Dynamic Hybrid Simulation

Dynamic Hybrid Simulation Comparison of different approaches in HEV-modeling GT-SUITE Conference 12. September 2012, Frankfurt/Main Institut für Verbrennungsmotoren und Kraftfahrwesen Universität Stuttgart Florian Winke Prof. Dr. Michael Bargende

Agenda 1. Project Goals 2. Drivetrain Modeling 4. Summary 2

1. Project Goals Investigation of the necessary complexity for dynamic HEV drivetrain simulations How much dynamic behavior is needed? Comparison of different approaches using the example of an urban HEV concept Optimal dimensioning of battery, motor, engine and transmission Predictions of the CO 2 -Potentials of such systems Quantification of different influences: ICE Start-Strategy, Exhaust Aftertreatment and NVH 4

1. Project Goals Boundary conditions Investigation of P2-Hybrid Parallel HEV with 2 clutches Calculation of consumption but not pollutant emissions Layout of 35 kw 2 cyl. engine concept with 1D GT-SUITE model Engine-Maps from simulations Reference-Vehicle Mitsubishi i-miev Focus of the investigations on urban operation 5

2. Drivetrain Modeling Kinematic vs. Dynamic Drivetrain Modeling Flow of information for kinematic simulation Driving Cycle Driving Cycle Vehicle Final Gearbox Clutch (Road) Drive v h n T n T n T n T Vehicle Final Gearbox Clutch (Road) Drive v h n T n T n T n T Engine Engine Flow of information for dynamic simulation Δv Δv Driver Driver T Driving Cycle T v Vehicle n T Final n T Gearbox n T Clutch n T (Road) Drive Engine T 7 Driving Cycle T T v v Vehicle n n T T Final n n T T Gearbox n n T T Clutch n n T T (Road) Drive T v n T n T n T n T Engine

2. Drivetrain Modeling Comparison of the two approaches Creation of kinematic and dynamic Model Investigation of different vehicle-setups with both models in different Driving Cycles (NEDC, Artemis Urban) and comparison of results Variation of Hybridization Rate (HR) by scaling electric components Electric machine, battery, power electronics HR P ICE EM EM No equivalence of installed drive power P P 8

2. Drivetrain Modeling Kinematic vs. Dynamic Simulation NEDC HR-Variation results in optimum at identical configuration HR P ICE P EM Difference: ΔCO 2 = 1.8 g/km ΔCO 2 = 2.1 % P EM 9

2. Drivetrain Modeling Kinematic vs. Dynamic Simulation Artemis Urban Big relative and absolute differences Optimum not at the same HR Difference: ΔCO 2 = 18.3 g/km ΔCO 2 = 15.2 % ΔHR = 0.1 10

2. Drivetrain Modeling Kinematic vs. Dynamic Simulation Results Real-World Driving Cycle shows the limits of the kinematic model Optimization of vehicle configuration with kinematic model in real-world driving cycle leads to big relative and absolute differences and optimum at wrong configuration Kinematic Map-Based Dynamic Map-Based??? Mean Value GT-Power FRM??? 11

Quasi-Static vs. Dynamic Battery Modeling Quasi-Static Quasi-Static Battery Modeling Battery Modeling Dynamic Dynamic Battery Modeling Battery Modeling C 1 C 1 R i R i R 1 R 1 V OC V OC V Batt VR Batt 0 R 0 V Cell V Cell V OC V OC I Batt I Batt I Cell I Cell 13 Battery losses are calculated by one single inner resistance No dynamic effects Additional RC-Circuit Dynamic effects in losses and load can be taken into account

Granularity of battery modeling For the quasi-static battery modeling, two different options are compared: 1. One single inner resistance map for discharging and charging mode of the battery 2. Two separate inner resistance maps for discharging and charging mode of the battery Thermal model included only in dynamic model 1. Quasi-Static (1-Map) 2. Quasi-Static (2-Map) 3. Dynamic Complexity of Battery Model 14

Comparison of Battery Models Artemis Urban 15

Battery Losses Quasi-Static 1-Map vs. Dynamic NEDC Electrical power at battery clamps almost identical (control strategy) Differences in power loss Battery model shows distinct dynamic behavior Causes: 1. Voltage (RC-Circuit) 2. Inner resistance 16

Behavior of Battery Temperature Dynamic requirements result in significantly higher increase in battery temperature Relevance regarding battery losses: Static inner resistance decreases by 10 % 17

Comparison of total losses during driving cycles Considerable differences between driving cycles Significant differences between the battery models 18

Comsumption results with battery models Moderate differences in quasi-static NEDC vs. significant differences in dynamic Artemis Urban Cycle Dynamic modeling of battery results in lower consumption or CO 2 -emission values No significant differences between quasi static models 19

4. Summary Dynamic Hybrid Simulation Dynamic Drivetrain Modeling Dynamic Simulation Model required for dynamic driving cycle (And thus generally, transition (dyn. - stat.) not clearly presentable) Kinematic Simulation Model can be used within restrictions for driving cycle with very low dynamic requirements (NEDC) Dynamic Battery Modeling Complexity of battery model has significant influence on comsumption and CO 2 -emission values Dynamic Battery Model with thermal model required to create predictive vehicle model 21

Dynamic Hybrid Simulation Comparison of different approaches in HEV-modeling GT-SUITE Conference 12. September 2012, Frankfurt/Main Institut für Verbrennungsmotoren und Kraftfahrwesen Universität Stuttgart Florian Winke Prof. Dr. Michael Bargende