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Electrification of vehicles is an important step towards making mobility more sustainable and carbon-free. Hybrid electric vehicles use an electric machine with an on-board energy storage system, in some form to provide additional torque and reduce the power requirement from the internal combustion engine. It is important to control and optimize this power source split between the engine and electric machine to make the best use of the system. This paper showcases an implementation of the Adaptive Equivalent Consumption Minimization Strategy (A-ECMS) with minimization in real-time in the dSPACE MicroAutobox II as the Hybrid Supervisory Controller (HSC). While the concept of A-ECMS has been well established for many years, there are no published papers that present results obtained in a production vehicle suitably modified from conventional to hybrid electric propulsion including real world testing as well as testing on regulatory cycles.

Delivery trucks and vans represent a growing transportation segment which reflects the shift of consumers towards on-line shopping and on-demand delivery. Therefore, electrification of this class of vehicles is going to play a major role in the decarbonization of the transportation sector and in the transition to a sustainable mobility system. Hybrid electric vehicles can represent a medium-term solution and have gained an increasing share of the market in recent years. These vehicles include two power sources, typically an internal combustion engine and a battery, which gives more degrees of freedom when controlling the powertrain to satisfy the power request at the wheels. Components sizing and powertrain energy management are strongly coupled and can make a substantial impact on the final energy consumption of a hybrid vehicle.

The definition of the energy management strategy for a hybrid electric vehicle is a key element to ensure maximum energy efficiency. The ability to optimally manage the on-board energy sources, i.e., fuel and electricity, greatly affects the final energy consumption of hybrid powertrains. In the case of plug-in series-hybrid architectures, such as Range-Extender Electric Vehicles (REEVs), fuel efficiency optimization alone can result in a stressful operation of the range-extender engine with an excessively high number of start/stops. Nonetheless, reducing the number of start/stops can lead to long periods in which the engine is off, resulting in the after-treatment system temperature to drop and higher emissions to be produced at the next engine start.

A key enabler to maximizing the benefits from advanced powertrain technologies is to adopt a systems integration approach and develop optimized controls that consider the propulsion system and vehicle as a whole. This approach becomes essential when incorporating Advanced Driver Assistance Systems (ADAS) and communication technologies, which can provide information on future driving conditions. This may enable the powertrain control system to further improve the vehicle performance and energy efficiency, shifting from an instantaneous optimization of energy consumption to a predictive and “look-ahead” optimization. Benefits from this approach can be realized at all levels of electrification, from conventional combustion engines to hybrid propulsion systems and full electric vehicles, and at all levels of vehicle automation.

The assessment of fuel economy of new vehicles is typically based on regulatory driving cycles, measured in an emissions lab. Although the regulations built around these standardized cycles have strongly contributed to improved fuel efficiency, they are unable to cover the envelope of operating and environmental conditions the vehicle will be subject to when driving in the “real-world”. This discrepancy becomes even more dramatic with the introduction of Connectivity and Automation, which allows for information on future route and traffic conditions to be available to the vehicle and powertrain control system. Furthermore, the huge variability of external conditions, such as vehicle load or driver behavior, can significantly affect the fuel economy on a given route. Such variability poses significant challenges when attempting to compare the performance and fuel economy of different powertrain technologies, vehicle dynamics and powertrain control methods.

Battery packs used in electrified automotive powertrains support heavy electrical loads resulting in significant heat generation within them. Cooling systems are used to regulate the battery pack temperatures, helping to slow down battery aging. Vehicle-level energy consumption simulations serve as a first step for determining the specifications of a battery cooling system based on the duty cycle and interactions with the rest of the powertrain. This paper presents the development of a battery model that takes into account the energy impact of heating in the battery and demonstrates its use in a vehicle-level energy consumption simulator to set the specifications of a suitable cooling system for a vehicle application. The vehicle application used in this paper is a Class 6 Pickup and Delivery commercial vehicle with a Range-Extended Electric Vehicle (REEV) powertrain configuration.

Global regulatory targets and customer demand are driving the automotive industry to improve vehicle fuel efficiency. Methods for achieving increased efficiency include improvements in the internal combustion engine and an accelerating shift toward electrification. A key enabler to maximizing the benefit from these new powertrain technologies is proper systems integration work - including developing optimized controls for the propulsion system as a whole. The next step in the evolution of improving the propulsion management system is to make use of available information not typically associated with the powertrain. Advanced driver assistance systems, vehicle connectivity systems and cloud applications can provide information to the propulsion management system that allows a shift from instantaneous optimization of fuel consumption, to optimization over a route. In the current paper, we present initial work from a project being done as part of the DOE ARPA-E NEXTCAR program.

