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The AutoDrive Challenge competition sponsored by General Motors and SAE gives undergraduate and graduate students an opportunity to get hands-on experience with autonomous vehicle technology and development as they work towards their degree. Michigan Technological University has participated in the AutoDrive Challenge since its inception in 2017 with students participating through MTU’s Robotic System Enterprise. The MathWorks Simulation Challenge has been a component of the competition since its second year, tasking students with the development of perception, control and testing algorithms using MathWorks software products. This paper presents the pedagogical approach graduate student mentors used to enable students to build their understanding of autonomous vehicle concepts using familiar tools. This approach gives undergraduate students a productive experience with these systems that they may not have encountered in coursework within their academic program.

The piezoelectric cylinder pressure transducer is one of the most critical tools for internal combustion (IC) engine research and development. However, not all cylinder pressure transducers perform equally in every application, and the fidelity of transducers can vary across different models and manufacturers. Even slightly dissimilar models from the same manufacturer can have significantly different performance in areas such as sensitivity and resistance to intra-cycle thermal shock. These performance differences can lead to errors and inconsistencies in the calculation of combustion metrics like mean effective pressure (MEP), the polytropic compression and expansion exponents (PolyC and PolyE), and mass fraction burn (MFB) calculations. The variations can lead to suboptimal hardware and calibration choices during the engine development phase.

Robust autonomous navigation in unstructured environments is an unsolved problem and critical to the operation of autonomous military and rescue ground vehicles. Two-dimensional path planners operating on occupancy grids or costs maps can produce infeasible paths when the operational area includes complex terrain. Recently, sample-based path planners that plan on LiDAR-acquired point-cloud maps have been proposed. These approaches require no discretization of the operational area and provide direct pose estimation by modeling vehicle and terrain interaction. In this paper, we show that direct sample-based path planning on point clouds is effective and robust in unstructured environments. Robustness is demonstrated by completing a system parameter sensitivity analysis of the system in an Unreal simulation environment and partnered with field validation.

Off-road vehicle operation requires constant decision-making under great uncertainty. Such decisions are multi-faceted and range from acquisition decisions to operational decisions. A major input to these decisions is terrain information in the form of soil properties. This information needs to be propagated to path planning algorithms that augment them with other inputs such as visual terrain assessment and other sensors. In this sequence of steps, many resources are needed, and it is not often clear how best to utilize them. We present an integrated approach where a mission’s overall performance is measured using a multiattribute utility function. This framework allows us to evaluate the value of acquiring terrain information and then its use in path planning. The computational effort of optimizing the vehicle path is also considered and optimized. We present our approach using the data acquired from the Keweenaw Research Center terrains and present some results.

The study of spray-wall interaction is of great importance to understand the dynamics during fuel-surface impingement process in modern internal combustion engines. The identification of droplet post-impingement pattern (contact, transition, non-contact) and droplet characteristics can quantitatively provide an estimation of energy transfer for spray-wall interaction, thus further influencing air-fuel mixing and emissions under combusting conditions. Theoretical criteria of single droplet post-impingement pattern on a dry wall have been experimentally and numerically studied by many researchers to quantify the hydrodynamic droplet behaviors. However, apart from model fidelity, another issue is the scalability. A theoretical criterion developed from one case might not be well suited to another scenario. In this paper, a data-driven approach for single droplet-dry wall post-impingement pattern utilizing arithmetical machine learning classification methods is proposed and demonstrated.

The advancement in embedded systems and positional accuracy with base station GPS modules created opportunity to develop high performance autonomous ground vehicles. However, the development of vehicle model and making accurate state estimations play vital role in reducing the cross track error. The present research focus on developing Linear Quadratic Gaussian (LQG) with Kalman estimator for autonomous ground vehicle to track various routes, that are made with the series of waypoints. The model developed in the LQG controller is a kinematic bicycle model, which mimics 1/5th scale truck. Further, the cubic spline fit has been used to connect the waypoints and generate the continuous desired/target path. The testing and implementation has been done at APS labs, MTU on the mentioned vehicle to study the performance of controller. Python has been used for simulations, controller coding and interfacing the sensors with controller.

Off road navigation demands ground robots to traverse complex and often changing terrain. Classification and assessment of terrain can improve path planning strategies by reducing travel time and energy consumption. In this paper we introduce a terrain classification and assessment framework that relies on both exteroceptive and proprioceptive sensor modalities. The robot captures an image of the terrain it is about to traverse and records corresponding vibration data during traversal. These images are manually labelled and used to train a support vector machine (SVM) in an offline training phase. Images have been captured under different lighting conditions and across multiple locations to achieve diversity and robustness to the model. Acceleration data is used to calculate statistical features that capture the roughness of the terrain whereas angular velocities are used to calculate roll and pitch angles experienced by the robot.

