Co-Optima: Biofuel design and production predicated upon engine performance criteria

Bio4Fuels kick-off meeting

February 10th, 2017 Anthe GeorgeSandia National Laboratories

Petroleum derived fuels constrained engine design for past 150 years

Engines optimized around new bio-fuels can expand performance

Co-optima:30% per vehicle

petroleum reduction by 2030

Fuel and Engine Co-Optimization

o What fuel properties maximize engine performance?

o How do engine parameters affect efficiency?

o What fuel and engine combinations are sustainable, affordable and scalable?

o Are there fuel and engine combinations that are optimal – highest combined GHG reduction?

30% per vehicle petroleum

reduction viaefficiency and displacement

Source: EIA 2014 reference case

Co-Optimization:

9-14% GHG reduction

(beyond “business as usual”)

National goal:80%

reduction in transportation GHG by

2050

Science will be applicable to light, medium and heavy duty engines

Today’s Gasoline

New bio-blendstock

Co-optima born of US Department of Energy (DOE) National Lab System

10 national labs ~10 year program

VTO-BETOFY16 - $27m

Total ~$250m

Co-Optima Technical Challenges

Low reactivity fuel High reactivity fuelRange of fuel properties TBD

Thrust 1: Spark Ignition (SI) – NEAR TERM

Thrust 2: Advanced Compression Ignition (ACI)kinetically-controlled and compression-ignition combustion

Fundamentally different combustion dynamicsrequire different fuel properties

11 SAE 2015 Energy Saving & Emission Reduction Forum, 节能减排技术论坛

Many forms of LTC under investigation

-360 -300 -240 -180 -120 -60 0

Level of In-Cylinder Fuel Stratification at the Start of Combustion

RCCI PCCI CDC

HCCI – Homogeneous Charge Compression IgnitionGCI – Gasoline Compression IgnitionRCCI – Reactivity Controlled Compression IgnitionPCCI – Premixed Charge Compression IgnitionCDC – Conventional Diesel Combustion

NOTE: The arrows represent the approximate location of first injection for the different combustion modes

HCCI Partial Fuel Stratification

Moderate Fuel Stratification

Heavy Fuel Stratification

Full Stratification

High Performance Fuels

Advanced EngineDevelopment

MarketTransformation

Fuel Properties

Analysis of Sustainability,Scale, Economics, Risk,

and Trade - ASSERT

Modeling and Simulation Toolkit

Co-Optima: six integrated teams

Providing data for technology options

Rigorous candidate screening process

Target fuel properties to generate key data

Compatibility, performance, and production data

Establish fuel criteria

Obtain blendstocks

Test properties

Measure and validate

Establish pathway data

Feedback to ASSERT & MT

TC

BC

Purchase or produce candidates for evaluation

Create database, generate correlations

Determine blend properties, model fuel properties

Retrosynthetic analysis

High Performance Fuel team approach – FY16 results

What properties do SI engines need?

• Database of critical fuel properties of bio-derived and petroleum blendstocks ~400 molecules/mixtures (at present)

- 25 database fields for fuel properties- Includes fully blended fuels (more to come)

• Data from experiment and literature or calculated/estimated (where needed)

• Includes blending data (RON, MON, etc.)

• Now “live” and accessible to the public*

* https://fuelsdb.nrel.gov/fmi/webd#FuelEngineCoOptimization

Fuel property database

High-level screening of Thrust I fuels

18

Tier I criteriaMelting point/cloud point below -10oCBoiling point between 20oC and 165oCMeasured or estimated RON ≥ 98Meet toxicity, corrosion, solubility, and biodegradation requirements

> 40 promising bio-blendstocks from many functional group classes

Not final – this is an iterative process!

Identification of Thrust I candidates

Blendstock candidates passing Tier 1 criteria

Tier 2 and Thrust 2 are next!

• Ambitious new Department of Energy initiative

• Accelerating introduction of affordable, scalable, and sustainable fuels and high-efficiency, low-emission engines

• Engagement with industry stakeholders critical to success

Summary

Supported by USDOEEnergy Efficiency and Renewable Energy

Bioenergy Technologies OfficeVehicle Technologies Office

M

DOE Leadership:Alicia Lindauer and Borka Kostova (BETO)Kevin Stork, Gurpreet Singh, Leo Breton, and Mike Weismiller (VTO)

National Lab Leadership:John Farrell (NREL)*, John Holladay (PNNL), Robert Wagner (ORNL), Mark Musculus (SNL) Dan Gaspar (PNNL), Paul Miles (SNL), Jim Szybist (ORNL)Jennifer Dunn (ANL), Matt McNenly (LLNL), Doug Longman (ANL)

Acknowledgments

Thank You!

If we identify target values for the critical fuel properties that maximize efficiency and emissions performance for a given engine architecture, then

fuels that have properties with those values (regardless of chemical composition) will provide comparable performance

There are engine architectures and strategies that provide higher thermodynamic efficiencies than available from modern internal combustion

engines; new fuels are required to maximize efficiency and operability across a wide speed/load range

Governing Co-Optima hypotheses