Nikolce Murgovski
Chalmers University of Technology, Signals and Systems, Faculty Member
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The electrification of vehicles plays an important role in the reduction of energy consumption and pollutant emissions of ground transportation. With the goal of improving energy efficiency and employing renewable energy sources, vehicle... more
The electrification of vehicles plays an important role in the reduction of energy consumption and pollutant emissions of ground transportation. With the goal of improving energy efficiency and employing renewable energy sources, vehicle manufactures are currently introducing several types of electrified vehicles. Competing concepts introduced to the market are electric vehicles (EVs), hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), fuel cell vehicles (FCVs), fuel cell hybrid vehicles (FCHVs), etc. With the introduction of new vehicle concepts, vehicle manufacturers also encounter new computational challenges, such as optimal sizing of powertrain components and optimal arbitration of traction power among multiple power sources. This work presents a convex programming framework for the combined design and control optimization of electrified vehicles. The key element is the convex modeling of powertrain components, such as internal combustion engine, electric machine, engine-generator unit, fuel cell system, electric battery, electric capacitor, etc. A complete Matlab code is provided that addresses realistic vehicle design and control problems.
The library of optimization examples in Matlab can be found at
https://chalmersuniversity.app.box.com/cones
The library of optimization examples in Matlab can be found at
https://chalmersuniversity.app.box.com/cones
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ABSTRACT We present a method for automated engine calibration, by optimizing engine management settings and power-split control of a hybrid electric vehicle (HEV). The problem, which concerns minimization of fuel consumption under a NOx... more
ABSTRACT We present a method for automated engine calibration, by optimizing engine management settings and power-split control of a hybrid electric vehicle (HEV). The problem, which concerns minimization of fuel consumption under a NOx constraint, is formulated as an optimal control problem. By applying Pontryagin’s maximum principle, this paper shows that the problem is separable in space. In the case where the limits of battery state of charge are not activated, we show that the optimization problem is also separable in time. The optimal solution is obtained by iteratively solving the power-split control problem using dynamic programming or the equivalent consumption minimization strategy. In addition, we present a computationally efficient suboptimal solution, which aims at reducing the number of power-split optimizations required. An example is provided concerning optimization of engine management settings and power-split control of a parallel HEV.
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ABSTRACT We present a method for obtaining a computationally efficient, sub-optimal energy management of an electrified vehicle containing a planetary gear set. We first reformulate the optimization problem to become separable in space... more
ABSTRACT We present a method for obtaining a computationally efficient, sub-optimal energy management of an electrified vehicle containing a planetary gear set. We first reformulate the optimization problem to become separable in space (optimization variables). The problem is then decomposed into two optimization problems. The first is a static problem that looks for the optimal engine speed that maximizes efficiency of a compound unit, resembling an engine-generator unit combining the planetary gear and kinetic energy converters connected to it. The second is a dynamic optimization problem deciding the optimal power split between an electric buffer and the compound unit. By approximating the losses of the compound unit as convex, second order polynomial in generated power, we are able to solve the power split problem in less than 2 seconds, when the engine on/off sequence is known in advance. By comparing results with dynamic programming, we observed an approximation error of less than 0.2 %.