Over the past decade, there have been many efforts to generate engine sound inside the cabin either in reducing way or in enhancing way. To reduce the engine noise, the passive way, such as sound absorption or sound insulation, was widely used but it has a limitation on its reduction performance. In recent days, with the development of signal processing technology, ANC (Active Noise Control) is been used to reduce the engine noise inside the cabin. On the other hand, technologies such as ASD (Active Sound Design) and ESG (Engine Sound Generator) have been used to generate the engine sound inside the vehicle. In the last ISNVH, Hyundai Motor Company newly introduced ESEV (Engine Sound by Engine Vibration) technology. This paper describes the ESEV Plus Minus that uses engine vibration to not only enhance the certain engine order components but reduce the other components at the same time. Consequently, this technology would produce a much more diverse engine sound.

Battery temperature variations have a strong effect on both battery aging and battery performance. Significant temperature variations will lead to different battery behaviors. This influences the performance of the Hybrid Electric Vehicle (HEV) energy management strategies. This paper investigates how variations in battery temperature will affect Lithium-ion battery aging and fuel economy of a HEV. The investigated energy management strategy used in this paper is the Equivalent Consumption Minimization Strategy (ECMS) which is a well-known energy management strategy for HEVs. The studied vehicle is a Honda Civic Hybrid and the studied battery, a BLS LiFePO4 3.2Volts 100Ah Electric Vehicle battery cell. Vehicle simulations were done with a validated vehicle model using multiple combinations of highway and city drive cycles. The battery temperature variation is studied with regards to outside air temperature.

In order to extend the operability limit of the gasoline compression ignition (GCI) engine, as an avenue for low temperature combustion (LTC) regime, the effects of parametric variations of engine operating conditions on the performance of six-stroke GCI (6S-GCI) engine cycle are numerically investigated, using an in-house 3D CFD code coupled with high-fidelity physical sub-models along with the Chemkin library. The combustion and emissions were calculated using a skeletal chemical kinetics mechanism for a 14-component gasoline surrogate fuel. Authors’ previous study highlighted the effects of the variation of injection timing and split ratio on the overall performance of 6S-GCI engine and the unique mixing-controlled burning mode of the charge mixtures during the two additional strokes. As a continuing effort, the present study details the parametric studies of initial gas temperature, boost pressure, fuel injection pressure, compression ratio, and EGR ratio.

Wireless Power Transfer (WPT) promises automated and highly efficient charging of electric and plug-in-hybrid vehicles. As commercial development proceeds forward, the technical challenges of efficiency, interoperability, interference and safety are a primary focus for this industry. The SAE Vehicle Wireless Power and Alignment Taskforce published the Recommended Practice J2954 to help harmonize the first phase of high-power WPT technology development. SAE J2954 uses a performance-based approach to standardizing WPT by specifying ground and vehicle assembly coils to be used in a test stand (per Z-class) to validate performance, interoperability and safety. The main goal of this SAE J2954 bench testing campaign was to prove interoperability between WPT systems utilizing different coil magnetic topologies. This type of testing had not been done before on such a scale with real automaker and supplier systems.

Automotive companies are trying to enhance the customer’s impression by improving engine sound quality. The target of this sound quality is to create a brand sound that is preferred by their customers as well as quietness of interior noise. Over the past decade there have been many studies in the field of automotive sound quality. These have included the technologies such as tuning of intake orifice and exhaust orifice, tuning of structure-borne, intake feedback devices, active exhaust valves, ANC (Active Noise Cancellation) and ASD (Active Sound Design). The three elements of the sound that affect the feeling of the customer are known as engine order arrangement, frequency balance, and linearity. Here, the most important thing in sound quality development is the order arrangement.

Passengers sitting on the back seats of cars while talking on their mobiles can easily experience the invasion of their speech privacy by the driver. Protecting speech privacy can be done by utilizing masking sounds - masking sound may be so loud that it annoys both drivers and speakers. In this research, the feasibility of utilizing active noise control (ANC) which aims to reduce the level of speech at the driver’s seat and, hence, is able to lower the needed level of masking sounds while still protecting the speech privacy is investigated. Speech reception threshold (SRT), which is a subjective measurement method for speech intelligibility, is proceeded for seeing the effect of ANC on speech intelligibility when the masking sound is in use. The SRT measurement result implied that utilizing ANC to reduce the speech level of the back-seat passenger at the driver’s seat is able to lower the needed level of masking sound for keeping the speech privacy.

