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MIMO Control of a Turbogenerator for Energy Recovery
Technical Paper
2020-01-0261
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Market trends for increased engine power and more electrical energy on the powergrid (3kW+), along with customer demands for fuel consumption improvements and emissions reduction, are driving requirements for component electrification, including turbochargers. GTDI engines waste significant exhaust enthalpy; even at moderate loads the WG (Wastegate) starts to open to regulate the turbine power. This action is required to reduce EBP (Exhaust Back Pressure). Another factor is catalyst protection, where the emissions device is placed downstream turbine. Lambda enrichment or over-fueling is used to perform this. However, the turbine has a temperature drop across it when used for energy recovery. Since catalyst performance is critical for emissions, the only reasonable location for an additional device is downstream of it. This is a challenge for any additional energy recovery, but a smaller turbine is a design requirement, optimized to operate at lower pressure ratios. A WAVE model of the 2.0L GTDI engine was adapted to include a TG (Turbogenerator) and TBV (Turbine Bypass Valve) with the TG in a mechanical turbocompounding configuration, calibrated with steady state dynamometer data. This includes power and fuel consumption, and additionally a sensitivity analysis and knock impact assessment. Further work includes transient verification with WAVE-RT on WLTP and RDE drive cycles, estimating dynamic energy recovery, assessing electrical turbocompounding, interfacing to the powergrid, and calibration optimisation, using combined WG and TBV settings. Development of more advanced MIMO (Multiple-Input, Multiple-Output) control system algorithms and prototype testing on dynamometer or vehicle could be performed to verify design assumptions and simulation results.
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Petrovich, S., Ebrahimi, K., Kalantzis, N., and Pezouvanis, A., "MIMO Control of a Turbogenerator for Energy Recovery," SAE Technical Paper 2020-01-0261, 2020, https://doi.org/10.4271/2020-01-0261.Data Sets - Support Documents
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