1. Tom Carlson Sierra Research, Inc. March 19, 2015 CEC Workshop Tom Carlson Sierra Research, Inc. March 19, 2015 CEC Workshop

2. Purpose  Background and Key Sources  Light Duty Vehicles  Heavy Duty Vehicles 2

3. Background  Vehicle attributes are used as input data for CEC consumer choice modeling to estimate the characteristics of the California vehicle fleet.  Attributes include vehicle price, fuel economy, number of different models offered (vehicle configurations), as well as performance and utility metrics.  Attributes are forecast for:  Vehicle classes comprising 18 light-duty and 16 heavy- duty size and vehicle type categories used by CEC; and  Technology/fuel groups encompassing 14 conventional and emerging alternative fuels (gas, diesel, NG, ethanol, electricity) and vehicle technologies (conventional, hybrids, plug-in hybrids, electric and fuel cell). 3

4. Draft 2015 IEPR Scope and Key Sources  Light-duty fleet attribute forecasts:  2013 National Academy of Sciences “Transitions to Alternative Vehicles & Fuels” study, LAVE-Trans model  CARB 2013/14 ZEV Amendments  Updated historical data (added MY2012-2013 data)  Heavy-duty fleet attribute forecasts:  Truck fuel economy – ARB’s EMFAC2014 model  Truck prices – tabulations of new vehicle MSRP data purchased from industry content provider  National Petroleum Council (NPC) “Trucks 5.1” heavy- duty vehicle market penetration model 4

5. Light Duty Vehicles 5

6. Light-Duty Vehicle Historical Baseline  For 2015 IEPR, Sierra adding two model years of data (2012 and 2013) to historical database back to MY1992 with additional attributes described earlier:  Data compiled at vehicle model & powertrain level  Historical averages by model year, vehicle class and fuel/tech group based on U.S. fleet new vehicle sales  Price (MSRP) and fuel economy data collected for MYs 2014-2015 to test early-year forecasts. 6

7. Light-Duty Attribute Forecasts – Overview  Attribute forecasts through MY2026 being prepared for a set of fuel price/economic/regulatory policy scenarios specified by CEC.  Scenarios assumed compliance with adopted federal (CAFE, GHG, & RFS), and California ( GHG, ZEV, & LCFS) regulations.  Preliminary fuel price scenarios from CEC to be updated upon availability of new 2015 EIA forecasts  Primary source for attribute forecasts is 2013 NAS “Transitions to Alt. Vehicles & Fuels” study and LAVE- Trans model 7

8. Light-Duty Attribute Forecasts - NAS-Based Assumptions & Methods  Key NAS analysis scenarios:  Reference – Adopted federal regs. through 2025 (CAFE, RFS2) – used for Draft 2015 IEPR  Midrange – Policy support beyond 2025 seeking 50% LDV GHG reductions by 2030 from 2005 levels  Optimistic – Aggressive policy support and “stretch goals” requiring greater R&D and vehicle design success  NAS technology penetrations:  Powertrain improvements - Vehicle simulation modeling performed for EPA 2025 GHG regulations  Load reductions – Improvements from light-weighting, aero. drag & rolling resistance reductions and accessory efficiency gains 8

9. Light-Duty Attribute Forecasts - NAS-Based Assumptions & Methods (cont.)  NAS-based technology costs:  Fully-learned, high-volume costs and phase-in schedules  Separate estimates developed for: Internal combustion engines (ICEs) Hybrids (HEVs) – added as increment to ICE costs (subtracting credits for smaller engines) Plug-In Hybrids (PHEVs) – 3-10 times higher battery/ motor sizes Battery-Electric Vehicles (EVs) – 30 times higher battery/motor sizes than HEVs Fuel Cell Vehicles (FCVs) and Compressed Natural Gas Vehicles (CNGVs) – cheaper than EVs, infrastructure constrained 9

10. Light-Duty Attribute Forecasts - NAS-Based Assumptions & Methods (cont.)  Key NAS assumptions:  NAS did not consider further efficiency improvements to diesel engines – assumed manufacturers would focus ICE improvements on gasoline engines  Lithium-ion is long-term technology for plug-in hybrid and battery electric vehicles  Weight reductions ranging from 15% to 20% (relative to 2010) by 2030  Markup factor of 1.25 (to translate production costs to retail-equivalent increments)  Manufacturers will trade historical increases in performance and utility for downsizing to comply with stringent GHG/FE standards 10

