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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.

This paper presents a methodology to optimize the blending of charge-depleting (CD) and charge-sustaining (CS) modes in a multi-mode plug-in hybrid electric vehicle (PHEV). The objective of the optimization is to best utilize onboard energy for minimum overall energy consumption based on speed and elevation profile. The optimization reduces overall energy consumption when the selected route cannot be completely driven in all-electric mode. The optimization method splits drive cycles into constant distance segments and then uses a reduced-order model to sort the segments by the best use of battery energy vs. fuel energy. The PHEV used in this investigation is the Stellantis Pacifica. Results support energy savings up to 20% which depend on the route and initial battery State of Charge (SOC). Initial optimization takes 1 second for 38 km and 3 seconds for 154 km.

This paper presents a methodology that optimizes the periods of engine operation on a selected route for a Plug-in Hybrid Electric Vehicle (PHEV) or Hybrid Electric Vehicle (HEV) using Connected Vehicle data to minimize energy consumption. The study was conducted using a Reduced-Order Powertrain model of second-generation Chevrolet Volt. The method utilizes the Backward Induction Dynamic Programming algorithm to come up with an optimal control mode matrix of engine operation along the selected route for various battery states of charge. The objective of this method is to make use of Vehicle Connectivity to minimize the energy utilization of an HEV by using the speed and elevation profile of a selected route transmitted to the vehicle via V2X communication systems.

Improving vehicle response through advanced knowledge of traffic behavior can lead to large improvements in energy consumption for the single isolated vehicle. This energy savings across multiple vehicles can even be larger if they travel together as a cohort in harmonization. Additionally, if the vehicles have enough information about their immediate path of travel, and other vehicles’ in that path (and their respective critical forward-looking information), they can safely drive close enough to each other to share aerodynamic load. These energy savings can be upwards of multiple percentage points, and are dependent on several criteria. This analysis looks at criteria that contributes to energy savings for a cohort of vehicles in synchronous motion, as well as describes a study that allows for better understanding of the potential benefits of different types of cohorted vehicles in different platoon arrangements.

This paper presents an overview of the connected controls and optimization system for vehicle dynamics and powertrain operation on a light-duty plug-in multi-mode hybrid electric vehicle developed as part of the DOE ARPA-E NEXTCAR program by Michigan Technological University in partnership with General Motors Co. The objective is to enable a 20% reduction in overall energy consumption and a 6% increase in electric vehicle range of a plug-in hybrid electric vehicle through the utilization of connected and automated vehicle technologies. Technologies developed to achieve this goal were developed in two categories, the vehicle control level and the powertrain control level. Tools at the vehicle control level include Eco Routing, Speed Harmonization, Eco Approach and Departure and in-situ vehicle parameter characterization.

The proliferation of small silicon micro-chips has led to a large assortment of low-cost transducers for data acquisition. Production vehicles on average exploit more than 60 on board sensors, and that number is projected to increase beyond 200 per vehicle by 2020. Such a large increase in sensors is leading the fourth industrial revolution of connectivity and autonomy. One major downfall to installing many sensors is compromises in their accuracy and processing power due to cost limitations for high volume production. The same common errors in data acquisition such as sampling, quantization, and multiplexing on the CAN bus must be accounted for when utilizing an entire array of vehicle sensors. A huge advantage of onboard sensors is the ability to calculate vehicle parameters during a daily drive cycle to update ECU calibration factors in real time. One such parameter is driveline damping, which changes with gear state and drive mode. A damping value is desired for every gear state.

This paper presents real-world driving energy and fuel consumption results for the second-generation Chevrolet Volt plug-in hybrid electric vehicle (PHEV). A drive cycle, local to Michigan Technological University, was designed to mimic urban and highway driving test cycles in terms of distance, transients and average velocity, but with significant elevation changes to establish an energy intensive real-world driving cycle for assessing potential energy savings for connected and automated vehicle (CAV) control. The investigation began by establishing baseline and repeatability of energy consumption at various battery states of charge. It was determined that drive cycle energy consumption under a randomized set of boundary conditions varied within 3.6% of mean energy consumption regardless of initial battery state of charge.

This paper presents the development of a reduced-order powertrain model for energy and SOC estimation of a multi-mode plug-in hybrid electric vehicle using only vehicle speed profile and route elevation as inputs. Such a model is intended to overcome the computational inefficiencies of higher fidelity powertrain and vehicle models in short and long horizon energy optimization efforts such as Coordinated Adaptive Cruise Control (CACC), Eco Approach and Departure (EcoAND), Eco Routing, and PHEV mode blending. The reduced-order powertrain model enables Connected and Automated Vehicles (CAVs) to utilize the onboard sensor and connected data to quickly react and plan their maneuvers to highly dynamic road conditions with minimal computational resources.

This paper presents a methodology to optimize the blending of charge-depleting (CD) and charge-sustaining (CS) modes in a multi-mode plug-in hybrid electric vehicle (PHEV) that reduces overall energy consumption when the selected route cannot be completely driven in all-electric mode. The PHEV used in this investigation is the second-generation Chevrolet Volt and as many as four instrumented vehicles were utilized simultaneously on road to acquire validation data. The optimization method used is dynamic programming (DP) paired with a reduced-order powertrain model to enable onboard embedded controller compatibility and computational efficiency in optimally blending CD, CS modes over the entire drive route.

