Integrated, Emission Optimized Hybrid Operating Strategy Development Through a Novel Testing Methodology
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- Provide download link
- Format
- Price
- Non-members (tax incl.)：￥6,600 Members (tax incl.)：￥5,280
- Paper/Info type
- SAE Paper
No.2021-24-0106
- Pages
- 1-14(Total 14 p)
- Date of publication
- Sep 2021
- Publisher
- SAE International
- Language
- English
- Event
- International Conference on Engines and Vehicles 2021
Detailed Information
| Author(E) | 1) Tim Steinhaus, 2) Maximilian Stumpp, 3) Christian Beidl |
|---|---|
| Affiliation(E) | 1) Technical University of Darmstadt, 2) Technical University of Darmstadt, 3) Technical University of Darmstadt |
| Abstract(E) | Even under consideration of the increasing dynamics of measures to reduce CO2 in the transport sector and the resulting, now visible changes in the development and registration of new passenger cars (electrification), it is anticipated that vehicle drives containing an internal combustion engine will continue to have significant market shares in the medium to long term. It is assumed that a significant proportion of these cars will be hybrid vehicles in the future. As a result, in order to implement future requirements for improved air pollution control (Post EU6/Zero Impact) and CO2 reduction, consideration of these aspects must be an integral part of the application and of development activities in general. At TU Darmstadt, a consistent method for the development of powertrains with regard to their relevant real-world driving emissions - the Most Relevant Testing Procedure (MRTP), was established. This paper demonstrates how the described method can be used for the integrated application of an operating strategy for hybrid vehicles. For this purpose, the MRTP is first run through and explained with a C-segment P2-hybrid vehicle serving as a use-case. Subsequently, it is shown how the knowledge generated during the course of this process can be utilized to incorporate it into the operating strategy. This is achieved using an adaptive equivalent consumption minimization strategy (A-ECMS), in which emission-critical states, which are derived through machine-learning principles, are included into the ECMS-defining cost function. The special feature of this approach is that the derived, basic operating strategy is not generated specifically for the individual use-case, but as part of general powertrain characterization process without additional application effort. In this way, an emission-minimizing basic operating strategy is created automatically, so to speak, under relevant operating conditions. The emission reduction potential of the method are demonstrated for the selected use case. |
