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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 studies the hardware-in-the-loop (HiL) design of a power-split hybrid electric vehicle (HEV) for the research of HEV lithiumion battery aging. In this paper, an electrochemical model of a lithium-ion battery pack with the characteristics of battery aging is built and integrated into the vehicle model of Autonomie® software from Argonne National Laboratory. The vehicle model, together with the electrochemical battery model, is designed to run in a dSPACE real-time simulator while the powertrain power distribution is managed by a dSPACE MicroAutoBoxII hardware controller. The control interface is designed using dSPACE ControlDesk to monitor the real-time simulation results. The HiL simulation results with the performance of vehicle dynamics and the thermal aging of the battery are presented and analyzed.

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.

This paper evaluates the potential of adding higher alcohols to gasoline blendstock in an attempt to improve overall fuel performance. The alcohols considered include ethanol, normal- and iso-structures of propanol, butanol and pentanol as well as normal-hexanol (C2-C6). Fuel performance is quantified based on energy content, knock resistance as well as petroleum displacement and promising multi-component blends are systematically identified based on property prediction methods. These promising multi-component blends, as well as their respective reference fuels, are subsequently tested for efficiency and emissions performance utilizing a gasoline direct injection, spark ignition engine. The engine test results confirm that combustion and efficiency of tailored multi-component blends closely match those of the reference fuels. Regulated emissions stemming from combustion of these blends are equal or lower compared to the reference fuels across the tested engine speed and load regime.

Interest in natural gas as a fuel for light-duty transportation has increased due to its domestic availability and lower cost relative to gasoline. Natural gas, comprised mainly of methane, has a higher knock resistance than gasoline making it advantageous for high load operation. However, the lower flame speeds of natural gas can cause ignitability issues at part-load operation leading to an increase in the initial flame development process. While port-fuel injection of natural gas can lead to a loss in power density due to the displacement of intake air, injecting natural gas directly into the cylinder can reduce such losses. A study was designed and performed to evaluate the potential of natural gas for use as a light-duty fuel. Steady-state baseline tests were performed on a single-cylinder research engine equipped for port-fuel injection of gasoline and natural gas, as well as centrally mounted direct injection of natural gas.

This paper details a study of the effects of multiple torque converter design and operating point parameters on the resistance of the converter to cavitation during vehicle launch. The onset of cavitation is determined by an identifiable change in the noise radiating from the converter during operation, when the collapse of cavitation bubbles becomes detectable by nearfield acoustical measurement instrumentation. An automated torque converter dynamometer test cell was developed to perform these studies, and special converter test fixturing is utilized to isolate the test unit from outside disturbances. A standard speed sweep test schedule is utilized, and an analytical technique for identifying the onset of cavitation from acoustical measurement is derived. Effects of torque converter diameter, torus dimensions, and pump and stator blade designs are determined.

Measurement of internal combustion engine air flow is challenging due to the required modification of the intake system and subsequent change in the air flow pattern. In this paper, various surge tank volumes were investigated to improve the accuracy of measuring air flow rate into a 674-cm3, four-stroke, liquid-cooled, internal combustion engine. According to the experimental results, when the venturi meter is used to measure the intake air flow rate, an air surge tank is required to be installed downstream of the venturi to smoothen the air flow. Moreover, test results revealed that increasing air surge tank volume beyond a limit could have a negative effect on the engine performance parameters especially in carbureted engines where controlling AFR is difficult. Although the air flow rate into the engine changed with increasing tank volume, the air-fuel ratio was leaner for smaller tank volumes.

The increased market share of electric vehicles and renewable energy resources have raised concerns about their impact on the current electrical distribution grid. To achieve sustainable and stable power distribution, a lot of effort has been made to implement smart grids. This paper addresses Demand Response (DR) load control in a smart grid using Internet of Things (IoT) technology. A smart grid is a networked electrical grid which includes a variety of components and sub-systems, including renewable energy resources, controllable loads, smart meters, and automation devices. An IoT approach is a good fit for the control and energy management of smart grids. Although there are various commercial systems available for smart grid control, the systems based on open sources are limited. In this study, we adopt an open source development platform named Node-RED to integrate DR capabilities in a smart grid for DR load control. The DR system employs the OpenADR standard.

