Ram Vijayagopal

Automated Model Based Design Process to Evaluate Advanced Component Technologies

To reduce development time and introduce technologies faster to the market, many companies have been turning more and more to Model Based Design. In Model Based Design, the development process centers around a system model, from requirements capture and design to implementation and test. Engineers can skip over a generation of system design processes on the basis of hand coding and use graphical models to design, analyze, and implement the software that determines machine performance and behavior. This paper describes the process implemented in Autonomie, a Plug-and-Play Software Environment, to design and evaluate component hardware in an emulated environment. We will discuss best practices and provide an example through evaluation of advanced high-energy battery pack within an emulated Plug-in Hybrid Electric Vehicle.

Maximizing Net Present Value of a Series PHEV by Optimizing Battery Size and Vehicle Control Parameters

For a series plug-in hybrid electric vehicle (PHEV), it is critical that batteries be sized to maximize vehicle performance variables, such as fuel efficiency, gasoline savings, and zero emission capability. The wide range of design choices and the cost of prototype vehicles calls for a development process to quickly and systematically determine the design characteristics of the battery pack, including its size, and vehicle-level control parameters that maximize the net present value (NPV) of a vehicle during the planning stage. Argonne National Laboratory has developed Autonomie, a modeling and simulation framework. With support from The MathWorks, Argonne has integrated an optimization algorithm and parallel computing tools to enable the aforementioned development process. This paper presents a study that utilized the development process, where the NPV is the present value of all the future expenses and savings associated with the vehicle. The initial investment on the battery and the future savings that result from reduced gasoline consumption are compared. The investment and savings results depend on the battery size and the vehicle usage. For each battery size, the control parameters were optimized to ensure the best performance possible with the battery design under consideration. Real-world driving patterns and survey results from the National Highway Traffic Safety Administration were used to simulate the usage of vehicles over their lifetime.

Fuel Displacement Potential of a Thermoelectric Generator in a Conventional Vehicle

This paper evaluates the fuel displacement potential of a Thermoelectric Generator (TEG) device in a conventional gasoline vehicle using vehicle simulation and engine in the loop. A TEG device was modelled in Simulink, to exhibit the thermal and electrical characteristics of such a device. This TEG model was integrated into the vehicle simulation software, Autonomie and evaluated in a real engine - virtual vehicle scenario using Engine in the loop (EIL) technique. The EIL approach was used to evaluate the fuel consumption benefit of TEG under cold and hot conditions. The complete vehicle model was then validated and used to evaluate the impact of the current TEG system on additional drive cycles as well as future TEG systems (i.e. no device temperature limits). EIL evaluation shows a fuel economy gain within the current device of 1% on the US06 cycle. The simulation study will quantify the impact of driving cycles and TEG design on fuel displacement potential.

Battery in the loop: Battery evaluation in a systems context

System simulation and hardware component in the loop are key steps in the model-based system engineering (MBSE) process that seeks to shorten the development time of advanced powertrain technologies. Battery Component in the Loop (BCIL), also commonly abbreviated as Battery in the Loop (BIL), is an important step in the evaluation of advanced prototype batteries for electrified vehicles. This paper discusses the possible experiments that can be performed with BIL in order to evaluate the battery in a vehicle systems context (battery focused), and to evaluate the vehicle-level impact of different battery scenarios (vehicle focused). The paper then details the numerous steps necessary in the setup of BIL, including system simulation and virtual vehicle development, hardware setup, closed loop control development, and actual battery evaluation in a virtual vehicle environment. BIL was used to evaluate a prototype 48-V battery developed by Samsung SDI. The virtual vehicle was developed in Autonomie, Argonne National Laboratorys vehicle system simulation software. A mean value engine model developed in AMESim was integrated in the virtual vehicle model and targeted to a dSPACE system for BIL evaluation of the real battery pack. The system evaluation of the Samsung SDI battery will be used to describe the BCIL process throughout the paper.

PHEV Energy Management Strategies at cold temperatures with Battery temperature rise and Engine efficiency Improvement considerations

Limited battery power and poor engine efficiency at cold temperature results in low plug in hybrid vehicle (PHEV) fuel economy and high emissions. Quick rise of battery temperature is not only important to mitigate lithium plating and thus preserve battery life, but also to increase the battery power limits so as to fully achieve fuel economy savings expected from a PHEV. Likewise, it is also important to raise the engine temperature so as to improve engine efficiency (therefore vehicle fuel economy) and to reduce emissions. One method of increasing the temperature of either component is to maximize their usage at cold temperatures thus increasing cumulative heat generating losses. Since both components supply energy to meet road load demand, maximizing the usage of one component would necessarily mean low usage and slow temperature rise of the other component. Thus, a natural trade-off exists between battery and engine warm-up. This paper compares energy management strategies for a power-split PHEV for their ability to warm –up the battery and the engine, and ultimately the resulting fuel economy. The engine model predicts engine fuel rate as a function of engine utilization history and starting temperature, apart from speed and torque. The battery temperature rise model is a function of battery utilization. Engine and battery utilization is varied by changing the control parameter - wheel power demand at which the engine turns ON. The paper analyses the sensitivity of fuel and electrical energy consumption to engine and battery temperature rise, for different driving distances and driver aggressivenes