Increasing Battery Potential: Estimation & Control of Electrochemical Models Scott Moura Assistant Professor | eCAL Director University of California, Berkeley Ming Hsieh Institute CommNetS Seminar | USC | Los Angeles, CA Download: https://ecal.berkeley.edu/pubs/slides/Moura-USC-Batts-Slides.pdf Scott Moura | UC Berkeley ElectroChemical model based Control (ECC) March 29, 2017 | Slide 1
eCAL Battery Controls Team @ UC Berkeley Current Researchers Prof. Scott Moura | Dr. Satadru Dey | Leo Camacho-Solorio | Saehong Park | Dong Zhang | ZACH GIMA Supporting Researchers Prof. Xiaosong Hu | Dr. Hector Perez | Defne Gun | Preet Gill | Reve Ching | Zane Liu | Dylan Kato Scott Moura | UC Berkeley ElectroChemical model based Control (ECC) March 29, 2017 | Slide 2
A Golden Era Keyword Search: Battery Systems and Control No. of Publications 3000 2000 1000 0 1985 1990 1995 2000 2005 2010 2015 Year Scott Moura | UC Berkeley ElectroChemical model based Control (ECC) March 29, 2017 | Slide 3
The Battery Problem Needs: Cheap, high energy/power, fast charge, long life Scott Moura | UC Berkeley ElectroChemical model based Control (ECC) March 29, 2017 | Slide 4
The Battery Problem Needs: Cheap, high energy/power, fast charge, long life Reality: Expensive, conservatively design/operated, die too quickly Scott Moura | UC Berkeley ElectroChemical model based Control (ECC) March 29, 2017 | Slide 4
The Battery Problem Needs: Cheap, high energy/power, fast charge, long life Reality: Expensive, conservatively design/operated, die too quickly Some Motivating Facts 1000 USD / kWh (2010) ∗ 485 USD / kWh (2012) ∗ 350 USD / kWh (2015) ∗∗ EV Batts 125 USD / kWh for parity to IC engine Only 50-80% of available capacity is used Range anxiety inhibits adoption Lifetime risks caused by fast charging Scott Moura | UC Berkeley ElectroChemical model based Control (ECC) March 29, 2017 | Slide 4
The Battery Problem Needs: Cheap, high energy/power, fast charge, long life Reality: Expensive, conservatively design/operated, die too quickly Scott Moura | UC Berkeley ElectroChemical model based Control (ECC) March 29, 2017 | Slide 4
The Battery Problem Needs: Cheap, high energy/power, fast charge, long life Reality: Expensive, conservatively design/operated, die too quickly Some Motivating Facts 1000 USD / kWh (2010) ∗ 485 USD / kWh (2012) ∗ 350 USD / kWh (2015) ∗∗ EV Batts 125 USD / kWh for parity to IC engine Only 50-80% of available capacity is used Range anxiety inhibits adoption Lifetime risks caused by fast charging Two Solutions Design better batteries Make current batteries better (materials science & chemistry) (estimation and control) ∗ Source: MIT Technology Review, “The Electric Car is Here to Stay.” (2013) ∗∗ Source: Tesla Powerwall. (2015) Scott Moura | UC Berkeley ElectroChemical model based Control (ECC) March 29, 2017 | Slide 4
Outline BACKGROUND & BATTERY ELECTROCHEMISTRY FUNDAMENTALS 1 ESTIMATION AND CONTROL PROBLEM STATEMENTS 2 ELECTROCHEMICAL MODEL 3 STATE ESTIMATION 4 CONSTRAINED OPTIMAL CONTROL 5 SUMMARY AND OPPORTUNITIES 6 Scott Moura | UC Berkeley ElectroChemical model based Control (ECC) March 29, 2017 | Slide 5
