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The Particle Number–Portable Emission Measurement System (PN-PEMS) came into force with Euro VI Phase E regulations starting January 1, 2022. However, positive ignition (PI) engines must comply from January 1, 2024. The delay was due to the unavailability of the PN-PEMS system that could withstand high concentrations of water typically present in the tailpipe (TP) of CNG vehicles, which was detrimental to the PN-PEMS systems. Thus, this study was designed to evaluate the condensation particle counter (CPC)-based PN-PEMS measurement capabilities that was upgraded to endure high concentration of water. The PN-PEMS measurement of solid particle number (SPN23) greater than 23 nm was compared against the laboratory-grade PN systems in four phases. Each phase differs based upon the PN-PEMS and PN system location and measurements were made from three different CNG engines. In the first phase, systems measured the diluted exhaust through constant volume sampler (CVS) tunnel.

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.

This article analyzes the aerodynamic performance of Class 8 tractor-trailer geometries made available by the Environmental Protection Agency (EPA) using CFD simulation. Large Eddy Simulations (LES) were carried out with the CFD package, Simerics-MP+. A Sleeper tractor and a 53-foot box trailer configuration was considered. The configuration featured a detailed underbody, an open-grille under-hood engine compartment, mirrors, and the radiator and condenser. Multiple tractor-trailer variants were studied by adding aerodynamic surfaces to the baseline geometries. These include tank fairings and side extenders for the cabins, two types of trailer skirts, and a trailer tail. The effect of these devices towards reducing the overall vehicle drag was investigated. Mesh generation was carried out directly on the given geometry, without any surface modifications, using Simerics’ Binary-Tree unstructured mesher.

Shafaat and Kenley in 2015 identified the opportunity to improve System Engineering Standards by incorporating the design principle of learning. The System Level Assessment (SLA) Methodology is an approach that fulfills this need by efficiently capturing the learnings of a team of subject matter experts in the early stages of product system design. By gathering expertise, design considerations are identified that when used with market and business requirements improve the overall quality of the product system. To evaluate the effectiveness of this approach, the methodology has been successfully applied over 400 times within each realm of the New Product Introduction process, including most recently to a Technology Development program (in the earliest stages of the design process) to assess the viability of various electrification technologies under consideration by an automotive Tier 1 supplier.

The lack of inherent security controls makes traditional Controller Area Network (CAN) buses vulnerable to Machine-In-The-Middle (MitM) cybersecurity attacks. Conventional vehicular MitM attacks involve tampering with the hardware to directly manipulate CAN bus traffic. We show, however, that MitM attacks can be realized without direct tampering of any CAN hardware. Our demonstration leverages how diagnostic applications based on RP1210 are vulnerable to Machine-In-The-Middle attacks. Test results show SAE J1939 communications, including single frame and multi-framed broadcast and on-request messages, are susceptible to data manipulation attacks where a shim DLL is used as a Machine-In-The-Middle. The demonstration shows these attacks can manipulate data that may mislead vehicle operators into taking the wrong actions.

Safety, fuel economy and uptime are key requirements for the operation of heavy-duty line-haul trucks within a fleet. With the penetration of connectivity and automation technologies, energy optimal and safe operation of the trucks are further improved through Advanced Driver Assistance System (ADAS) features and automated technologies as in truck platooning. Understanding the braking capability of the vehicle is very important for optimal ADAS and platooning control system design and integration. In this paper, the importance of tire connectivity and tire conditions on truck stopping distance are demonstrated through testing. The test data is further utilized to develop tire models for integration in an optimal vehicle automation for platooning. New ways to produce and use the tire related information in real-time optimal control of platooning trucks are proposed and the contribution of tire information in fuel economy is quantified through simulations.

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.