Vehicle-to-Grid (V2G) service has a potential to improve the reliability and stability of the electrical grid due to the ability of providing bi-directional power flow from/to the grid. However, frequent charging/discharging may impact the battery lifetime. This paper presents the analysis of battery degradation in three scenarios. In the first scenario, different battery capacities are considered. In the second scenario, the battery degradation with various depth of discharge (DOD) are studied. In the third scenario, the capacity loss due to different charging regime are compared. The charging/discharging of plug-in electric vehicles (PEVs) are simulated in a single-phase microgrid system integrated with a photovoltaics (PV) farm, an energy storage system (ESS), and ten electric vehicle service equipment (EVSE). The battery degradation model is an energy throughput model, which is developed based on the Arrhenius equation and a power law relationship between time and capacity fading.

When a manual transmission (MT) powertrain is subjected to high speeds and high torques, the vehicle driveshaft, and other components experience an increase in stored potential energy. When the engine and driveshaft are decoupled during an up or down shift, the potential energy is released causing clunk during the shift event. The customer desires a smooth shift thus reduction of clunk will improve experience and satisfaction. In this study, a six-speed MT, rear-wheel-drive (RWD) passenger vehicle was used to experimentally capture acoustic and vibration data during the clunk event. To replicate the in-situ results, additional data was collected and analyzed for powertrain component roll and pitch. A lumped parameter model of key powertrain components was created to replicate the clunk event and correlate with test data. The lumped parameter model was used to modify clutch tip-out parameters, which resulted in reduced prop shaft oscillations.

This paper studies the nonlinear model predictive control for a power-split Hybrid Electric Vehicle (HEV) power management system to improve the fuel economy. In this paper, a physics-based battery model is built and integrated with a base HEV model from Autonomie®, a powertrain and vehicle model architecture and development software from Argonne National Laboratory. The original equivalent circuit battery model from the software has been replaced by a single particle electrochemical lithium ion battery model. A predictive model that predicts the driver’s power request, the battery state of charge (SOC) and the engine fuel consumption is studied and used for the nonlinear model predictive controller (NMPC). A dedicated NMPC algorithm and its solver are developed and validated with the integrated HEV model. The performance of the NMPC algorithm is compared with that of a rule-based controller.

For spark-ignition gasoline engines operating under the wide speed and load conditions required for light duty vehicles, ignition quality limits the ability to minimize fuel consumption and NOx emissions via dilution under light and part load conditions. In addition, during transients including tip-outs, high levels of dilution can occur for multiple combustion events before either the external exhaust gas can be adjusted and cleared from the intake or cam phasing can be adjusted for correct internal dilution. Further improvement and a thorough understanding of the impact of the ignition system on combustion near the dilution limit will enable reduced fuel consumption and robust transient operation. To determine and isolate the effects of multiple parameters, a variable output ignition system (VOIS) was developed and tested on a 3.5L turbocharged V6 homogeneous charge direct-injection gasoline engine with two spark plug gaps and three ignition settings.

Higher carbon number alcohols offer an opportunity to meet the Renewable Fuel Standard (RFS2) and improve the energy content, petroleum displacement, and/or knock resistance of gasoline-alcohol blends from traditional ethanol blends such as E10 while maintaining desired and regulated fuel properties. Part II of this paper builds upon the alcohol selection, fuel implementation scenarios, criteria target values, and property prediction methodologies detailed in Part I. For each scenario, optimization schemes include maximizing energy content, knock resistance, or petroleum displacement. Optimum blend composition is very sensitive to energy content, knock resistance, vapor pressure, and oxygen content criteria target values. Iso-propanol is favored in both scenarios' suitable blends because of its high RON value.

