Going Places: Prospects for Transportation Alternatives
Moderator: Dr. Jonathan Burbaum, ARPA-E Dr. Ilan Gur, Program Director, ARPA-E Dr. Ping Liu, Program Director, ARPA-E Dr. Liz Santori, ARPA-E The Panel
Moderator/Panelist: Panelist: Jonathan Burbaum Ping Liu Program Director Program Director [PETRO, OPEN 2012] [RANGE, OPEN 2012]
PETRO vs. Petroleum: One Battery for Everything Opportunities for New Technologies
Panelist: Panelist: Ilan Gur Liz Santori Program Director ARPA-E Fellow [AMPED, OPEN 2012]
An Intelligent Electric Future Grounding the Flying Car
February 24, 2014 2 Rewiring the Sankey Diagram
12.4
21.1 25.7
3 PETRO vs. Petroleum: Opportunities for New Technologies
Jonathan J. Burbaum Program Director, ARPA-E
February 24, 2014 ARPA-E Energy Innovation Summit Washington, DC 5 Biofuel vs. Petroleum
Biofuel Petroleum
CO2 CO2 CO2 Grown Processed Burned Extracted Processed Burned $ coproducts? $$ coproducts!
Surface land use: ~100% Surface land use: <<50% Process up time: ~25% Process up time: ~100%
6 7 What really happens: Food AND Fuel!
Data from 2012 10-K (in thousands) Gross profit (loss): Ethanol production $ (4,895) Corn oil production $ 32,388 Agribusiness $ 35,973 Marketing and distribution $ 32,362 Intersegment eliminations $ 943 Total $ 96,771
8 What’s next after PETRO?
‣ PETRO – Improving Energy & Carbon Flow via engineering biology – Sole metrics: Cost /Energy in fuel product, coproducts ‣ Other Approaches: – IT-enabled “Precision” Agriculture – Component Improvements – Rethinking the System
10% improvement of land use based on geometry alone!
9 Can tobacco be an energy crop?
16% DW TAG × 9 tons per acre = 48 GJha-1y-1 …vs. soybean = 17 GJha-1y-1 …vs. corn = 80 GJha-1y-1
Plant Biotechnology Journal 24 OCT 2013 DOI: 10.1111/pbi.12131
10 One Battery For Everything
Dr. Ping Liu Program Director
February 24, 2014 Storage: Cornerstone of Grid and Vehicle
Grid Storage: GRIDS
Distributed Generation
Vehicle Storage: BEEST RANGE
12 ARPA-E’s Grid Storage Portfolio
SMES
Capacity & Duration Flywheel
Flow Cells
Power Density (Capacity) Density Power E-C Cells CAES
GRIDS:
100 $/kWh
5000 cycles Energy Density (Duration)
13 ARPA-E’s Vehicle Storage Portfolio-BEEST
Highest theoretical energy density
ARPA-E
Commercial
BEEST pushed the boundaries of energy storage
14
ARPA-E’s Vehicle Storage Approach-RANGE
RANGE System/cell Approach constant = 1 System Goal Multi-functional design
Robust chemistry/ architecture innovation TARGETS: Current robust chemistry/ architecture 100-125 $/kWh
Energy Density of the Battery System Battery the of Density Energy 1000 cycles
Energy Density of Battery Chemistry/Cell
Focus on system optimization led to novel EV storage approaches
15 RANGE Program Portfolio
Aqueous Robust Non-aqueous
Solid State Multifunctional
cloteam, LLC
16 Requirements for “One Battery For Everything”
Cost ($/kWh) Life (cycles) Specific energy (Wh/kg)
Grid 100 5000 - Electric Vehicles (EVs) 100-150 1000 100+ Distributed Generation ? 5000? -
Low-E flow cells Pb acid batteries Grid ? Li-S batteries Vehicle DG
A > 100 Wh/kg, >> 10000 cycle solution is the key
17 Benefits of “One Battery For Everything”
Cost ($/kWh) Life (cycles) Specific energy (Wh/kg)
Grid 100 5000 - EVsFavorable 100-150 1000 100+ Distributedeconomics Generation ? 5000? - One Battery for < 200? >> 10000 100+ Everything Favorable economics
Viable V2G
“One For All” Solution Rapid charging
18 Searching For The Ultimate Energy Storage
Most desirable Opportunity Li-S, Li-FeF3, LiSi
Li-Ion ΔG Ni-MH
Ni-Cd Low energy Li-ion Pb-acid
Capacitor
ΔS
19 Batteries That Live “Forever”
Anode for Li replenishment
The ultra long life
LiFePO4-Li4Ti5O12 Battery
Zaghib, et al, J. Power Sources, 196, 3949 Wang, et al, J. Power Sources, 196, 5966
20 A Unified Generation/Storage Future
21 An Intelligent Electric Future
Dr. Ilan Gur Program Director
February 24, 2014
Electricity 350% Energy Intensity 50%
1920 1940 1960 1980 2000 intelligence Productivity 400%
Information Optimization Flexibility
1920 1940 1960 1980 2000
Grid Intelligence: GENI Synchrophasor Information Load disaggregation AMI Condition mon. Digital subs.