Development of the next generation internal combustion engines and automatic transmissions for automotive applications is a mandatory powertrain engineering activity required now and in the coming years to meet forthcoming global emissions regulations. This paper details a preliminary investigation into possible synergies for fuel consumption reduction considering emerging automotive technologies integrated into the next generation combustion engine and automatic transmission architectures. A range of hypothetical gasoline engines were created and paired with a generalized set of step gear automatic transmissions designed to meet the performance requirements of high volume longitudinal full size truck application. These designs were then run through a design of experiments orthogonal array for prediction of fuel consumption on the WLTP test schedule and stand still acceleration to 100 kph.

This investigation utilizes energy analysis and statistical methods to optimize step gear automatic transmissions gear selection for fuel consumption. A full factorial matrix of simulations using energy analysis was performed to determine the optimal number of gears and gear ratios that provide the best fuel consumption performance for a particular vehicle - engine application. The full factorial matrix setup as a design of experiment (DOE) was applied to five vehicle applications, each with two engines to examine the potential differences that variations in road load and engine characteristics might have on optimal transmission gearing selection. The transmission gearing options considered in the DOE were number of gears, launch gear ratio and top gear ratio. Final drive ratio was also included due to its global influence on vehicle performance and powertrain operating speeds and torque.

This investigation utilizes a DFSS analysis approach to determine automatic transmission gear content required to minimize fuel consumption for various powertrain - vehicle systems. L18 and L27 inner arrays with automatic transmission design and shift pattern constraint parameters were varied to determine their relative influence on fuel consumption. An outer noise array consisting of two vehicles with various engines, final drive ratios and legislated emissions test cycles was used to make a robust transmission selection based on minimizing fuel consumption. The full details of the DFSS analysis method and assumptions are presented along with a detailed examination of the results. With respect to transmission design parameters, parasitic spinloss and gear mesh efficiency were found to be most important followed by the number of gears. The DFSS analysis further revealed that unique transmission design formulations are potentially required for widely varying engines.

This paper presents an alternative launch device for layshaft dual clutch transmissions (DCT's). The launch device incorporates a hydrodynamic torque converter, a lockup clutch with controlled slip capability and two wet multi-plate clutches to engage the input shafts of the transmission. The device is intended to overcome the deficiencies associated with using conventional dry or wet launch clutches in DCT's, such as limited torque capacity at vehicle launch, clutch thermal capacity and cooling, launch shudder, lubricant quality and requirement for interval oil changes. The alternative device enhances drive quality and performance at vehicle launch and adds the capability of controlled capacity slip to attenuate gear rattle without early downshifting. Parasitic torque loss will increase but is shown not to drastically influence fuel consumption compared to a dry clutch system, however synchronizer engagement can become a concern at cold operating temperatures.

A cost effective means of achieving fuel economy gains in conventional powertrain is to utilize a 12 volt start/stop (S/S) system to turn the engine off and on during periods of vehicle idle. This paper presents powertrain integration issues specific to a 12 volt S/S system and the powertrain hardware content and calibration strategies required to execute a 12 volt S/S system for start ability, reduced noise and vibration (N&V) and vehicle launch. A correlated lumped parameter modeling methodology is used to determine engine startup profiles, starter hardware and intake cam park position requirements based upon vehicle level response to the startup event. Optimization of the engine startup is reported for a multitude of powertrain configurations, including transverse and longitudinal arrangements with manual, automatic and dual clutch transmissions.

The torque converter and torque converter clutch are critical devices governing overall power transfer efficiency in automatic transmission powertrains. With calibrations becoming more aggressive to meet increasing fuel economy standards, the torque converter clutch is being applied over a wider range of driving conditions. At low engine speed and high engine torque, noise and vibration concerns originating from the driveline, powertrain or vehicle structure can supersede aggressive torque converter clutch scheduling. Understanding the torsional characteristics of the torque converter clutch and its interaction with the drivetrain can lead to a more robust design, operation in regions otherwise restricted by noise and vibration, and potential fuel economy improvement.

The objective of this investigation is to characterize the ability of loose gears to resist rattle in a manual transmission driven by an internal combustion engine. A hemi-anechoic transmission dynamometer test cell with the capability to produce torsional oscillations is utilized to initiate gear rattle in a front wheel drive (FWD) manual transmission, for a matrix of operating loads and selected gear states. A signal processing technique is derived herein to identify onset of gear rattle resulting from a standardized set of measurements. Gear rattle was identified by a distinct change in noise and vibration measures, and correlated to gear oscillations by a computed quantity referred to as percent deviation in normalized gear speed. An angular acceleration rattle threshold is defined based upon loose gear inertia and drag torque. The effects of mean speed, mean and dynamic torque, and gear state on the occurrence of loose gear rattle are reported.

Lumped parameter analysis is a simple and commonly used technique for performing torsional analysis or design parameter sensitivity studies on automotive powertrains and drivelines. The purpose of this paper is to demonstrate the application of lumped parameter analysis to manual transmission gear rattle. A representative model is developed for a FWD manual transmission, as operated in a dynamometer test cell. Once validated by experimental data, the model is used to investigate the influence on gear rattle of parameters not readily modified or controlled during hardware evaluations. A sinusoidal torque is used to excite the system, and a signal processing technique similar to that derived in Part I of this two part paper is used to identify the inception of gear rattle. Functional relations for torque losses associated with shafts, gears, seals, lubricating oil flow and bearing clearances as a function of basic design parameters are included within the model.