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

Event
SAE Convergence 2010
Authors Abstract
Content
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.
Meta TagsDetails
DOI
https://doi.org/10.4271/2010-01-2310
Pages
12
Citation
Vijayagopal, R., Kwon, J., Rousseau, A., and Maloney, P., "Maximizing Net Present Value of a Series PHEV by Optimizing Battery Size and Vehicle Control Parameters," SAE Int. J. Passeng. Cars - Electron. Electr. Syst. 3(2):56-67, 2010, https://doi.org/10.4271/2010-01-2310.
Additional Details
Publisher
Published
Oct 19, 2010
Product Code
2010-01-2310
Content Type
Journal Article
Language
English