Analysis of CO2 Emissions to Consider Future Technologies and Integrated Approaches in the Road Transport Sector

February 26, 213 IEEJ APERC Annual Conference Keio Plaza Hotel Tokyo, Shinjuku, Tokyo Analysis of CO2 Emissions to Consider Future Technologies and Integrated Approaches in the Road Transport Sector Shuichi KANARI Japan Automobile Research Institute

Contents 1.Introduction 2.Outline of CEAMAT (Model Structure, Analysis Target) 3.Input Data (Demand of Road Transport Sector,Automotive Technologies) 4.Result of Scenario Analysis (Number of Automobiles, Fuel Economy,CO2 Emissions) 5.Reduction of CO2 Emissions with Integrated Approaches 6.Conclusion CEAMAT:Energy Analysis model for the long term in road the transport sector 2

Background of this Research Increasing concerns about energy security and global climate change Necessity of energy saving, fuel diversification and reduction of greenhouse gas Not enough evaluation of long term technical scenarios in the road transport sector Not considering cost-effectiveness and realizing fuel economy improvement technologies Restriction of analysis vehicle target (e.g. only Passenger cars) 4

Purpose of this Research Development of long term CO2 reduction scenarios to consider future automotive technologies and integrated approaches in the Japanese automotive sector. 1. Construction of a database with demand of the road transport sector and future automotive technologies 2. Development of cost-effectiveness tools for future automotive technologies 3. Scenario analysis 4. Analysis of CO2 reduction with integrated approaches 5

What is CEAMAT? CEAMAT is an analysis energy model for the road transport sector for the long-term. CEAMAT links with the IEEJ25 model that is an energy analysis model for all sectors worldwide, developed by The Institute of Energy Economics, JAPAN. IEEJ 25 model (World) Estimation of Economy/Energy/ No. of Vehicles World population Each country/region GDP Energy consumption of each sector Primary energy consumption Cost of fuel and electricity Vehicle demand No. of new vehicle sales/ in use Estimation of Fuel Economy and Vehicle Number by Each Technology Gasoline/Diesel Vehicle Biofuel Vehicle HEV PHEV EV FCV NGV LPG Other Integrated Approaches CEAMAT (Japan) Estimation of Fuel Demand Oil (Gasoline/Diesel) Biofuel Electricity Hydrogen CNG LPG Synthetic fuel Other Improving Traffic flow Eco-driving Social Cost Emission of GHG Vehicle cost / fuel cost Social cost (infrastructure, tax, etc.) Energy consumption CO2 emissions Annual total cost Output Analysis Scenario 7

Target Vehicle Type and Class Vehicle section, focus on the Japanese automotive market Passenger car Truck Bus Middle (> 2cc) Small ( 2cc) Mini ( 66cc) Large (GVW > 8t) Middle (3.5t<GVW 8t) Small (GVW 3.5t) Mini ( 66cc) Large (GVW > 8t) Small (GVW 8t) Passenger car Truck Bus 8

Target Automotive Technologies Technology Fuel path GICEV Gasoline Internal Combustion Engine Vehicle GICEHEV Gasoline Internal Combustion Engine Hybrid Vehicle Gasoline/Ethanol DICEV Diesel Internal Combustion Engine Vehicle DICEHEV Diesel Internal Combustion Engine Hybrid Vehicle Diesel oil/bdf HICEV Hydrogen Internal Combustion Engine Vehicle HICEHEV Hydrogen Internal Combustion Engine Hybrid Vehicle Hydrogen/Gasoline CNGV Compressed Natural Gas Vehicle CNG DMEV Dimethylether Vehicle DME LPGV Liquefied Petroleum Gas Vehicle LPG EV Electric Vehicle Electricity HFCV Hydrogen Fuel Cell Vehicle Hydrogen GICEPHEV Gasoline Internal Combustion Engine Plug-in Hybrid Vehicle Gasoline/Electricity DICEPHEV Diesel Internal Combustion Engine Plug-in Hybrid Vehicle Diesel oil/electricity HFCPHEV Hydrogen Fuel Cell Plug-in Hybrid Vehicle Hydrogen/Electricity EV GICEV (mixed Bio-ethanol) GICEHEV CNGV DICEV (mixed Bio-diesel) HICEV GICEPHEV HFCV 9

