Energy efficiency assessment in the context of multimodal...
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1 Energy efficiency assessment in the context of multimodal passenger transport: From ‘well’ to ‘wheels’ Konstantinos N. Genikomsakis University of Deusto 1 st MOVESMART Workshop ‐ 15 th October 2015 Bilbao, Spain
Transcript of Energy efficiency assessment in the context of multimodal...
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Energy efficiency assessment in the context of multimodal passenger transport:
From ‘well’ to ‘wheels’
Konstantinos N. GenikomsakisUniversity of Deusto
1st MOVESMART Workshop ‐ 15th October 2015Bilbao, Spain
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AimTo support the mobility of individuals by assisting the traveler to combinevarious means of transportation in an energy‐efficient way
Impact assessment of multimodal routes in relation to the MOVESMART project
Methodological approach“Well‐to‐wheels” analysis of energy consumption/emissions over the operational phase of the life cycle
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Challenges in relation to MOVESMART objectives
• Scientific:– Consideration of traffic conditions
– Inclusion of electric vehiclesin mobility chains
• Technological:– Low response time
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Transport services and standardisation in EU
“The assessment of energy consumption and GHG emissions of a transport service shall include both vehicle operational processes andenergy operational processesthat occur during the operational phase of the lifecycle.”
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Scope of the analysis
Resource extraction and processing
Transport (ship, train,
lorry, pipeline)
Conversion(refinery, power
plant)
Transport (diesel),
transformation (electricity)
Final energy consumption
(vehicle operation)
Facility construction
Facility decommissioning
Vehicle manufacturing
Vehicle disposal
Well‐to‐Tank Tank‐to‐Wheels
Well‐to‐Wheels
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From “well” to “tank”: Energy operational processes (1)
• Modeling of upstream stages of the life cycle for:‒ Transport fuels‒ Electricity
• Tool/Database: SimaPro v8/Ecoinvent v3
• Methods and impact categories: ‒ Global warming potential (GWP 100a, IPCC 2013)
‒ Cumulative energy demand (CED v1.08)
• Determination of upstream energy and emission factors as part of the life cycle analysis, by:‒ fuel type‒ electricity generation technology(composition of electricity mix)
Example of well‐to‐tank (WTT) stages of petroleum‐based transport fuels
Electricity generation, transmission, transformation and distribution to end users
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From “well” to “tank”: Energy operational processes (2)
21%
12%
3%15%31%
15%
1% 1%
2011
22%
9%
4%19%26%
17%
2% 1%
2012
21%
15%
5%14%
21%
20%
2% 1%
2013
Global warming potential (GWP 100a, IPCC 2013)
Electricity (ES mix)
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
2011 2012 2013
kg CO2 eq
/kWh
Oil
Co‐generation
Wind
Natural gas
Hard coal
Solar
Hydro
Nuclear
Global warming potential (GWP 100a, IPCC 2013)
Cumulative energy demand (CED v1.08)
Transport fuels (EU)
0.00
0.50
1.00
1.50
MJ inp
ut/M
J outpu
t
0.000.010.020.030.040.050.06
kg CO2 eq
/MJ
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• Dynamic emission factors from Handbook Emission Factors for Road Transport (HBEFA)
• Integrated in a MongoDB:– Inputs:
Traffic situation: <area type, road type, speed limit, level of service>Road gradient: 0%, ±2%, ±4%, and ±6% Car engine technology: Diesel, Petrol (4‐stroke), Petrol (2‐stroke), LPG, BifuelCNG/Petrol, Flex‐fuel E85Engine size class: Small (<1.4 L), Medium (>=1.4 L and <2 L), Large (>=2 L)Emission class: Up to Euro 6Fuel type (for bifuel vehicles)
– Outputs:Emission factors for CO2, CH4, N2OFuel consumption
From “tank” to “wheels”: Vehicle operational processes for passenger cars (PCs)
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• Physics‐based vehicle model → Estimation of traction power, i.e. power required to overcome the forces opposing to the movement of the vehicle and drive it at speed u
• EV components model → Transformation of traction powerrequirements (at wheels) into EV battery power requirements
From “tank” to “wheels”: Vehicle operational processes for electric vehicles (EVs) (1)
Normal forward drivingRegenerative braking
Battery Motor & controller
Gear system Wheels
Accessories
Energy flows in typical battery powered EVs
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• What’s new in modeling of motor operation?– Efficiency‐load curves based on motor type:
Synchronous
Induction
– Normalisation of efficiency based on motor size
– Modeling of “overtorque” conditions
From “tank” to “wheels”: Vehicle operational processes for electric vehicles (EVs) (2)
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• What’s new in modeling of energy recuperation?– Symmetric torque‐speed curve:
Motor mode
Generator mode
– Maximum torque limitation on energy recuperation
– Maximum regeneration capability (%) as function of vehicle speed
No energy recuperation at low vehicle speeds
Maximum energy recuperation for vehicle speeds above a minimum threshold
From “tank” to “wheels”: Vehicle operational processes for electric vehicles (EVs) (3)
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• Definition of 3 “average” models based on available electric cars in the market
From “tank” to “wheels”: Vehicle operational processes for electric cars (1)
Based on:
• Citroen C‐Zero
• Peugeot Ion
•Mitsubishi i‐Miev
• VW e‐Up!
Low power model
Based on:
• Renault Zoe
• Renault Fluence ZE
• Nissan Leaf
• KIA Soul
Medium power model
Based on:
• Ford Focus Electric
• BMW i3
•MercedesBenz Class b Electric
High power model
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• Realistic driving cycles for urban traffic conditions
From “tank” to “wheels”: Vehicle operational processes for electric cars (2)
Free flow
Heavy flow Stop and go flow
Saturated flow
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• Extraction of energy consumption factors: Indicative simulation results for stop and go flow and various road gradients
From “tank” to “wheels”: Vehicle operational processes for electric cars (3)
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• Model: The Core (GG)
• Regenerative braking: No
• Driving cycle: World Motorcycle Test Cycle (WMTC) – part 1
• Road gradient: 0%
From “tank” to “wheels”: Vehicle operational processes for electric scooter
Vehicle speed over time
Power “at wheels”
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From “well” to “wheels”: Full energy/vehicle pathway for PCs and EVs
SimaPro/Ecoinvent
Own model
HBEFA
HBEFA
SimaPro/Ecoinvent
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From “well” to “wheels”: Full energy/vehicle pathway for public transport (PT)
SimaPro/Ecoinvent SimaPro/Ecoinvent
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Energy Efficiency Assessment Module as a MOVESMART web‐service
• Purpose: Integration of components for energy efficiency assessment of multimodal routes
• Challenge: Low response time
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Concluding remarks
• Worst case scenario:– 15 km route with PC in Vitoria‐Gasteiz– 136 road segments
• Achievement: Response time <0.6 secwith Intel Core i5 and 4GB RAM
• Potential improvements:– Current implementation is built with Jersey and runs on Grizzly: Test other frameworks, e.g. Vert.x and Akka
– Load road network characteristics and energy/consumption databases in memory
