Abstract
The key motivation for Hybrid Electric Vehicles (HEV) is fuel consumption optimisation, but the impact of their control strategies on emissions can be very strong, both positive and negative. Indeed, the Energy Management Problem (EMP) in HEVs consists in optimally splitting power demands among the available energy sources (i.e. batteries and fuel) such that consumption and/or emissions are minimised. While fuel consumption minimisation has been widely studied, simple map-based emissions simulations which assume a quasi-static engine behaviour have been mostly used for emission assessment. This tends to underestimate emissions, and can be particularly wrong in the case of switching strategies, as for most automatic engine start&stop systems, but also for optimal solutions arising from the fuel efficiency minimisation problem solved by minimisation of the total energy costs. This paper proposes an Engine-in-the-Loop-based HEV model for optimal control strategies evaluation which does not require emissions and fuel consumption models for the evaluation. For this purpose, a dynamic forwards (i.e. causal) non-linear power train is modelled, calibrated wit dynamic measurements from corresponding production vehicle. Then, a real engine is placed within the simulation, allowing to measure pollutants and fuel consumption directly from test bench sensors. Thus, a real-time capable simplified linear HEV control strategy is evaluated with real driving cycles as well as offline methods like dynamic programming for benchmarking purposes. The experimental effect of optimally solving the fuel minimisation EMP on pollutant emissions is also considered and evaluated for different scenarios.
| Original language | English |
|---|---|
| Title of host publication | 7th IFAC Symposium on Advances in Automotive Control |
| Pages | 29-34 |
| Number of pages | 6 |
| Volume | 7 |
| DOIs | |
| Publication status | Published - Sept 2013 |
Publication series
| Name | IFAC Proceedings Volumes (IFAC-PapersOnline) |
|---|---|
| Number | 21 PART 1 |
| Volume | 46 |
| ISSN (Print) | 1474-6670 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Fields of science
- 206002 Electro-medical engineering
- 207109 Pollutant emission
- 202 Electrical Engineering, Electronics, Information Engineering
- 202027 Mechatronics
- 202034 Control engineering
- 203027 Internal combustion engines
- 206001 Biomedical engineering
JKU Focus areas
- Mechatronics and Information Processing
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