Models and control methodologies for ic engine idle speed control design

With the increased emphasis on improving fuel economy and reducing emissions, hybrid electric vehicles (HEVs) have emerged as very strong candidates to achieve these goals. The power-split hybrid system, which is a complex hybrid powertrain, exhibits great potential to improve fuel economy by determining the most efficient regions for engine operation and thereby high-voltage (HV) battery operation to achieve overall vehicle efficiency optimization. To control and maintain the actual HV battery power, a sophisticated control system is essential, which controls engine power and thereby engine speed to achieve the desired HV battery maintenance power. Conventional approaches use proportional-integral (PI) control systems to control the actual HV battery power in power-split HEV, which can sometimes result in either overshoots of engine speed and power or degraded response and settling times due to the nonlinearity of the power-split hybrid system. We have developed a novel approach to intelligently controlling engine power and speed behavior in a power-split HEV using the fuzzy control paradigm for better performances. To the best of our knowledge, this is the first reported use of the fuzzy control method to control engine power and speed of a power-split HEV in the applied automotive field. Our approach uses fuzzy gain scheduling to determine appropriate gains for the PI controller based on the system's operating conditions. The improvements include elimination of the overshoots as well as approximate 50% faster response and settling times in comparison with the conventional linear PI control approach. The improved performances are demonstrated through simulations and field experiments using a ford escape hybrid vehicle.

Fuzzy Gain-Scheduling Proportional–Integral Control for Improving Engine Power and Speed Behavior in a Hybrid Electric Vehicle

A systematic fuzzy logic control design method for control of automotive engine idling speed is discussed. The method uses the direct intelligent control paradigm. The procedure is based on partitioning of the state space into small rectangles called cell groups, and quantization of the states and the available controls into finite levels or bins. Membership functions are then assigned for the state and controls. The transition from one conditional subspace to another is accomplished via a center-point mapping of the cell groups, under the applied action of each if-then rule. The systematic aspects of the procedure refer to the way transitional elements are clustered on the basis of an explicitly defined performance measure. Optimum transitions are selected using a search procedure. The fuzzy rules are generated automatically for transitioning from any initial cell group to the target cell group. A simplified engine model for idling speed control is used as the testbed for development and demonstration purposes. >

Fuzzy logic control of an automotive engine

In this paper, the engine idle speed control (ISC) problem is revisited with the l/sub 1/ optimization design methodology. A controller configuration consisting of a feedback linear quadratic Gaussian (LQG) optimal controller and a feedforward l/sub 1/ optimal controller is chosen to satisfy the design objectives. The main goal of this work is to demonstrate the application of the l/sub 1/ optimal control methodology on a standard automotive control problem and evaluate the advantages and disadvantages of the l/sub 1/ design method. Using the idle bypass valve as the only actuator, the l/sub 1/ optimal feedforward control is shown to be effective in reducing the idle speed errors at the expense of extra control energy being spent during the transient. The controller design process is described along with the simulation and experimental results on a 4.6L 2 valve V8 production engine. A method to address tradeoff issues in evaluating the performance of a feedforward control system is also discussed.

Application of l/sub 1/ optimal control to the engine idle speed control problem

A type of fuzzy inference coprocessor in which active rules can be detected from a set of arbitrary fuzzy rules was designed In this architecture, referred to as the flexible active-rule-driven architecture, active rules are detected for various fuzzy rules and for various membership functions The fuzzy inference scheme and the concept of active rules are reviewed It is shown that active rules can be defined for different fuzzy inference methods The main features of the flexible active-rule-drive architecture are described The basic function of the coprocessor was confirmed using a logic simulator Other features of the coprocessor, such as arithmetic-unit calculation of membership grades, are also discussed The circuit simulations of the coprocessor and its implementation are outlined >

Norberto Dosio

Papers

Use of fuzzy logic for engine idle speed control