Model Structure for New Vehicles Base Vehicle Weight Engine Power HEV Fuel Economy ICE Fuel Economy Base Vehicle Cost ICE Fuel Consumption Improvement Cost Engine Weight Fuel Tank Weight Vehicle Weight ICE Fuel Estimation Formula Electric Charger Coefficient Engine Cost Fuel Tank Cost Motor Weight Battery Weight EV Fuel Estimation Formula (DC) EV Electric Economy (AC) Motor Cost Battery Cost Vehicle Cost FC System Weight Battery Coefficient Fuel Economy Sub Model FC System Coefficient FC Fuel Economy (AC) FC System Cost Plug-in Additional Cost Vehicle Cost Sub Model Fuel Economy Usage Age Vehicle Cost Fuel Cost per Unit Fuel Cost per Automotive Tax Driving Distance per Total Cost for Usage Age Line-up Number Formula Selecting Each Technology Sales Number Each Technology Sale Number Each Technology New Sales Number Sub Model 1

Related Probability of Technology Choice and Driving Distance (e.g. GICEV vs. GHEV) Probability of technology choice (Pr) is estimated by total cost in the depreciation period and Line-up number for each distance. Pr k M k ' K 1 k M exp( 1 k ' C exp( Tk ) C Tk ' ) k:technology section K:Assembly technology section C Tk :Total cost in usage period M k :Line-up number θ, θ 1 :Parameter (θ =-6.46, θ 1 =.94) 1% Probability technology choice 8% 6% 4% 2% % GICEHEV GICE Annual driving distance (km/year) 11

Demand of the Road Transport Sector and Fuel Price in the IEEJ25 Model Vehicle mileage traveled (VMT: billion person km) Traffic volume( Billion ton km) 4 35 3 25 2 15 1 5 8 7 6 5 4 3 2 1 Demand of Passenger car (Passenger car & Bus) 25 21 215 22 225 23 235 24 245 25 Passenger car 25 21 215 22 225 23 235 24 245 25 Bus Demand of freight (Truck) Truck Annual driving distance (km/year) FUel price(with tax:yen/mj) 35, 3, 25, 2, 15, 1, 5, 1 8 6 4 2 25 21 215 22 225 23 235 24 245 25 25 21 215 22 225 23 235 24 245 25 Number of Vehicles in Use Passenger car Truck Bus Fuel Price Gasoline Diesel oil CNG Electricity Hydrogen 13

Efficiency and Price of Future Technologies in ICE Vehicles Base vehicle:standard vehicles in the year 2 Choice of good cost-effectiveness technology combinations Determination of approximate curve to use good cost-effectiveness technology combinations Grouping Engine Transmission Accessories Improving FE Technology Passenger car Truck Gasoline Direct Injection (Stoichimetric) Gasoline HCCI Cam Phasing Engine Downsizing EGR Improved Engine Friction Improved firing chamber Other normal advance technologies Turbo Compound Variable Compression Ratio Valuable Valve Timing 2 stages Turbo After treatment Device CVT 5AT 6AT Multiple Transmission High Differential Gears Ratio Direct-connected maximum gear Dual Clutch AMT Electric Power Steering Improved Alternator Electric Accessories Additionnal Price (1,yen) Additional Price(1,yen) 25 2 15 1 5 % 1% 2% 3% 4% 5% 14 12 1 8 6 4 2 Price curve (Passenger car) Price curve (Truck) Improving fuel economy ratio(%) Middle Small Mini % 5% 1% 15% 2% 25% 3% Improving fuel economy ratio(%) Large Middle Small Mini 14

Scenario of Future Technological Factors with EV and HFCV Price and efficiency of battery and fuel cell systems in the future are based on government and private sector reports and interviews. Future scenarios are efficiency improvement and a lower price, considering advanced technologies and mass production. Trucks part prices are higher than passenger car parts prices, because system accessories are larger and more expensive. Battery Weight(kg/kWh) 12 1 8 6 4 2 Battery Weight Passenger car Truck Battery Price(1,yen/kWh) 7 6 5 4 3 2 1 Battery Price Passenger car Truck Imrovement ratio of FC system effiiciency 25 21 215 22 225 23 235 24 245 25.3.25.2.15.1.5 Passenger car Truck Fuel cell system efficiency improving Ratio. 25 21 215 22 225 23 235 24 245 25 FC system price(1,yen/kw) 25 21 215 22 225 23 235 24 245 25 9 8 7 6 5 4 3 2 1 25 21 215 22 225 23 235 24 245 25 Fuel cell system Price Passenger car Truck 15

New Vehicle Energy Economy and Price (e.g. Small Passenger car) All technologies improved energy economy, considering advance technology factors such as ICE technologies, battery and fuel cost. Improvement technologies price of improvement are added to ICEV's vehicle price. Other new automobiles come down in price to reflect mass production of technology factors (Battery and fuel cell system, etc.). Fuel economy (MJ/km) 2.5 2. 1.5 1..5. 25 21 215 22 225 23 235 24 245 25 Energy economy (JC8 mode) Vehicle price(1,yen) 2,9 2,7 2,5 GICEV 2,3 GICEHEV 2,1 DICEV 1,9 EV 1,7 HFCV GICEPHEV 1,5 1,3 25 21 215 22 225 23 235 24 245 25 Vehicle price GICEV GICEHEV DICEV EV HFCV GICEPHEV 16