The characteristics of combustion knock metrics over a number of engine cycles can be an essential reference for knock detection and control in internal combustion engines. In a Spark-Ignition (SI) engine, the stochastic nature of combustion knock has been shown to follow a log-normal distribution. However, this has been derived from experiments done with gasoline only and applicability of log-normal distribution to dual-fuel combustion knock has not been explored. To evaluate the effectiveness and accuracy of log-normal distributed knock model for methane-gasoline blended fuel, a sweep of methane-gasoline blend ratio was conducted at two different engine speeds. Experimental investigation was conducted on a single cylinder prototype SI engine equipped with two fuel systems: a direct injection (DI) system for gasoline and a port fuel injection (PFI) system for methane.

This paper aims to provide the experimental and numerical investigation of a single fuel droplet impingement on the different wall conditions to understand the detailed impinging dynamic process. The experimental work was carried out at the room temperature and pressure except for the variation of the impinged wall temperature. A high-speed camera was employed to capture the silhouette of the droplet impinging on wall process against a collimated light. Water, diesel, n-dodecane, and n-heptane were considered as four different droplets and injected from a precision syringe pump with the volume flow rate of 0.2 mL/min at various impact Weber numbers. The impingement outcomes after droplet impacting on the wall include stick, spread, rebound and splash, which depend on the controlling parameters of Weber number, Reynolds number, liquid and surface properties, etc.

Battery State of Charge (SOC) constraints are used to prevent the battery in Hybrid Electric Vehicles (HEVs) from over-charging or over-discharging. These constraints strongly influence the power-split of the HEV. This paper presents results on how Battery State of Charge (SOC) constraints effects Lithium ion battery aging and fuel economy when using the Equivalent Consumption Minimization Strategy (ECMS). The vehicle studied is the Honda Civic Hybrid. The battery used is A123 Systems’ ANR26650 battery cell. Vehicle simulation uses multiple combinations of highway and city drive cycles. For each combination of drive cycles, nine SOC constraints ranges are used. Battery aging is evaluated using a semi-empirical model combined with the accumulated Ah-throughput method which uses, as an input, the battery SOC trajectory from the vehicle simulations. The simulation results provide insight into how SOC constraints effect fuel economy as well as battery aging.

Operator training using a weapon in a real-world environment is risky, expensive, time-consuming, and restricted to the given environment. In addition, governments are under intense scrutiny to provide security, yet they must also strive for efficiency and reduce spending. In other words, they must do more with less. Virtual simulation, is usually employed to solve these limitations. As the operator is trained to maximize weapon effectiveness, the effectiveness-focused training can be completed in an economical manner. Unfortunately, the training is completed in limited scenarios without objective levels of training factors for an individual operator to optimize the weapon effectiveness. Thus, the training will not be effective. For overcoming this problem, we suggest a methodology on guiding effectiveness-focused training of the weapon operator through usability assessments, big data, and Virtual and Constructive (VC) simulations.

Carbon Nanotube (CNT) thin film speakers produce sound with the thermoacoustic effect. Alternating current passes through the low heat capacity CNT thin film changing the surface temperature rapidly. CNT thin film does not vibrate; instead it heats and cools the air adjacent to the film, creating sound pressure waves. These speakers are inexpensive, transparent, stretchable, flexible, magnet-free, and lightweight. Because of their novelty, developing a model and better understanding the performance of CNT speakers is useful in technology development in applications that require ultra-lightweight sub-systems. The automotive industry is a prime example of where these speakers can be enabling technology for innovative new component design. Developing a multi-physics (Electrical-Thermal-Acoustical) FEA model, for planar CNT speakers is studied in this paper. The temperature variation on the CNT thin film is obtained by applying alternating electrical current to the CNT film.

To address the need of increasing fuel economy requirements, automotive Original Equipment Manufacturers (OEMs) are increasing the number of turbocharged engines in their powertrain line-ups. The turbine-driven technology uses a forced induction device, which increases engine performance by increasing the density of the air charge being drawn into the cylinder. Denser air allows more fuel to be introduced into the combustion chamber, thus increasing engine performance. During the inlet air compression process, the air is heated to temperatures that can result in pre-ignition resulting and reduced engine functionality. The introduction of the charge air cooler (CAC) is therefore, necessary to extract heat created during the compression process. The present research describes the physics and develops the optimized simulation method that defines the process and gives insight into the development of CACs.

Around the world, the major automakers are developing their strategies for conductive and wireless charging technologies, with concerted efforts to establish technical standards on wireless electric vehicle charging, mainly focused on the safety considerations and inter-operability. Wireless Charging Technology and the Future of Electric Transportation covers the current status of wireless power transfer (WPT) technology and its potential applications to the future road and rail transportation systems. Focusing on the applications of WPT technology to electric vehicle charging and the future green transportation field, Wireless Charging Technology and the Future of Electric Transportation was written collaboratively by nine experts in the field, led by Dr. In-Soo Suh, a professor and researcher from the Korean Advanced Institute of Technology (KAIST).