The application of cooperative adaptive cruise control (CACC) to heavy-duty trucks known as truck platooning has shown fuel economy improvements over test track ideal driving conditions. However, there are limited test data available to assess the performance of CACC under real-world driving conditions. As part of the Cummins-led U.S. Department of Energy Funding Opportunity Announcement award project, truck platooning with CACC has been tested under real-world driving conditions and the results are presented in this paper. First, real-world driving conditions are characterized with the National Renewable Energy Laboratory’s Fleet DNA database to define the test factors. The key test factors impacting long-haul truck fuel economy were identified as terrain and highway traffic with and without advanced driver-assistance systems (ADAS).

Evaluation of the platooning legislative space suggests a limited near-term opportunity for autonomous vehicles as currently only nine states have platooning and autonomous favorable legislations. An extensive closed-loop vehicle model simulation was conducted to quantify two-truck platooning fuel economy entitlement benefits across all United States (US) interstate routes (I-xx) spanning over 40,000 miles as compared to a single truck. A simultaneous study was carried out to identify the density of Class 8 heavy-duty trucks on these interstates, using the Freight Analysis Framework (FAF) 4 database. These two studies were combined to ascertain interstates that foresee the least fuel consumption due to platooning and thus identifying states with the most platooning benefits. Identification of states with most platooning benefits provides realistic data to push for autonomous driving and platooning legislations.

The present study examines the impact of using low thermal mass (LTM) turbine housing designs on the transient characteristics of the turbine outlet temperature for a light-duty diesel standard certification cycle (FTP75). For a controlled exhaust flow, the turbine outlet temperature will directly determine the impact on an aftertreatment system warm-up from a cold state, typical of engine-off and engine idling conditions. The performance of the aftertreatment system such as a Selective Catalytic Reduction (SCR) system is highly dependent on how quickly it warms up to its desirable temperature to be able to convert the harmful oxides of Nitrogen (NOx) to gaseous Nitrogen. Previous works have focused on mostly insulating the exhaust manifold and turbine housing to conserve the heat going into the aftertreatment system. The use of LTM turbine housing has not been previously considered as a means for addressing this requirement.

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.

This paper investigates an optimal design of a diesel engine and after-treatment systems for a series hybrid electric forklift application. A holistic modeling approach is developed in GT-Suite® to establish a model-based hardware definition for a diesel engine and an after-treatment system to accurately predict engine performance and emissions. The used engine model is validated with the experimental data. The engine design parameters including compression ratio, boost level, air-fuel ratio (AFR), injection timing, and injection pressure are optimized at a single operating point for the series hybrid electric vehicle, together with the performance of the after-treatment components. The engine and after-treatment models are then coupled with a series hybrid electric powertrain to evaluate the performance of the forklift in the standard VDI 2198 drive cycle.

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.

Autonomous vehicle operation is dependent upon accurate position estimation and thus a major concern of implementing the autonomous navigation is obtaining robust and accurate data from sensors. This is especially true, in case of Inertial Measurement Unit (IMU) sensor data. The IMU consists of a 3-axis gyro, 3-axis accelerometer, and 3-axis magnetometer. The IMU provides vehicle orientation in 3D space in terms of yaw, roll and pitch. Out of which, yaw is a major parameter to control the ground vehicle’s lateral position during navigation. The accelerometer is responsible for attitude (roll-pitch) estimates and magnetometer is responsible for yaw estimates. However, the magnetometer is prone to environmental magnetic disturbances which induce errors in the measurement.

While significant progress has been made in recent years to develop hybrid and battery electric vehicles for passenger car and light-duty applications to meet future fuel economy targets, the application of hybrid powertrains to heavy-duty truck applications has been very limited. The relatively lower energy and power density of batteries in comparison to diesel fuel and the operating profiles of most heavy-duty trucks, combine to make the application of hybrid powertrain for these applications more challenging. The high torque and power requirements of heavy-duty trucks over a long operating range, the majority of which is at constant cruise point, along with a high payback period, complexity, cost, weight and range anxiety, make the hybrid and battery electric solution less attractive than a conventional powertrain.