Namwook Kim

Sufficient conditions of optimal control based on Pontryagin's minimum principle for use in hybrid electric vehicles

Over 10 years ago, the equivalent consumption minimization strategy was introduced as an effective approach, using the concept of equivalent fuel consumption for electricity use, to solve a control problem for hybrid electric vehicles. Although numerous studies have documented outstanding results as a consequence of applying the concept and have shown that the equivalent consumption minimization strategy could be explained on the basis of an optimal control concept such as Pontryagin’s minimum principle, few studies have proven, mathematically, its optimal performance when solving the control problem of hybrid electric vehicles. The present research builds upon previous research studies that proved that the control based on Pontryagin’s minimum principle can be a global optimal solution for hybrid electric vehicles under the assumption that the battery efficiency is not a function of the state of charge. In this paper, we expand upon the original concept, deriving the optimality within more generalized cases than previously reported. In conclusion, if the battery efficiency is a concave function of the state of charge, which is possibly a natural characteristic of the battery, the optimal control based on Pontryagin’s minimum principle enables optimal performance to be achieved. We can therefore apply this control concept to hybrid electric vehicles which use a wide range of states of charge, such as plug-in hybrid electric vehicles.

Autonomie model validation with test data for 2010 Toyota Prius

The Prius — a power-split hybrid electric vehicle from Toyota — has become synonymous with the word “Hybrid.” As of October 2010, two million of these vehicles had been sold worldwide, including one million vehicles purchased in the United States. In 2004, the second generation of the vehicle, the Prius MY04, enhanced the performance of the components with advanced technologies, such as a new magnetic array in the rotors. However, the third generation of the vehicle, the Prius MY10, features a remarkable change of the configuration ― an additional reduction gear has been added between the motor and the output of the transmission [1]. In addition, a change in the energy management strategy has been found by analyzing the results of a number of tests performed at Argonne National Laboratory’s Advanced Powertrain Research Facility (ARRF). Whereas changes in the configuration, such as the reduction gear, are possibly noticeable, it is not easy to determine the effect of the energy management strategy because the supervisory control algorithm is, generally, not published. Further, it is almost impossible to analyze the algorithm without testing results obtained from a well-designed testing process. On the basis of extensive experience in designing the controllers of power-split hybrid electric vehicles in Autonomie, we could identify the supervisory control algorithm by analyzing the testing results obtained from the APRF. A vehicle model and a control model for the Prius MY10 have been developed to reproduce the real-world behaviors, and the simulation results are compared with the testing results. In the simulation, the developed vehicle model achieves fuel consumption that is close to the testing value, within 5%, and the operation of the engine model was similar to that of the real-world engine.

Comparison between Rule-Based and Instantaneous Optimization for a Single-Mode, Power-Split HEV

Over the past couple of years, numerous Hybrid Electric Vehicle (HEV) powertrain configurations have been introduced into the marketplace. Currently, the dominant architecture is the power-split configuration, notably the input splits from Toyota Motor Sales and Ford Motor Company. This paper compares two vehicle-level control strategies that have been developed to minimize fuel consumption while maintaining acceptable performance and drive quality. The first control is rules based and was developed on the basis of test data from the Toyota Prius as provided by Argonne National Laboratory’s (Argonne’s) Advanced Powertrain Research Facility. The second control is based on an instantaneous optimization developed to minimize the system losses at every sample time. This paper describes the algorithms of each control and compares vehicle fuel economy (FE) on several drive cycles. Results demonstrate that both algorithms achieve similar FE values, which serve to demonstrate the benefits of the instantaneous optimal control: because it does not require tuning by the engineers, control development time is accelerated.

Vehicle-level control analysis of 2010 Toyota Prius based on test data

The Prius, a power-split hybrid electric vehicle developed by Toyota, has been the top-selling vehicle in the United States hybrid electric vehicle market for the last decade. The transmission system of the vehicle is a frequent theme of study for hybrid electric vehicles. However, the control concept of the vehicle is not well known, since analyzing control behaviors requires well-designed facilities to obtain testing results and well-defined processes to analyze the obtained results. Argonne National Laboratory has these resources and capabilities. In addition, Argonne has produced a reliable simulation tool, Autonomie, by which a vehicle model for the 2010 Prius is developed on the basis of the analyzed results, and it is validated with the results of testing. The developed model demonstrates that results of vehicle performance from simulation are close to those of from real-world tests—within 5%. The main focus of this study is to provide information about the supervisory control for the 2010 Prius, so that researchers can reproduce the real-world behavior of the vehicle through simulations. The analyzed control ideas based on the testing results will be very helpful in terms of understanding the control behavior of the vehicle, and the information resulting from this study is useful to develop the controller for the vehicle at a simulation level.

Electric Drive Vehicle Development and Evaluation Using System Simulation

Abstract To reduce development time and introduce technologies faster to the market, many companies have been moving to Model-based System Engineering (MBSE). In MBSE, the development process centers around a multi-physics model of the complete system being developed, from requirements to design, implementation and test. Engineers can avoid a generation of system design processes based on hand coding, and use graphical models to design, analyze, and implement the software that determines system performance and behavior. This paper describes the process implemented in Autonomie, a Plug-and-Play Software Environment, to design and evaluate electric drive powertrain and component technologies in a multi-physics environment. We will discuss best practices and provide examples of the different steps of the V-diagram including model-in-the-loop, software-in-the-loop and component-in-the-loop simulation.