Topology control Optimization Demand opt. Reactive P cont. Stochastic opt.
HVDC FACTS Flexibility Breakers U.S. DOE, “National Transmission Grid Study” (2002) DSR Storage 27 Battery Intelligence: AMPED Information Internal Temp Strain Potential
Chemistry
Physics models Optimization Degradation models Dynamic controls Load prediction Order red.
Reconfigurable Flexibility Cell power mgmt Cell thermal mgmt
Wireless bms
28 One Battery for Everything
29 One Intelligent Battery for Everything
‣Integrated sensors ‣Power conversion ‣Computation, control, and diagnostics ‣Wireless readout
30 One Intelligent Battery for Everything
31 Grounding the Flying Car
Dr. Liz Santori ARPA-E Fellow
March 4, 2014 Air travel for short-duration trips
Shorten travel times • Average speed: >100 mph vs. average city speed ~30 mph
Reduce idling and braking
Direct routes
Potential for safer travel • In a collision of 1,500 lb car vs. 15,000 lb truck, who wins?
30 mi
>1,200 ft
33 Move toward automation
Boeing.com
Collision avoidance Sensing Fly-by-Wire
Communication Vehicle-to-vehicle communication Semi-autonomous Intelligent unmanned aerial vehicle Controls
Autonomous vehicles Fully autonomous aircraft
34 ‘Flying cars’ are cool… Would they consume much more energy?
Moller M400 Terrafugia Transition
Terrafugia TF-X Joby Aviation Monarch
35 Breakdown of electric vehicle losses
Rolling Accessories Losses Resistance 4% 22%
Inertia to Net DC Powertrain Wheels 47% 100% 64%
Regenerative Braking -38% Aerodynamic EPA city mpg ~ 105 Drag
65 mpg in heavy summer traffic
Nissan Leaf. Argonne National Laboratory
36 Current light aircraft
Sikorsky S-333™ • MPG ~ 5 • 2,460 lbs • 4 person Sikorsky
Cirrus SR22 • MPG ~ 16 • 3,600 lbs • 4 person Cirrus Aircraft
Camcopter® S-100 (UAV) • MPG ~ 8 • 441 lbs
Schiebel
37 Estimation of MPG for VTOL
30 mi
3,000 ft 1.2 kWh
C2 C = C + L D d p × AR×e s Losses from drag in cruise 1 F = rSC v2 D 2 D
Key assumptions: E = mgh Potential energy of aircraft 1100 lb aircraft ηEM = 92% ηbattery = 97% ηprop = 80%
38 Estimation of MPG
Cruise Lift over Drag (L/D) 3 8 14 28
22 ft 33 ft 22 ft 36 ft
Cruise power 80 kW 45 kW 28 kW 16 kW % mgh 5% 13% 20% 35% % weight 94% 38% 24% 15% battery
MPG 30 80 120 200
39 Thank You
Moderator/Panelist: Panelist: Jonathan Burbaum Ping Liu Program Director Program Director [PETRO, OPEN 2012] [RANGE, OPEN 2012]
PETRO vs. Petroleum: One Battery for Everything Opportunities for New Technologies
Panelist: Panelist: Ilan Gur Liz Santori Program Director ARPA-E Fellow [AMPED, OPEN 2012]
An Intelligent Electric Future Grounding the Flying Car
40 www.arpa-e.energy.gov
41