Energy Storage Systems Discussion

Transcription

1 Annual Meeting and Technology Showcase Logan, Utah September 27-28, 2016 Energy Storage Systems Discussion SELECT Energy Storage Systems Lead Dr. Gregory L. Plett (UCCS)

2 Energy Storage Systems The battery pack is the single-most expensive component of hybrid and electric vehicles (xevs) Making batteries last longer and work better is key to reduced cost and higher market penetration Solution involves far more than optimized cell chemistry and manufacturing Improved battery controls can eke more performance out of the battery pack while still extending life Novel power electronics can enable cell-level controls and prognostics, multi-chemistry/hybrid battery packs Systems-level optimizations can eliminate redundant components and leverage common signals to advantage 2

3 SELECT Energy Storage Research Areas Research focused on: High-fidelity reduced-order physics-based ESScomponent (battery cells, supercapacitors) modeling: understanding causes of premature degradation Physics-based ESS prognostics and controls enabling operation up to but not exceeding actual physical limits Cost-neutral active balancing to extend life Hardware implementation of active balancing and model-predictive controls; long-term lab testing Validation via vehicle-level EV testbed Graduate education in electric-drivetrain technology 9 posters and demos this afternoon 3

4 Energy Storage Systems Projects ARPA-E: AMPED PI: Regan Zane, USU University of Colorado Boulder Univ. of Colorado Colorado Springs National Renewable Energy Labs Ford Motor Company Cost-neutral active balancing of battery pack 30 45% longer life GM: Physics-Based Reduced- Order Models for Vehicle BSE PI: Gregory Plett, UCCS Computationally efficient ways to make physicsbased predictions of internal cell behaviors and aging mechanisms Enables optimal controls to increase performance and extend life Posters MPC Applied to Large Battery Packs ID of Li-Ion Physics-Based Model Parameter Values Models of Li-Ion Cells Having Composite Electrodes Li-Ion Age Estimation via IMM Kalman Filter 4

5 Energy Storage Systems Projects ONR: Robust, Modular, Plugand-Play Expeditionary Energy Storage Architecture PI: Regan Zane, USU University of Colorado Boulder Univ. of Colorado Colorado Springs National Renewable Energy Labs Leveraging AMPED tech for distributed dc micro-grids USAF, LTC, UCCS, NASA: Demonstrating Research Innovations in Vehicle Electrification (DRIVE) Pis: Plett and Trimboli, UCCS Building EV testbed for validating battery estimation and controls methods Poster Research in Battery Modeling and Control for Transportation 5

6 Energy Storage Systems Projects DOE: GATE Center of Excellence in Innovative Drivetrains in Electric Automotive Technology Education (IDEATE) PI: Gregory Plett, UCCS University of Colorado Boulder Utah State University Graduate certificate and degrees in battery controls and vehicle power electronics Graduate Education in Battery Controls University of Colorado Colorado Springs Graduate Education in Vehicle Power Electronics University of Colorado Boulder Utah State University Graduate courses supporting PhD in EE emphasis area in Battery Controls Feedback Control Digital Control Multivariable Ctrl. Systems I, II, III MSEE option in Battery Controls Optimization System Identification Kalman Filtering Model Pred. Ctrl. Graduate Certificate in Electric Drivetrain Technology Modeling Battery Dynamics Battery Management & Ctrl. Power Electronics for Electric Drive Vehicles Adjustable Speed AC Drives MSEE emphasis area in Vehicle Power Electronics Intro. to Pwr. Elect. Res., Soft Switch Model/ctrl Pwr. Elect. Pwr. Elect Lab Graduate courses supporting PhD in EE emphasis area Vehicle Pwr. Elect. Analog IC Design Mix. Signal IC Des. Renewable Energy Ctrl. Sys. Analysis Embed. Sys. Des. Digital Control Sys. Poster: GATE Center of Excellence in Innovative Drivetrains in Electric Automotive Technology Education 6

7 Energy Storage Professor Introductions Scott Trimboli, UCCS Dragan Maksimović, CU-Boulder Regan Zane, Utah State University Tianbiao (Leo) Liu, Utah State University 7

8 Annual Meeting and Technology Showcase Logan, Utah September 27-28, 2016 Extending Battery Pack Lifetime and Safely Unlocking Pack Potential Lead Institution Utah State University, PI: Dr. Regan Zane Team Institutions University of Colorado Boulder, PI: Dr. Dragan Maksimovic University of Colorado Colorado Springs, PI: Dr. Scott Trimboli National Renewable Energy Laboratory, PI: Dr. Kandler Smith Ford Motor Company, PI: Dyche Anderson Technology to market advisor: Dr. Tallis Blalack DOE ARPA-E AMPED Program

9 Making battery packs last longer Life balancing approach rethinks balancing objectives 30% PHEV life extension; 40-45% BEV life extension 9

10 Life balancing development Model Prediction Impact of high SOC during rest periods Pack-level Validation * K. Smith, Predictive Models of Li-ion Battery Lifetime, IEEE Conference on Reliability Science for Advanced Materials and Devices, ** K. Smith, Y. Shi, E. Wood, A. Pesaran, Optimizing Battery Usage and Management for Long Life, Advanced Automotive Battery Conference 2016, Detroit MI, June

11 Life balancing map Extend lifetime by enforcing uniform degradation Continuous cell-level balancing based on life map Stronger cells are driven to a higher max SOC Weaker cells to a lower max SOC Mapping is chemistry dependent Pack US06 discharge cycle 11

12 Innovative hardware architecture Replaced existing battery management system and DC/DC with integrated modular BMS-DC/DC Differentially balance battery cells with 12 V load Advanced cell-level control is now possible 94% average efficiency over full charging cycle Cell-level and sub-string level module designs 12 V Bus 12

13 Impact on temperature gradient sensitivity Temperature differences lead to shorter battery pack lifetimes Life balancing removes differential temperature effects % Capacity Imbalance Year 8 BEV passive balancing Liquid and air cooled BEV life balancing Liquid and air cooled Cell-to-cell Temperature Difference ( C) 13

14 Advanced control unlocks battery potential Cell-level physics-based model predictive control (MPC) Safely enables performance to physical limits without cell degradation Side-reaction over-potential, 4C charging, 0ºC Standard voltage limits Physics-based MPC 10% capacity loss in 256 cycles! No capacity loss 14

15 The Team Dr. Regan Zane (USU), Dr. Dragan Maksimovic (CU-Boulder) Integrated modular BMS-DC/DC architecture, hardware realization Dr. Gregory Plett and Dr. Scott Trimboli (UCCS) Electrochemical models and physics-based MPC algorithms Dr. Kandler Smith (NREL) Life control algorithms and pack aging models Dyche Anderson (Ford) Battery systems and automotive engineering Dr. Tallis Blalack tech to market lead 15

16 Technology status Full-scale integration on Ford Fusion PHEV battery pack Long term A/B validation tests underway at NREL Extending to plug-and-play renewable energy micro-grid Active industry involvement Multiple auto OEMs Partnership with Hybrid Design Services (HDS) 16

17 Energy Storage Systems Panelists Michael Kultgen Design Director for Battery Management ICs, Linear Technology Corporation Prabhakar Patil Senior Advisor, LG Chem Ying Shi Research Engineer, Energy Storage, NREL M. Scott Trimboli Assistant Professor of ECE, University of Colorado Colorado Springs Regan Zane USTAR Professor of ECE, Utah State University 17