About the hand-in tasks. Vehicle Propulsion Systems Lecture 3. Outline. The Vehicle Motion Equation. Energy consumption for cycles

Transcription

1 About the han-in tasks Vehicle Propulsion Systems Lecture 3 Internal Combustion Engine Powertrains Vehicle Energy System Lars Eriksson Associate Professor (Docent) Vehicular Systems Linköping University November 1, 2012 General avice Prepare yourselves before you go to the computer Make a plan (list of tasks) Han-in Format We woul prefer (not a eman): Electronic han-in Report in PDF-format Reasons: Easy for us to comment Will give you fast feeback 2 / 40 3 / 40 The Vehicle Motion Equation Newtons secon law for a vehicle t v(t) = F t(t) (F a (t) + F r (t) + F g (t) + F (t)) F t tractive force F a aeroynamic rag force F r rolling resistance force F g gravitational force F isturbance force Fr α Fg mv g Fa Ft F 4 / 40 5 / 40 Energy consumption for cycles Two Approaches for Powertrain Simulation Dynamic simulation (forwar simulation) Cycle Driver Engine Transm. Wheel Vehicle Numerical values for MVEG-95, ECE, EUDC air rag = 1 rolling resistance = 1 kinetic energy = 1 v 3 i h = {319, 82.9, 455} vi h = {.856, 0.81, 0.88} āi vi h = {0.101, 0.126, 0.086} ĒMVEG-95 Af c mv cr mv 10 kj/100km Normal system moeling irection Requires river moel Quasistatic simulation (inverse simulation) Cycle Vehicle Wheel Reverse system moeling irection Follows riving cycle exactly Transm. Engine 6 / 40 7 / 40 QSS Toolbox Quasistatic Approach IC Engine Base Powertrain The [ Vehicle Motion ] Equation With inertial forces: + γ2 J rw 2 e + 1 J rw 2 w t v(t) = γ Te (Fa(t) + Fr (t) + Fg (t) + F(t)) rw 8 / 40 9 / 40

2 Primary Energy Sources Primary sources Different options for onboar energy storage Powertrain energy conversion uring riving Cut at the wheel! Driving mission has a minimum energy requirement. Few sources But many options Oil, Natural Gas, Coal Oil wells as we know them will be eplete Still much usable carbon in the groun Cost will increase Nuclear power Fission material available Fusion material available Solar power Hyro, win, wave power Solar cell electricity Crop, forest, waste Bacteria 10 / / 40 Energy Carriers for On-Boar Storage Energy carriers Many possibilities Diesel, Gasoline, Naphtha,... CH4, Compresse Natural Gas (CNG), Liquefie Petr. Gas (LPG),... CH3OH, C2H5OH, C4H9OH, DME,... H2 Batteries What are the esirable properties? High energy ensity Long range High refueling power Fast refueling Simple refueling Low environmental impact (health aspects) Infrastructure Why (Liqui) Hyrocarbons? Excellent energy ensity High refueling power Goo Well-to-Tank efficiency 12 / / 40 Why (Liqui) Hyrocarbons? Think of the fuel molecules as a wire that pulls the vehicle forwar. How thick is the fuel wire? kg car nees 6 liters per 100 km. Area = 0.006/ = 6e-8 m 2 D = 6e 8 4/pi 0.3 mm A kg truck nees 30 liters per 100 km. Area = 0.03/ = 3e-7 m 2 D = 3e 7 4/pi 0.6 mm Chemical bons are strong! 14 / 40 Why (Liqui) Hyrocarbons? Filling a car at the gas station. filling the tank with 55 [m 3 ] of gasoline takes about 1 minute an 55 secons What is the power? The heating value for isooctane is q LHV = 44.3 [MJ/kg], an the ensity is ρ = 0.69 [kg/m 3 ]. Gives the power Q = MJ 115 s = 14.6 [MW] (Perspective: Worls biggest win turbine is 7.58 MW. Enercon E-126, rate capacity 7.58 MW, height 198 m (650 ft), iameter 126 m.) What is the current? For a single line 240 V system this woul mean A! (Perspectives: 0.2 A kills a human. Resiential house, 3*16 A.) We have a challenge in fining a replacement for the fuel! 15 / 40 Upstream Energy Conversion Energy Conversion in Vehicles Manufacturing (pumping, crop,... ). Transport to refinery Refining Transport to filling station Filling of Vehicle Ongoing intense research Investigating energy paths an improving all processes. Many paths in the vehicle Energy storage(s) (tank, battery, super caps) Energy refiner (reformer) Energy converter(s) Power (force) to/from transportation mission This important topic will be covere later in the course 16 / / 40

3 W2M Energy Paths 18 / / 40 W2M Conventional Powertrains W2M Electric Vehicle 20 / / 40 W2M Fuel Cell Electric Vehicle Improvements on the big scale Well-to-tank (Upstream) Wheel-to-miles (Car parameters: mass, rolling, aeroynamics) Tank-to-wheel Improvements in Tank-to-wheel efficiencies Peak efficiency of the components Part loa efficiency Recuperate energy Optimize structure Realize supervisory control algorithms that utilize the avantages offere in the complex systems 22 / / 40 Primary sources Different options for onboar energy storage Powertrain energy conversion uring riving Cut at the wheel! Driving mission has a minimum energy requirement. 24 / / 40

4 Performance an riveability Top Spee Performance Starting point The vehicle motion equation. Important factors for customers Not easy to efine an quantify For passenger cars: Top spee Maximum grae for which a fully loae car reaches top spee Acceleration time from stanstill to a reference spee (100 km/h or 60 miles/h are often use) t v(t) = F t 1 2 ρ a A f c v 2 (t) g c r g sin(α) At top spee t v(t) = 0 an the air rag is the ominating loss. power requirement (F t = Pmax v ): P max = 1 2 ρ a A f c v 3 Doubling the power increases top spee with 26%. 26 / / 40 Uphill Driving Starting point the vehicle motion equation. t v(t) = F t 1 2 ρ a A f c v 2 (t) g c r g sin(α) Assume that the ominating effect is the inclination (F t = Pmax v ), gives power requirement: P max = v g sin(α) Improve numerical results require a more careful analysis concerning the gearbox an gear ratio selection. Acceleration Performance Starting point: Stuy the buil up of kinetic energy E 0 = 1 2 v 2 0 Assume that all engine power will buil up kinetic energy (neglecting the resistance forces) Average power: P = E 0 /t 0 A hoc relation, P = 1 2 P max Assumption about an ICE with approximately constant torque (also incluing some non accounte losses) P max = v 2 t 0 28 / / 40 Acceleration Performance Valiation Publishe ata an P max = mv v 2 t 0 30 / / 40 Optimization problems Different problem types occur in vehicle optimization Structure optimization Parametric optimization Control system optimization 32 / / 40

5 Driving cycle specification Gear ratio Path to the solution Implement a simulation moel that calculates m f for the cycle. Set up the ecision variables i g,j, j [1, 5]. Set up problem min m f (i g,1, i g,2, i g,3, i g,4, i g,5 ) s.t. moel an cycle is fulfille (1) Gears specifie but ratios free. How much can change gear ratios improve the fuel economy? Use an optimization package to solve (1) Analyze the solution. 34 / / 40 Moel implemente in QSS Structure of the coe Conventional powertrain. Efficient computations are important. Will use a similar setup in han-in assignment / / 40 Running the solver Running the solver Improves the fuel consumption with 5%. Improvements of 0.5% are worth pursuing. Complex problem, global optimum not guarantee. Several runs with ifferent initial guesses. 38 / / 40