The EcoCAR 2: Plugging into the Future team at The Ohio State University is designing a Parallel-Series Plug-in Hybrid Electric Vehicle capable of 44 miles of all-electric range. The vehicle features an 18.9-kWh lithium-ion battery pack with range extending operation in both series and parallel modes. This is made possible by a 1.8-L ethanol (E85) engine and 6-speed automated manual transmission. This vehicle is designed to drastically reduce fuel consumption, with a utility factor weighted fuel economy of 50 miles per gallon gasoline equivalent (mpgge), while meeting Tier II Bin 5 emissions standards. This paper details three years of modeling and simulation development for the OSU EcoCAR 2 vehicle. Included in this paper are the processes for developing simulation platform and model requirements, plant model and soft ECU development, test development and validation, automated regression testing, and controls and calibration optimization.

The EcoCAR 2 team at the Ohio State University has designed an extended-range electric vehicle capable of 44 miles all-electric range, which features a 18.9-kWh lithium-ion battery pack with range extending operation in both series and parallel modes made possible by a 1.8-L ethanol (E85) engine and a 6-speed automated manual transmission. This vehicle is designed to reduce fuel consumption, with a utility factor weighted fuel economy of 50 miles per gallon gasoline equivalent (mpgge), while meeting Tier II Bin 5 emissions standards. This report documents the team's refinement work on the vehicle during Year 3 of the competition, including vehicle improvements, control strategy calibration and dynamic vehicle testing, culminating in a 99% buy off vehicle that meets the goals set forth by the team. This effort was made possible through support from the U.S. Department of Energy, General Motors, The Ohio State University, and numerous competition and local sponsors.

This paper describes the development and experimental validation of a Plug-in Hybrid Electric Vehicle (PHEV) dynamic simulator that enables development, testing, and calibration of a traction control strategy. EcoCAR 2 is a three-year competition between fifteen North American universities, sponsored by the Department of Energy and General Motors that challenges students to redesign a Chevrolet Malibu to have increased fuel economy and decreased emissions while maintaining safety, performance, and consumer acceptability. The dynamic model is developed specifically for the Ohio State University EcoCAR 2 Team vehicle with a series-parallel PHEV architecture. This architecture features, in the front of the vehicle, an ICE separated from an automated manual transmission with a clutch as well as an electric machine coupled via a belt directly to the input of the transmission. The rear powertrain features another electric machine coupled to a fixed ratio gearbox connected to the wheels.

The EcoCAR 2: Plugging into the Future team at the Ohio State University is designing a Parallel-Series Plug-in Hybrid Electric Vehicle capable of 50 miles of all-electric range. The vehicle features a 18.9-kWh lithium-ion battery pack with range extending operation in both series and parallel modes. This is made possible by a 1.8-L ethanol (E85) engine and 6-speed automated manual transmission. This vehicle is designed to drastically reduce fuel consumption, with a utility factor weighted fuel economy of 51 miles per gallon gasoline equivalent (mpgge), while meeting Tier II Bin 5 emissions standards. This report details the fabrication and control implementation process followed by the Ohio State team during Year 2 of the competition. The fabrication process includes finalizing designs based on identified requirements, building and assembling components, and performing extensive validation testing on the mechanical, electrical and control systems.

The EcoCAR 2: Plugging into the Future team at the Ohio State University is designing a Parallel-Series Plug-in Hybrid Electric Vehicle capable of 50 miles of all-electric range. The vehicle features a 18.9-kWh lithium-ion battery pack with range extending operation in both series and parallel modes made possible by a 1.8-L ethanol (E85) engine and 6-speed automated manual transmission. This vehicle is designed to drastically reduce fuel consumption, with a utility factor weighted fuel economy of 75 miles per gallon gasoline equivalent (mpgge), while meeting Tier II Bin 5 emissions standards. This report details the rigorous design process followed by the Ohio State team during Year 1 of the competition. The design process includes identifying the team customer's needs and wants, selecting an overall vehicle architecture and completing detailed design work on the mechanical, electrical and control systems. This effort was made possible through support from the U.S.

For simulation and analysis of vehicles there is a need to have a means of generating drive cycles which have properties similar to real world driving. A method is presented which uses measured vehicle speed from a number of vehicles to generate a Markov chain model. This Markov chain model is capable of generating drive cycles which match the statistics of the original data set. This Markov model is then used in an iterative fashion to generate drive cycles which match constraints imposed by the user. These constraints could include factors such number of stops, total distance, average speed, or maximum speed. In this paper, systematic analysis was done for a PHEV fleet which consists of 9 PHEVs that were instrumented using data loggers for a period of approximately two years. Statistical analysis using principal component analysis and a clustering approach was carried out for the real world velocity profiles.

