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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.

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.

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.

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.

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.

The Electronic Control Unit (ECU) of an Electric Power Steering (EPS) system is a core device to decide how much assistance an electric motor applies on a steering wheel. The EPS ECU plays an important role in EPS systems. The effectiveness of an ECU needs to be thoroughly tested before mass production. Hardware-in-the-loop simulation provides an efficient way for the development and testing of embedded controllers. This paper focuses on the development of a HiL system for testing EPS controllers. The hardware of the HiL system employs a dSPACE HiL simulator. The EPS plant model is an integrated model consisting of a Vehicle Dynamics model of the dSPACE Automotive Simulation Model (ASM) and the Nexteer Steering model. The paper presents the design of an EPS HiL system, the simulation of sensors and actuators, the functions of the ASM Vehicle Dynamics model, and the integration method of the ASM Vehicle Dynamics model with a Steering model.

The emerging need of building an efficient Electric Vehicle (EV) charging infrastructure requires the investigation of all aspects of Vehicle-Grid Integration (VGI), including the impact of EV charging on the grid, optimal EV charging control at scale, and communication interoperability. This paper presents a cloud-based simulation and testing platform for the development and Hardware-in-the-Loop (HIL) testing of VGI technologies. Although the HIL testing of a single charging station has been widely performed, the HIL testing of spatially distributed EV charging stations and communication interoperability is limited. To fill this gap, the presented platform is developed that consists of multiple subsystems: a real-time power system simulator (OPAL-RT), ISO 15118 EV Charge Scheduler System (EVCSS), and a Smart Energy Plaza (SEP) with various types of charging stations, solar panels, and energy storage systems.

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.