FROST: Fuel Reduction - Optimal Strategy and Toolbox

Reference number
Coordinator Scania CV Aktiebolag - Avd NEPP
Funding from Vinnova SEK 6 500 000
Project duration October 2017 - December 2020
Status Ongoing
Venture Electronics, software and communication - FFI
Call 2016-04638-en

Purpose and goal

In the pursuit of lower fuel consumption, CO2 emissions, and exhaust emissions, the powertrain in commercial vehicles is becoming increasingly complex. For example, the development of advanced after treatment systems and different types of electrification is ongoing. To take full advantage of the complex powertrain, management strategies are needed that effectively integrate the different systems. Due to the complexity, the most energy efficient control strategy is difficult to devise. This project will determine such control strategies using optimal control theory.

Expected results and effects

The project is expected to develop tools and methods for modeling, simulation and optimization of control strategies for vehicles with advanced powertrains. The methods will be applied to industrial problems, and the goal is to reduce fuel consumption through more efficient interactions between internal combustion engine and electric motor, more efficient exhaust after treatment in hybrid vehicles and better cooperation in vehicle platoons involving electrified vehicles. A fuel saving of about 2% is estimated to be achievable compared with rule-based control.

Planned approach and implementation

The project is planned to be conducted in two parts, one academic part conducted by two PhD students and one industrial part conducted by employees at Scania. The PhD students will work on developing methods and techniques for formulating and solving optimal control problems, which will be applied to relevant industry problems by the Scania team. In this way, knowledge from the research world is brought to users in industry, while the industry dialogue provides important feedback to the university on research.

The project description has been provided by the project members themselves and the text has not been looked at by our editors.

Last updated 8 January 2019

Reference number 2016-05380

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