11. Light-Duty Attribute Forecasts - NAS-Based Assumptions & Methods (cont.)  LAVE-Trans spreadsheet model developed under NAS study used to generate FE and vehicle price forecasts  Used NAS-based relative FE improvements and vehicle prices (MSRP) for gas ICE, HEV, PHEV, FCVs and CNG technologies.  Diesels - NAS-based load reduction gains (and costs) for gas ICEs used to forecast FE improvements and MSRP.  Future battery costs scaled from NAS “midrange” estimates: (over 80% reductions in 2035 for HEVs, 70- 75% for PHEVs, 65% for EVs relative to 2010) – plan to test LAVE-Trans sensitivity to battery cost assumptions 11

12. Light-Duty Attribute Forecasts - Additional Adjustments  Model availability forecasts (number of models):  Gas ICEs & HEVs – Scaled from LAVE-Trans sales projections  Diesel ICEs – grown through MY2018 based on Bosch projections from June 2013 workshop  PHEVs, EVs, FCVs: Grown from 2013 baseline to reflect updated ZEV light-duty vehicle sales targets (initially based on Vision Scenario 2) CARB-based splits by vehicle type (car vs. truck)  Fuel price-triggered size class shifts within car and truck fleets (based on Busse, et al., 2013) 12

13. Heavy Duty Vehicles 13

14. Analysis Framework – Heavy-Duty Vehicle Classes 14  EMFAC2014 model utilizes a scheme of over 30 separate heavy-duty vehicle categories defined by vehicle type (bus vs. truck), size/capacity (GVWR) and usage (for alignment with recent fleet rules)  Mapped by CEC to the 16 heavy-vehicle classes in its demand model (classes by GVWR and usage)  Trucks 5.1 model uses 3 basic categories:  All GVWR Class 3-6 vehicles  GVWR Class 7-8 single unit trucks  GVWR Class 7-8 combination unit trucks  All three models/schemes employ breakdowns by fuel type (e.g., gasoline, diesel, natural gas)

15. Heavy-Duty Vehicle Historical Database - Vehicle Prices  Sources of historical “new vehicle” price data (i.e. sale price for vehicle when sold new) are much more limited for heavy-duty vehicles than the light-duty fleet  Sierra has identified Price Digests and their “Truck Blue Book” database (www.truckbluebook.com) as a source of heavy-duty price data (MSRP and current value)  Detailed “configuration level” data purchased from Price Digests for 10 most recent model years:  Make, model, model year  GVW, GCW, wheel base, axle config.  Engine description and fuel type  Several thousand individual configurations 15

16. Heavy-Duty Vehicles - Historical and Forecasted Fuel Economy  CARB’s EMFAC2014 model used to develop initial estimates of both historical (2000-2013) and forecasted (2014-2026) heavy-duty vehicle fuel economy  Statewide EMFAC run executed for all vehicle types, calendar years and model years  Outputs processed to calculate new vehicle fuel economy (mpg) by model year, vehicle class and fuel  EMFAC2014 “default mode” fuel use estimates reconciled to match historical CA fuel sales  Forecasted fuel economy for heavy-duty vehicles reflects adopted federal MY2014-2018 CAFE standards 16

17. Heavy-Duty Vehicle Market Penetration Modeling  NPC Trucks 5.1 market penetration model being used by CEC to forecast heavy truck technology penetrations  Historical heavy-duty MSRP data (from Price Digests) being tabulated into Trucks 5.1 input structures:  “Base” technology prices by model year  Incremental costs for alternative technologies (gas vs. diesel vs. natural gas)  Averages by 3 groups: Class 3-6, Class 7-8 single unit, Class 7-8 combination unit  Fuel economy by model year (in above three groups)  “Preference” factors reflecting the utility/value of alternative technologies when prices are equal 17

18. Closing Summary  Preliminary light and heavy-duty attribute forecasts being delivered by end of March  Sierra and CEC staff will be performing more extensive sensitivity analyses (tracking sales external to attributes, reviewing/incorporating feedback from consumer choice results) and examining consistency with other IEPR forecast elements  Workflow being designed to accommodate longer horizons (to 2050) if needed 18

19. Questions and Comments Tom Carlson Sierra Research, Inc. 916-444-6666 tcarlson@sierraresearch.com THANK YOU! 19

20. Appendix 20

21. Analysis Framework – Light-Duty Vehicle Classes 21

22. Analysis Framework – Light-Duty Vehicle Attributes Modeled 22  Attributes estimated by model year, vehicle class and fuel/tech group – sales weighted composites

23. 14 Tech/Fuel Groups Used 23