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In order to improve the fuel economy for future high-efficiency spark ignition engines, the applications of advanced combustion strategies are considered to be beneficial with an overall lean and/or exhaust gas recirculation diluted cylinder charge. Stronger and more reliable ignition sources become more favorable under extreme lean/EGR conditions. Existing research indicates that the frequency of plasma restrikes increases with increased flow velocity and decreased discharge current level, and a higher discharge current can reduce the gap resistance and maintain the stretched plasma for a longer duration under flow conditions. An in-house developed current boost control system provides flexible control of the discharge current level and discharge duration. The current boost ignition system is based on a multi-coil system with a discharge current level of 180mA.

Lithium Ion (Li-ion) batteries have emerged as the dominant technology for electric mobility due to their performance, stability, and long cycle life. Nevertheless, there are emerging environmental and economic issues from Li-ion batteries related to depleting critical resources and their potential shortage. This paper focuses on developing the Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) of a generic Li-ion battery pack with a Nickel-Manganese-Cobalt (NMC) cathode chemistry, being the most used, and a capacity of 95 kWh as an average between different carmakers. The LCA and LCC include all the relevant phases of the life cycle of the product. The costs related to the LCC assessment have been taken as secondary data. Lastly, the same system boundary has been chosen both for the LCA and LCC.

Alumina (Al2O3) thin film coatings are applied on Al alloys using Plasma Electrolytic Oxidation (PEO) method to reduce the wear and corrosion problems. Plasma Electrolytic Aluminating (PEA) is a technique which could generate Alumina coatings on cast iron, mild steel and copper alloys. In this study, the aim is to explore the anti-wear and anti-corrosion behaviours of PEA Alumina coatings on gray cast iron. The dry sliding tribology test data was obtained from Pin-on-Disk (POD) tests against SAE 52100 steel and Tungsten Carbide (WC) counterfaces. Comparing with the PEO Alumina coatings, the PEA Alumina coating has much lower Coefficient of Friction (COF) and less wear. The microstructure, chemical composition and phase composition of this coating were investigated with Scanning Electron Microscope (SEM), Energy-Dispersive X-Ray Spectroscopy (EDX) and X-Ray Diffraction (XRD), respectively. There was FeO (or FeAl2O4) found on the PEA Alumina coating.

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.

Performance of the ignition system becomes more important than ever, because of the extensively used EGR in modern spark-ignition engines. Future lean burn SI and SACI combustion modes demand even stronger ignition capability for robust ignition control. For spark-based ignition systems, extensive research has been carried out to investigate the discharge characteristics of the ignition process, including discharge current amplitude, discharge duration, spark energy, and plasma stretching. The correlation between the spark stretch and the discharge energy, as well as the impact of discharge current level on this correlation, are important with respect to both ignition performance, and ignition system design. In this paper, a constant volume combustion chamber is applied to study the impact of plasma stretch on the spark energy release process with cross-flow speed from 0 m/s up to 70 m/s.

As vehicles are getting electrified and more intelligent, the energy consumption of the auxiliary system increases rapidly. The auxiliary battery acts as the backbone of the system to support the proper operation of the vehicle. It is important to ensure the auxiliary battery has enough energy to meet the basic loads regardless the vehicle is in park or running. However, the existing methods only focus on auxiliary energy management when the vehicle is in a dynamic event. To fulfill the gap, we propose an intelligent strategy that detects the low state of charge (SOC) condition, temporarily turns down the auxiliary loads based on their priorities and charges the auxiliary battery at the maximum efficiency of the auxiliary power unit. In addition, the proposed strategy allows the vehicle to get the park duration update and make intelligent decisions on charging the auxiliary battery.

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 determining and analyzing low-frequency, low-SNR (Signal to Noise Ratio) noise sources of an automobile by using a new technology known as Sound Viewer. Such a task is typically very difficult to do especially at low or even negative SNR. The underlying principles behind the Sound Viewer technology consists of a passive SODAR (Sonic Detection And Ranging) and HELS (Helmholtz Equation Least Squares) method. The former enables one to determine the precise locations of multiple sound sources in 3D space simultaneously over the entire frequency range 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, etc.

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.

Automotive Open System Architecture (AUTOSAR) is a system-level standard that is formed by the worldwide partnership of automotive manufacturers and suppliers who are working together to develop a standardized Electrical and Electronic (E/E) framework and architecture for automobiles. The AUTOSAR methodology has two main activities: system configuration and the Electronic Control Unit (ECU) configuration. The system configuration is the mapping of the software components to the ECUs based on the system requirements. The ECU configuration (EC) process is an important part of the ECU software integration and generation. ECU-specific information is extracted from the system configuration description, and all the necessary information for the implementation such as tasks, scheduling, and assignments of the runnables to tasks and configuration of the Basic Software (BSW) modules are performed. The EC process involves configuring every single module of the AUTOSAR.

