5th International Seminar on ORC Athens, 09/09/2019 ASSESSING FUEL CONSUMPTION REDUCTION OF REVERCYCLE A REVERSIBLE MOBILE AIR CONDITIONING/ ORGANIC RANKINE CYCLE SYSTEM Luca Di Cairano1, Wissam Bou Nader2, Florent Breque1, Maroun Nemer1 luca.di_cairano@mines-paristech.fr 1MINES ParisTech, PSL Research University, Center for energy Efficiency of Systems, Palaiseau, FR 2Groupe PSA, Centre technique de Vélizy, Vélizy, FR

Outline Context Global warming Engine energy balance ORC in a passenger car ReverCycle The concept Operating modes Fuel economy Global dynamic model of the vehicle ORC model Optimal speed ratios in ORC mode ReverCycle fuel economy: methodology MAC activation time ReverCycle fuel economy: results Conclusions Future Work References

Global warming In Europe Passenger cars are responsible for 60% of the total transport sector emissions. (Source: European Environment Agency) Regulators are imposing strict emission limits to car manufacturers Transport sector CO2 emissions Time

Engine Energy Balance Rule of thumb Waste Heat Brake Power Due to this important amount of available energy Waste Heat Recovery systems have gained major interest. TEG Turbocompounding Thermoacoustic Rankine Cycle

CES concept: ReverCycle ORC in a passenger car In a passenger car: compactness requirements dynamic working conditions strong attention to system cost Automotive ORC development implies: A low cost and compact design Assessment of fuel economy on dynamic working conditions CES concept: ReverCycle

Outline Context Global warming Engine energy balance ORC in a passenger car ReverCycle The concept Operating modes Fuel economy Global dynamic model of the vehicle ORC model Optimal speed ratios in ORC mode ReverCycle fuel economy: methodology MAC activation time ReverCycle fuel economy: results Conclusions Future Work References

The concept MAC ORC Two mutualized components: ReverCycle = MAC +pump + heat exchanger + valves Reversible machine Standard MAC condenser Waste heat source: Engine Coolant Exergy R134a/R1234yf Automotive scroll compressor can be easily converted into a reversible machine

ReverCycle operating modes MAC ORC ReverCycle can operate as a MAC or as an ORC. Switching between modes: 2 automatic valves. ORC pump and compressor/expander are mechanically coupled to engine shaft. (Reversing gearbox for expander)

Outline Context Global warming Engine energy balance ORC in a passenger car ReverCycle The concept Operating modes Fuel economy Global dynamic model of the vehicle ORC model Optimal speed ratios in ORC mode ReverCycle fuel economy: methodology MAC activation time ReverCycle fuel economy: results Conclusions Future Work References

Global dynamic model of the vehicle Step by step development of a vehicle model. Powertrain model (Table based engine) Engine cooling system model (Table based cabin heater)

ReverCycle adds power to the engine shaft ORC model ThermoCycle Library (Quoilin et al, 2014) + CoolProp Library (Bell et al, 2014) ReverCycle adds power to the engine shaft = Fuel economy

Optimal speed ratios in ORC mode ORC average cycle efficiency as a function of speed ratios. A speed ratio has to be defined: Between pump and engine shaft Between expander and engine shaft   Pump/Engine 0.6 Pump/Engine 1 Pump/Engine 1.4 Expander/Engine 0.24 4.2 % 4.8 % 4.1 % Expander/Engine 0.4 2.5% 5 % Expander/Engine 0.56 2 % 3.9 % 4.2% The final choice is a compromise between cycle efficiency and average expander inlet vapor quality (avoid two phase expansion)

ReverCycle fuel economy: Methodology Reference driving cycle: WLTC Two initial conditions: Hot start: engine temperature is 85°C Cold start: engine temperature is equal to ambient temperature ReverCycle (ORC) is strongly affected by ambient temperature : Engine warm up ORC efficiency Waste heat energy is limited by cabin heater demand ORC availability is limited by MAC activation time The global vehicle model takes into account these effects

MAC activation time A cabin thermal model (Benouali, 2002) is coupled to a thermal comfort model (Fanger, 1982) to provide the percentage of drivers that will turn on the MAC entering the cabin after one hour thermal soak. Cabin model validation on (Marcos et al, 2014) exp. results City MAC activation time Paris 21% Moscow 16% Valencia 41% Brasilia 59% Annual simulation on 4 different climatic regions (Paris, Moscow, Valencia, Brasilia).

ReverCycle fuel economy: Results Fully available ORC +20% Fuel economy (E.g. Paris Hot Start 2% ->2.4%) 2 x Weight Higher cost City HOT START Fuel economy COLD START Paris 2% 1.3% Moscow 1.7% 1.1% Valencia 1.2% Brasilia 1% Best climatic region

Outline Context Global warming Engine energy balance ORC in a passenger car ReverCycle The concept Operating modes Fuel economy Global dynamic model of the vehicle ORC model Optimal speed ratios in ORC mode ReverCycle fuel economy: methodology MAC activation time ReverCycle fuel economy: results Conclusions Future Work References

Conclusions ReverCycle is a low cost and compact solution. This study was able to: Assess ReverCycle ORC mode availability (≈80% in a temperate region, ≈ 40% hot region) Assess the annual average fuel economy on a WLTC ( 1-2% fuel economy) Define the best climatic region for ReverCycle ROI (temperate region)

Future Work Add an ejector to ReverCycle MAC/ORC/ERC To increase WHR activation time ReverCycle Proof of Concept MAC ORC ERC

References Bell, I. et al. (2014) , ‘ Pure- and Pseudo-Pure Fluid Thermophysical Property Evaluation and the Open-Source Thermophysical Property Library CoolProp’, Ind. Eng. Chem. Res., 53, pp. 2498-2508. Benouali, J., 2002, Etude et minimisation des consommations des systèmes de climatisation automobile, PhD diss., Ecole des Mines de Paris. Fanger. P.O., 1982, Thermal Comfort. Malabar FL: Robet E. Krieger Publishing Company Marcos, D. et al. (2014) ‘The development and validation of a thermal model for the cabin of a vehicle’, Applied Thermal Engineering. Elsevier Ltd, 66(1–2), pp. 646–656. doi: 10.1016/j.applthermaleng.2014.02.054. Quoilin, S. et al. (2014) , ‘ThermoCycle: A Modelica library for the simulation of thermodynamic systems’, Proceedings of the 10th International Modelica Conference, pp. 683-692.