Number of New Vehicles and Vehicles in Use(Passenger Car) Number of new vehicles (1,Units) Number of vehicles in use(million Units) 5, 4,5 4, 3,5 3, 2,5 2, 1,5 1, 7 6 5 4 3 2 1 5 25 21 215 22 225 23 235 24 245 25 25 21 215 22 225 23 235 24 245 25 Number of new vehicles Number of vehicles in use Other DICEPHEV GICEPHEV HFCV EV LPGV CNGV DICEHEV DICEV GICEHEV GICEV Other DICEPHEV GICEPHEV HFCV EV LPGV CNGV DICEHEV DICEV GICEHEV GICEV Number of new vehicles Share of next generation vehicles : 48%(25) Number of vehicles in use Share of next generation vehicles : 43%(25) Next generation vehicles are HEV,EV,PHEV,FCV and CNGV.This definition is from a report by METI, Diffusion report of next generation vehicles 21 written in Japanese. 18

Fuel Economy of Vehicles in Use and TtW CO2 Emissions in the Passenger Car Sector Fuel economy vehicles in use (MJ/km) TtW CO2 emissions (Mt CO2) 3. 2.5 2. 1.5 1..5. 25 21 215 22 225 23 235 24 245 25 16 14 12 1 8 6 4 2 Fuel economy of vehicles in use CO2 Emissions CO2 emissions in 25: -55%(Based on 25) 25 21 215 22 225 23 235 24 245 25 19

CO2 Emissions(Road Transport Sector) TtW CO2 emissions(mt CO2) 25 2 15 1 5 Passenger car Truck Bus 25 21 215 22 225 23 235 24 245 25 CO2 emissions in 25: -47%(Based on 25)

Analysis Process of CO2 Reduction for Eco-driving CO2 reduction of eco-driving per unit Passenger car: 13% Truck: 9% Average of 9 organizations Popularization ratio of eco-driving in 25 Passenger car & Truck : 7% Energy ITS research meeting report Result of CEAMAT CO2 weighting factor in 25 (Based on vehicle mileage traveled) Passenger car: 65% Truck: 35% Each CO2 reduction for eco-driving Passenger car :9% Truck : 6% CO2 reduction for eco-driving Total : 8% 9 organizations of evaluating eco-driving: NIES,Libertas terra Co.Ltd,HONDA R&D CO.Ltd IID.Inc. NTSEL,LEVO, etc 22

Analysis Process of CO2 Reduction to Improving Traffic Flow GDP in Japan Road project cost in Japan Changing average speed in Japan CO2 emission factor (g/km) 35 3 25 2 15 1 5 Speed vs. CO2 emissions Driving DIstance Distribution 4% 35% 3% 25% 2% 15% 1% 5% % Speed distribution (all vehicle categories) 25 25 2 4 6 8 1 Average speed (km/h) Average speed range (km/h) Energy ITS research meeting report Item CO2 Reduction Platooning.2% Traffic light control (Only vehicle).4% Traffic light control (link up with infrastructure) 2% Full route information 1.4% Predicting optimal starting time.1% CO2 reduction to road improvement Total : 4% CO2 reduction to implement technologies Total : 4% CO2 reduction to improving traffic flow Total : 7% 23

CO2 Reduction for Integrated Approaches (Road Transport Sector) 25 TtW CO2 Emissions (Mt CO2) 2 15 1 5 Baseline case Baseline case + Integrated Approach case 25 21 215 22 225 23 235 24 245 25 Technological composition is assumed to be the same as the Baseline case, that is without an integrated approach. CO2 Reduction in 25: 55% based on 25(Baseline case:47%) 24

Conclusion Cost-effectiveness analysis tools including an automotive database with advanced technologies were developed, and future scenarios were analyzed. In addition, integrated approaches were researched and calculated for their potential to reduce CO2 emission. 1. From the result of this scenario analysis, CO2 reduction potential of the road transport sector with next generation automobiles is calculated as 47% (based on 25) in 25. 2. CO2 reduction potential from next generation automobiles and integrated approaches (Improving traffic flow and Eco-driving) is calculated as 55% (based on 25) in 25. From the result of CO2 reduction potential from improving traffic flow and eco-driving, we ve shown it is necessary to popularize next generation automobiles and integrated approaches. 26

Thank you for your attention!