The U.S. Renewable Fuel Standard (RFS2) requires an increase in the use of advanced biofuels up to 36 billion gallons by 2022. Longer chain alcohols, in addition to cellulosic ethanol and synthetic biofuels, could be used to meet this demand while adhering to the RFS2 corn-based ethanol limitation. Higher carbon number alcohols can be utilized to improve the energy content, knock resistance, and/or petroleum displacement of gasoline-alcohol blends compared to traditional ethanol blends such as E10 while maintaining desired and regulated fuel properties. Part I of this paper focuses on the development of scenarios by which to compare higher alcohol fuel blends to traditional ethanol blends. It also details the implementation of fuel property prediction methods adapted from literature. Possible combinations of eight alcohols mixed with a gasoline blendstock were calculated and the properties of the theoretical fuel blends were predicted.

This paper investigates the application of torque weighting to vibration dose value. This is done as a means to enhance correlation of perceived drive comfort directly to driver pedal commands while rejecting uncorrelated inputs. Current industry standards for vehicle comfort are formulated and described by ISO2631, which is a culmination of research with single or multi-axis vibration of narrow or broadband excitation. The standard is capable of estimating passenger comfort to vibrations, however, it only accounts for reaction vibrations to controlled inputs and not perceived vibration request vs. response vibration. Metrics that account for torque inputs and the vibration response create actionable estimates of dosage due to driver torque requests without uncorrelated inputs. This reduces the need for additional accelerometers and special compensating algorithms when road or track testing. The use case for the proposed modified metric is during the powertrain calibration process.

A test stand was developed to evaluate an 11.5 cc, two-stroke, internal combustion engine in anticipation of future combustion system modifications. Detailed engine testing and analysis often requires complex, specialized, and expensive equipment, which can be problematic for research budgets. This problem is compounded by the fact that testing “micro” engines involves low flow rates, high rotational speeds, and compact dimensions which demand high-accuracy, high-speed, and compact measurement systems. On a limited budget, the task of developing a micro-engine testing system for advanced development appears quite challenging, but with careful component selection it can be accomplished. The anticipated engine investigation includes performance testing, fuel system calibration, and combustion analysis. To complete this testing, a custom test system was developed.

This paper investigates the aging performance of the lithium ion cobalt oxide battery pack of a single shaft parallel hybrid electric vehicle (HEV) under different ambient temperatures. Varying ambient temperature of HEVs results in different battery temperature and then leads to different aging performance of the battery pack. Battery aging is reflected in the increasing of battery internal resistance and the decreasing of battery capacity. In this paper, a single shaft parallel hybrid electric vehicle model is built by integrating Automotive Simulation Model (ASM) from dSPACE and AutoLion-ST battery model from ECPower to realize the co-simulation of HEV powertrain in the common MATLAB/Simulink platform. The battery model is a physics-based and thermally-coupled battery (TCB) model, which enables the investigation of battery capacity degradation and aging. Standard driving cycle with differing ambient temperatures is tested using developed HEV model.

Extreme Fast Charging (XFC) infrastructure is crucial for an increase in electric vehicle (EV) adoption. However, an unmanaged implementation may lead to negative grid impacts and huge power costs. This paper presents an optimal energy management strategy to utilize grid-connected Energy Storage Systems (ESS) integrated with XFC stations to mitigate these grid impacts and peak demand charges. To achieve this goal, an algorithm that controls the charge and discharge of ESS based on an optimal power threshold is developed. The optimal power threshold is determined to carry out maximum peak shaving for given battery size and SOC constraints.

Predictive Signal Phase and Timing (SPAT) message set is one fundamental building block for vehicle-to-infrastructure (V2I) applications such as Eco-Approach and Departure (EAD) at traffic signal controlled urban intersections. Among the two complementary communication methods namely short-range sidelink (PC5) and long-range cellular radio link (Uu), this paper documents the work with long-range link: the complete data chain includes connecting to the traffic signals via existing backhaul communication network, collecting the raw signal phase state data, predicting the signal state changes and delivering the SPAT data via a geofenced service to requests over HTTP protocols. An Application Programming Interface (API) library is developed to support various cellular data transmission reduction and latency improvement techniques.

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.