Michael Back

Predictive Powertrain Control for Heavy Duty Trucks

Abstract A truck driver controls his vehicle with the objective of maintaining a desired velocity while keeping the fuel consumption as low as possible. In order to achieve these goals he continuously estimates oncoming operation points of thepowertrain and chooses the inputs (driving torque, brake level, gear) in an optimal manner based on this estimation. A navigation system combined with a 3D digital road map is able to provide information about the roadway not only for the current position but also for a coming distance if the route is known. By imitating the driver, the Model Predictive Control method is used to apply this information for the implementation of a predictive gearshift program and a predictive cruise controller. Thereby in both systems the input variables are determined by the successive solution of discrete/continuous optimal control problems. The present paper first points out the idea of a predictive powertrain control for a heavy duty truck, followed by the description of its algorithmic realization.

PREDICTIVE CONTROL OF DRIVETRAINS

Abstract Telematics allow a prediction of the future driving conditions of a car along some time horizon. This prediction offers a knowledge of the torque request caused by the route ahead and can be used for implementing sophisticated operating strategies for Hybrid Electric Vehicles (HEVs). The intention of the present paper is to describe a method which minimizes the fuel consumption of the system beyond the prediction horizon. Therefore the strategy determines the best operating conditions of the combustion engine and the electric motor with respect to the predicted torque request and the SOC of the battery.

A Real-Time Optimal Control Strategy for Parallel Hybrid Vehicles with On-Board Estimation of the Control Parameters

Abstract The paper presents a novel approach for hybrid powertrain control, based on a real-time minimization of the equivalent fuel consumption. This approach is non-predictive, thus the control strategy developed requires only information on the current status of the powertrain. The control parameters are continuously estimated on-board using information deriving from static route mapping and telemetry. Simulations for a prototypical hybrid car under development show the very promising benefits in terms of fuel consumption reduction and charge sustaining that can be obtained with the controller presented.

Predictive powertrain control for hybrid electric vehicles

Abstract This paper presents the use of telematics information for a predictive powertrain control in hybrid electric vehicles. By estimating the inclination and velocity profile of the road ahead the aggregates of the hybrid drivetrain can be controlled in a fuel-optimising fashion. For this purpose model predictive control is used. As the system is highly nonlinear Bellmans dynamic programming is used for solving the optimisation problem. The main focus of this paper is to describe how the computational effort of a hybrid drivetrain control algorithm can be reduced.

DETERMINATION OF THE FUEL-OPTIMAL TRAJECTORY FOR A VEHICLE ALONG A KNOWN ROUTE

Abstract Integration of topology data of the entire road ahead into the powertrain control unit in combination with hybrid electric powertrains offers a huge potential for optimising the control strategy. The intension of the present paper is to show a methodology for computing the maximum potential of fuel saving over a specified route. For this offline calculation a non-linear state-space model of the longitudinal dynamics is used to find the fuel optimal trajectory using Bellmans Dynamic Programming (DP). As the model is of third order with three control inputs and DP can only be applied to systems with a maximum sum of inputs and states of four, the iterative variant of DP is used. This special method described in this paper allows the solution of optimisation problems with a number of states and inputs up to six, without leading to insufferable computing times.

Implementing an MPC Algorithm in a Vehicle with a Hybrid Powertrain using Telematics as a Sensor for Powertrain Control

Abstract Telematics provide the possibility to predict future driving conditions of a vehicle. When connecting Powertrain Control (PTC) to this flow of information telematics are used as an additional sensor to PTC. Together with the method of Model Predictive Control (MPC) the optimal operating trajectory of a drivetrain with respect to its fuel consumption within a prediction horizon can be determined. This paper shows the implementation of an MPC algorithm in a test vehicle with a mild hybrid which is equipped with a telematic system delivering all available environmental conditions within a prediction horizon. The potential of the MPC algorithm for saving fuel is shown. Furthermore the requirements to a future connection of telematic systems to PTC are discussed from the PTC point of view.

Prädiktive Regelung mit Dynamischer Programmierung für nichtlineare Systeme erster Ordnung (Predictive Control with Dynamic Programming for First Order Nonlinear Systems)

Abstract Dieser Beitrag beschreibt die Einbindung der Dynamischen Programmierung nach Bellman in die Modellbasierte Prädiktive Regelung für nichtlineare Systeme erster Ordnung. Dabei wurden die Besonderheiten der Kombination dieser beiden Verfahren ausgenutzt, um eine Reduktion des Rechenaufwandes zu erzielen. Diese Reduktion wird anhand der Simulation einer prädiktiven Antriebsregelung eines Hybridfahrzeuges demonstriert.

Optimization of Hybrid Strategies with Heuristic Algorithms to Minimize Exhaust Emissions and Fuel Consumption

The hybrid powertrain is a promising concept to contribute to achieve future CO2-targets. This paper describes a method to improve future automotive powertrains efficiently in real world driving conditions. Beside the optimization of the internal combustion engine and the electric components, the operating strategy of the hybrid powertrain is of particular importance to minimize the vehicles fuel consumption. A combination of start/stop operation, downspeeding, load-point shifting and pure electric driving can provide substantial fuel savings compared to conventional powertrains. However, in addition to the fuel consumption the more and more stringent future emission legislation must be taken into the account when optimizing the operating strategy. A fast light-off of the catalytic converters and a control of the converter temperatures during pure electric driving must be achieved. Therefore, numerous parameters have to be optimized simultaneously to realize the best solution for the hybrid powertrain. A numerical optimization approach was used to define the operating strategies efficiently for the mentioned goals. The results of this optimization were compared to the fuel consumption and the exhaust emissions of the conventional powertrain. The potential of a further strategy optimisation could be evaluated. Generally, it could be shown that long phases of electric driving combined with aggressive load point shifting to balance the battery’s state of charge are most favorable in terms of efficiency. The phases of electric driving are additionally limited by the temperature drop of the catalysts and the lack of pollutant conversion after restart. This is a new and innovative approach to develop electrified powertrains efficiently. Finally it can be stated, that the numerical optimization method proved to be a powerful tool to support the development process of hybrid powertrains with numerous degrees of freedom.

Suboptimal Robust Control of Fast Electro-Magnetic Actuators

Abstract Electro-magnetic actuators can provide engine valves with variable valve timing to best suit the prevalent engine speed and load. All electro-magnetic valve actuators, however, suffer at least to some degree from high seating velocities, which result in un desire able audible noise and early fatigue of the valve and valve seat. This problem can be solved by closed-loop control of the valve motion. The controller has to be robust, because of a wide range of pressures in the combustion chamber during valve opening. Additionally the required electrical energy has to be minimal. Therefore the proposed controller is based on variable structure control. The energy-optimal sliding-surfaces are computed by Iterative Dynamic Programming technique. The control signals are calculated by a first-order nonlinear observer.