John Czubay

Derivation and Experimental Validation of a Power-Split Hybrid Electric Vehicle Model

Hybrid electric vehicles (HEVs) have attracted a lot of attention due to environmental and efficiency reasons. Typically, an HEV combines two power trains, a conventional power source such as a gasoline engine, a diesel engine, or a fuel cell stack, and an electric drive system (involving a motor and a generator) to produce driving power with a potential of higher fuel economy than conventional vehicles. Furthermore, such vehicles do not require external charging and thus work within the existing fueling infrastructures. The power-split power train configuration of an HEV has the individual advantages of the series and parallel types of HEV power train configurations. A sophisticated control system, however, is required to manage the power-split HEV power trains. Designing such a control system requires a reasonably accurate HEV system plant model. Much research has been done for developing dynamic plant models for the series and parallel types, but a complete and validated dynamic model for the power-split HEV power train is still in its infancy. This paper presents a power-split power train HEV dynamic model capable of realistically replicating all the major steady-state and transient phenomena appearing under different driving conditions. A mathematical derivation and modeling representation of this plant model and its components is shown first. Next, the analysis, verification, and validation through computer simulation and comparison with the data actually measured in the test vehicle at the Ford Motor Companys test track is performed. The excellent agreements between the model and the experimental results demonstrate the fidelity and validity of the derived plant model. Since this plant model was built by integrating the subsystem models using a system-oriented approach with a hierarchical methodology, it is easy to change subsystem functionalities. The developed plant model is useful for analyzing and understanding the dominant dynamics of the power train system, the interaction between subsystems and components, and system transients due to the change of operational state and the influence of disturbances. This plant model can also be employed for the development of vehicle system controllers, evaluation of energy management strategies, issue resolution, and verification of coded algorithms, among many other purposes

Fuzzy control to improve high-voltage battery power and engine speed control in a hybrid electric vehicle

With the recent emphasis on developing more environmentally friendly and fuel-efficient vehicles, Ford Motor Company developed a full hybrid electric vehicle (HEV) with a power-split hybrid powertrain consisting of an integrated motor and a generator. The power-split hybrid consists of two powertrains; an engine and an electric drive system. This powertrain provides a great potential to improve fuel economy in part due to its ability to operate engine at efficient regions independent of the vehicle speed. The engine speed determination in such a system depends on the desired high voltage (HV) battery power and the driver demand (driver torque/power request). Clearly, in order to control HV battery power to a desired power, a sophisticated controls system is essential which controls engine power to achieve the desired HV battery power. The desired engine power in turn determines the desired engine speed. It is essential that engine speed operation is smooth and stable with an acceptable response. Use of a classical proportional-integral (PI) based control system to control HV battery power is limited due to the nonlinear behavior of the powertrain, and results either in an undesired engine speed stability behavior under certain driving conditions or degraded response time. This paper presents a new nonlinear controls scheme based on a fuzzy controller to resolve the undesired engine speed behavior while achieving desired engine speed response and improved high-voltage battery power controls. Simulations are conducted with this controller and results show that the proposed fuzzy controller improves HV battery power controls and thereby the engine speed behavior and response time (e.g., no overshoots, improved settling time, and uncompromised rise time).