emission by 2035. However, the relatively low energy density of batteries in comparison to diesel fuel, as well as the operating profiles of heavy-duty trucks, make the application of electrified powertrain in these applications more challenging. Heavy-duty vehicles can be broadly classified into two main categories; long-haul tractors and vocational vehicles. Long-haul tractors offer limited benefit from electrification due to the majority of operation occurring at constant cruise speeds, long range requirements and the high efficiency provided by the diesel engine.

Implementing Advanced Driver Assistance Systems (ADAS) features that are available in all road scenarios and weather conditions is a big challenge for automotive companies and considered key enablers to achieve autonomous Level 4 (L4) vehicles. One important feature is the Lane Keep Assist System (LKAS). Most LKAS systems are based on lane line detection cameras and lane coefficient estimations by the camera is the key point for LKAS where the camera recognizes the lane lines using edge detection. But when the lane markers are not available due to high traffic and slow driving on the roads, another source of data for the lane lines needs to be available for the LKAS. In this paper a multi-sensor fusion approach based on camera, Lidar, and GPS is used to allow the vehicle to maintain its lateral location within the lane.

Lithium-ion polymer battery has been widely used for vehicle onboard electric energy storage ranging from 12V SLI (Starting, Lighting, and Ignition), 48V mild hybrid electric, to 300V battery electric vehicle. Formulation on cell parameters acquired from minimum numbers of experiments, the modeling and simulation could be an effective approach in predicting battery performance, thermal effectiveness, and degradation. This paper describes the modeling, simulation, and validation of Lithium-Nickel-Manganese-Cobalt-Oxide (LiNiMnCoO2) based cell with 3.6V nominal voltage and 20Ah capacity. Constant current 20A, 40A, 60A, and 80A discharge tests are conducted in the computer-controlled cycler and temperature chamber. Discharging voltage curves and cell surface temperature distributions are recorded in each discharging test. A three-dimensional cell model is constructed in the COMSOL multi-physics platform based on the cell parameters.

With the mass movement toward electrification and renewable technologies, the scope of innovation of electrification has gone beyond the automotive industry into areas such as electric motorcycle applications. This paper provides a discussion of the methodology and complexities of converting an internal combustion motorcycle to an electric motorcycle. In developing this methodology, performance goals including, speed limits, range, weight, charge times, as well as riding styles will be examined and discussed. Based on the goals of this paper, parts capable of reaching the performance targets are selected accordingly. Documentation of the build process will be presented along with the constraints, pitfalls, and difficulties associated with the process of the project. The step-by-step process that is developed can be used as a guideline for future build and should be used as necessary.

Automated driving functionalities delivered through Advanced Driver Assistance System (ADAS) have been adopted more and more frequently in consumer vehicles. The development and implementation of such functionalities pose new challenges in safety and functional testing and the associated validations, due primarily to their high demands on facility and infrastructure. This paper presents a rather unique Vehicle-in-the-Loop (VIL) test setup and methodology compared those previously reported, by combining the advantages of the hardware-in-the-loop (HIL) and traditional chassis dynamometer test cell in place of on-road testing, with a multi-agent real-time simulator for the rest of test environment.