Major original equipment manufacturers (OEMs) have already marketed electric vehicles in large scale but apart from business strategies and policies, the real engineering problems must be addressed. Lithium-ion batteries are a promising technology for energy storage; however, their low energy density and complex electro-chemical nature, compared to fossil fuels, presents additional challenges. Their complex nature and strong temperature dependence during operation must be studied with additional accuracy, capable to predict their behavior. In this research, a pseudo two dimensional (P2D) electro-chemical model, for a recent high capacity NMC pouch cell for automotive applications is developed. The electrochemical model with its temperature dependent parameters is validated at high, low, and reference temperature within 10°C to 50°C temperature range. For each temperature various discharge C-rates to accurately replicate the battery cell operational conditions.

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%.

Designing a vehicle for superior crash safety performance in consumer rating tests such as US-NCAP is a compelling target in the design of passenger vehicles. In today’s context, there is also a high emphasis on making a vehicle as lightweight as possible which calls for an efficient design. In modern vehicle design, these objectives can only be achieved through Computer-Aided Engineering (CAE) for which a detailed CAD (Computer-Aided Design) model of a vehicle is a pre-requisite. In the absence of the latter (i.e. a matured CAD model) at the initial and perhaps the most crucial phase of vehicle body design, a rational approach to design would be to resort to a knowledge-based methodology which can enable crash safety assessment of an assumed design using artificial intelligence techniques such as neural networks.

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.

Automotive Open System Architecture (AUTOSAR) is a system-level standard that is formed by the worldwide partnership of the automotive manufacturers and suppliers who are working together to develop a standardized Electrical and Electronic (E/E) framework and architecture for automobiles. The AUTOSAR methodology has two main activities: system configuration and the Electronic Control Unit (ECU) configuration. The system configuration is the mapping of the software components to the ECUs based on the system requirements. The ECU configuration process is an important part of the ECU software integration and generation. ECU specific information is extracted from the system configuration description and all the necessary information for the implementation such as tasks, scheduling, assignments of the runnables to tasks and configuration of the Basic Software (BSW) modules, are performed. The ECU configuration process involves configuring every single module of the AUTOSAR architecture.

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.

The Hotelling multivariate control chart and the sample generalized variance |S| are used to monitor the mean and dispersion of vehicle build vision data including the pallet information to identify the non-conforming pallets that are used in body shops of FCA US LLC assembly plants. An iterative procedure and the Gaussian mixture model (GMM) are used to rank the non-conforming or bad pallets in the order of severity. The Hotelling multivariate T2 test statistic along with Mason-Tracy-Young (MYT) signal decomposition method is used to identify the features that are affected by the bad pallets. These algorithms were implemented in the Advanced Pallet Analysis module of the FCA US software Body Shop Analysis Toolbox (BSAT). The identified bad pallets are visualized in a scatter plot with a different color for each of the top bad pallets. The run chart of an affected feature confirms the bad pallet by highlighting data points from the bad pallet.

Autonomous vehicles are currently a subject of great interest and there is heavy research on creating and improving algorithms for detecting objects in their vicinity. A ROS-based deep learning approach has been developed to detect objects using point cloud data. With encoded raw light detection and ranging (LiDAR) and camera data, several basic statistics such as elevation and density are generated. The system leverages a simple and fast convolutional neural network (CNN) solution for object identification and localization classification and generation of a bounding box to detect vehicles, pedestrians and cyclists was developed. The system is implemented on an Nvidia Jetson TX2 embedded computing platform, the classification and location of the objects are determined by the neural network. Coordinates and other properties of the object are published on to various ROS topics which are then serviced by visualization and data handling routines.

For internal combustion engine systems, lean and diluted combustion is an important technology applied for fuel efficiency improvement. Because of the thermodynamic boundary conditions and the presence of in-cylinder flow, the development of a well-sustained flame kernel for lean combustion is a challenging task. Reliable spark discharge with the addition of enhanced delivered energy is thus needed at certain time durations to achieve successful combustion initiation of the lean air-fuel mixture. For a conventional transistor coil ignition system, only limited amount of energy is stored in the ignition coil. Therefore, both the energy of the spark discharge and the duration of the spark discharge are bounded. To break through the energy limit of the conventional transistor coil ignition system, in this work, an adaptive spark ignition system is introduced. The system has the ability to reconstruct the conductive ion channels whenever it is interrupted during the spark discharge.