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The software design of this new engine control unit is based on a unique and homogenous torque structure. All input signals are converted into torque equivalents and a torque coordinator determines their influence on the final torque delivered to the powertrain. The basic torque structure is independent on the type of fuel and can be used for gasoline, diesel, or CNG injection systems. This allows better use of custom specific algorithms and facilitates reusability, which is supported by the graphical design tool that creates all modules using automatic code generation. Injection specific algorithms can be linked to the software by simply setting a software switch.

The powertrain electrification is currently not only taking place in public road mobility vehicles, but is also making its way to the racetrack, where it’s driving innovation for developments that will later be used in series production vehicles. The current development focus for electric vehicles is the balance between driving power, range and weight, which is given even greater weighting in racing. To redefine the current limits, IAV developed a complete e-powertrain for a racing MX motorcycle and integrated it into a real drivable demonstrator bike. The unique selling point is the innovative direct phase-change cooling (PCC) of the three-phase e-motor and its power electronics, which enables significantly increased continuous power (Pe = 40 kW from 7,000 rpm to 9,000 rpm) without thermal power reduction. The drive unit is powered by a replaceable Lithium-Ion round cell battery (Ubat,max = 370V) with an energy storage capacity of Ebat = 5 kWh.

In this paper, a nonlinear Hammerstein model was used to represent the dynamic behavior of an electronic throttle body at different operating conditions. The structure of the Hammerstein model was nonlinear in its parameters. It consisted of a static nonlinear function representing the coulomb friction and limp-home return springs in series with dynamic piecewise-linear transfer functions. The mathematical modeling of the throttle body was derived in state-space discrete form. Separable least squares estimation and optimization methods were implemented as a means of simultaneously estimating and identifying both the linear and nonlinear elements to match the results obtained from the simulation of the nonlinear Hammerstein model and the experimental tests.

In this paper, we present an estimation of the coulomb friction and return spring effects in an automotive electronic throttle control (ETC) system using a nonlinear model-based estimator. The non-linear model-based estimator smoothly estimates this static non-linear behavior based on a priori knowledge of the feedback signals of the position error and the angular velocity of the throttle plate. Extensive simulations showed that the estimator sufficiently predicts the actual static non-linear behavior. The performance of the estimator was compared to an approximation based on the experimental nonlinear characteristics of the throttle. The non-linear model-based estimator can be used for compensation and can cancel the effect of the static nonlinearity in the throttle actuator to improve throttle position control.

The purpose of this paper is to use a multilayer perceptron neural network model to identify and control a non-linear electronic throttle system. The neural network model, which represents the dynamic behaviour of the non-linear throttle servo system, was first identified at different operating conditions. The neural network controller model was then designed (or trained) with the throttle identifier network model, so that the tracking control position of the throttle system follows a reference model. The neural network controller training is computationally expensive and requires the use of the dynamic backpropagation algorithm, which is significantly time consuming during on-line implementation. For this reason, the throttle identifier network model is used to assist in training the neural controller in off-line mode. The neural network controller was trained with the same inputs that are fed to the actual throttle system to produce the same output.

This paper describes the approaches, possibilities and benefits of using artificial Neural Networks for Engine Management Systems with the focus on new calibration methods and control strategies. Utilizing this approach in the calibration and function algorithm development process makes it possible to fulfill shorter development time frames for series production intended projects. Additionally, this technology helps to develop new control and diagnostic strategies for new Engine Management Systems to meet the upcoming stricter emission and OBD regulations.

The Electronic Throttle Control (ETC) system presented in this paper combines gain-scheduled Proportional, Integral and Derivative (PID) feedback control with feed-forward compensation of throttle plate friction. The non-linear model-based friction compensator was integrated along with the PID controller as a TargetLink block in the IAV Electronic Control Unit (ECU) engine controller software, implemented on a rapid prototype real-time system. The best gains for the PID controller were determined on-line using our Computer-Aided Calibration (CAC) methods. All experimental results revealed adequate tracking and satisfied requirements for both controller performance and cost. The control structure with friction compensation was robust and simple to implement. The influence of the throttle control on vehicle performance during slow and fast maneuvers is presented.