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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.

A multimode transmission combines several power-split modes and possibly several fixed gear modes, thanks to complex arrangements of planetary gearsets, clutches and electric motors. Coupled to a battery, it can be used in a highly flexible hybrid configuration, which is especially practical for larger cars. The Chevrolet Tahoe Hybrid is the first light-duty vehicle featuring such a system. This paper introduces the use of a high-level vehicle controller based on instantaneous optimization to select the most appropriate mode for minimizing fuel consumption under a broad range of vehicle operating conditions. The control uses partial optimization: the engine ON/OFF and the battery power demand regulating the battery state-of-charge are decided by a rule-based logic; the transmission mode as well as the operating points are chosen by an instantaneous optimization module that aims at minimizing the fuel consumption at each time step.

Argonne National Laboratory (ANL), working with the FreedomCAR Partnership, maintains the hybrid vehicle simulation software, Powertrain System Analysis Toolkit (PSAT). The importance of component models and the complexity involved in setting up optimized control laws require validation of the models and control strategies. Using its Advanced Powertrain Research Facilities (APRF), ANL thoroughly tested the 2004 Toyota Prius to validate the PSAT drivetrain. In this paper, we will first describe the methodology used to quality check test data. Then, we will explain the validation process leading to the simulated vehicle control strategy tuning, which is based on the analysis of the differences between test and simulation. Finally, we will demonstrate the validation of PSAT Prius component models and control strategy, using APRF vehicle test data.