Two-stage turbochargers are a recent solution to improve engine performance, reducing the turbo-lag phenomenon and improving the matching. However, the definition of the control system is particularly complex, as the presence of two turbochargers that can be in part operated independently requires effort in terms of analysis and optimization. This work documents a characterization study of two-stage turbocharger systems. The study relies on a mean-value model of a Diesel engine equipped with a two-stage turbocharger, validated on experimental data. The turbocharger is characterized by a VGT actuator and a bypass valve (BPV), both located on the high-pressure turbine. This model structure is representative of a “virtual engine”, which can be effectively utilized for applications related to analysis and control. Using this tool, a complete characterization was conducted considering key operating conditions representative of FTP driving cycle operations.

Two-stage turbochargers are a recent solution to improve engine performance. The large flexibility of these systems, able to operate in different modes, can determine a reduction of the turbo-lag phenomenon and improve the engine tuning. However, the presence of two turbochargers that can be in part operated independently requires effort in terms of analysis and optimization to maximize the benefits of this technology. In addition, the design and calibration of the control system is particularly complex. The transitioning between single stage and two-stage operations poses further control issues. In this scenario a model-based approach could be a convenient and effective solution to investigate optimization, calibration and control issues, provided the developed models retain high accuracy, limited calibration effort and the ability to run in real time.

The objective draw by this project is to develop tools for Plug-in Hybrid Electric Vehicle (PHEV) design, energy analysis and energy management, with the aim of analyzing the effect of design, driving cycles, charging frequency and energy management on performance, fuel economy, range and battery life. A Chevrolet Equinox fueled by bio diesel B20 has been hybridized at the Center for Automotive Research (CAR), at The Ohio State University. The vehicle model has been developed in Matlab/Simulink environment, and validated based on laboratory and test. The PHEV battery pack has been modeled starting from Li-Ion batteries experimental data and then implemented into the simulator. In order to simulate “real world” scenarios, custom driving cycles/typical days were identified starting from average driving statistics and well-known cycles.

Plug-In Hybrid Vehicles (PHEVs) represent the middle point between Hybrid Electric Vehicles (HEVs) and Electric Vehicles (EVs), thus combining benefits of the two architectures. PHEVs can achieve very high fuel economy while preserving full functionality of hybrids - long driving range, easy refueling, lower emissions etc. These advantages come at an expense of added complexity in terms of available fuel. The PHEV battery is recharged both though regenerative braking and directly by the grid thus adding extra dimension to the control problem. Along with the minimization of the fuel consumption, the amount of electricity taken from the power grid should be also considered, therefore the electricity generation mix and price become additional parameters that should be included in the cost function.

Model-based design is a collection of practices in which a system model is at the center of the development process, from requirements definition and system design to implementation and testing. This approach provides a number of benefits such as reducing development time and cost, improving product quality, and generating a more reliable final product through the use of computer models for system verification and testing. Model-based design is particularly useful in automotive control applications where ease of calibration and reliability are critical parameters. A novel application of the model-based design approach is demonstrated by The Ohio State University (OSU) student team as part of the Challenge X advanced vehicle development competition. In 2008, the team participated in the final year of the competition with a highly refined hybrid-electric vehicle (HEV) that uses a through-the-road parallel architecture.

Strong dependency on crude oil in most areas of modern transportation needs lead into a significant consumption of petroleum resources over many decades. In order to maximize the effective use of remaining resources, various types of powertrain topologies, such as hybrid configurations among fuel cell, electric battery as well as conventional IC engine, have been proposed and tested out for number of vehicle classes including a personal commuting vehicle. In this paper the vehicle parameters are based on a typical commercial sub-compact vehicle (FIAT Panda) and energy needs are estimated on the sized powertrain. The main control approach is divided in two categories: off-line global optimization with dynamic programming (DP, not implementable in real time), and on-line Proportional and Feed-Forward with PI controllers. The proposed control approaches are developed both for charge-sustaining and charge-depleting mode and sample results are shown and compared.

The intent of this research is to understand the effects worn dampers have on vehicle stability and safety through dynamic model simulation. Dampers, an integral component of a vehicle's suspension system, play an important role in isolating road disturbances from the driver by controlling the motions of the sprung and unsprung masses. This paper will show that a decrease in damping leads to excessive body motions and a potentially unstable vehicle. The concept of poor damping affecting vehicle stability is well established through linear models. The next step is to extend this concept for non-linear models. This is accomplished through creating a vehicle simulation model and executing several driving maneuvers with various damper characteristics. The damper models used in this study are based on splines representing peak force versus velocity relationships.