History Luigi Galvani, 1737-1798, Experiments on frog legs Physicist, Bologna, Italy “Animal electricity” Dubbed “galvanism” First foray into electrophysiology Alessandro Volta, 1745-1827 Voltaic Pile Monument to Volta in Como Physicist, Como, Italy Scott Moura | UC Berkeley ElectroChemical model based Control (ECC) March 29, 2017 | Slide 6
Comparison of Lithium Ion (Cathode) Chemistries Lithium Cobalt Oxide (LiCO 2 ) Lithium Manganese Oxide (LiMn 2 O 4 ) Lithium Iron Phosphate (LiFePO 4 ) Lithium Nickel Manganese Cobalt Lithium Nickel Cobalt Aluminum Oxide Lithium Titanate (Li4Ti 5 O 12 ) Oxide (LiNiMnCoO 2 ) (LiNiCoAlO 2 ) Source: http://batteryuniversity.com/learn/article/types_of_lithium_ion Scott Moura | UC Berkeley ElectroChemical model based Control (ECC) March 29, 2017 | Slide 7
Outline BACKGROUND & BATTERY ELECTROCHEMISTRY FUNDAMENTALS 1 ESTIMATION AND CONTROL PROBLEM STATEMENTS 2 ELECTROCHEMICAL MODEL 3 STATE ESTIMATION 4 CONSTRAINED OPTIMAL CONTROL 5 SUMMARY AND OPPORTUNITIES 6 Scott Moura | UC Berkeley ElectroChemical model based Control (ECC) March 29, 2017 | Slide 8
Battery Models Equivalent Circuit Model (a) OCV-R Scott Moura | UC Berkeley ElectroChemical model based Control (ECC) March 29, 2017 | Slide 9
Battery Models Equivalent Circuit Model (a) OCV-R (b) OCV-R-RC (c) Impedance Scott Moura | UC Berkeley ElectroChemical model based Control (ECC) March 29, 2017 | Slide 9
Battery Models Electrochemical Model Equivalent Circuit Model (a) OCV-R - - sep sep + + 0 L 0 L L 0 x (b) OCV-R-RC Cathode Anode Separator c s - ( r ) c s + ( r ) e - Li + e - c ss - c ss + (c) Impedance r r c e ( x ) Li x C 6 Li 1-x MO 2 Electrolyte Scott Moura | UC Berkeley ElectroChemical model based Control (ECC) March 29, 2017 | Slide 9
Safely Operate Batteries at their Physical Limits Electrochemical model-based limits of operation ECM-based limits of operation ECM-based limits of operation Terminal Voltage Overpotential Surface concentration Cell Current Scott Moura | UC Berkeley ElectroChemical model based Control (ECC) March 29, 2017 | Slide 10
ElectroChemical Controller (ECC) Measurements I r ( t ) V ( t ), T ( t ) I ( t ) EChem-based Battery Cell Controller + Innovations _ EChem-based Estimated ^ ^ x ( t ), θ ( t ) States & Params State/Param ^ ^ V ( t ), T ( t ) Estimator ElectroChemical Controller (ECC) Scott Moura | UC Berkeley ElectroChemical model based Control (ECC) March 29, 2017 | Slide 11
ElectroChemical Controller (ECC) The State Estimation Problem Measurements I r ( t ) V ( t ), T ( t ) I ( t ) EChem-based Battery Cell Controller + Innovations _ EChem-based Estimated ^ ^ x ( t ), θ ( t ) States & Params State/Param ^ ^ V ( t ), T ( t ) Estimator ElectroChemical Controller (ECC) The State (a.k.a. SOC) Estimation Problem Given measurements of current I ( t ) , voltage V ( t ) , and temperature T ( t ) , estimate the electrochemical states of interest. Exs: bulk solid phase Li concentration (state-of-charge) surface solid phase Li concentration (state-of-power) Scott Moura | UC Berkeley ElectroChemical model based Control (ECC) March 29, 2017 | Slide 12
ElectroChemical Controller (ECC) The Parameter Estimation Problem Measurements I r ( t ) I ( t ) V ( t ), T ( t ) EChem-based Battery Cell Controller + Innovations _ EChem-based Estimated ^ ^ x ( t ), θ ( t ) State/Param States & Params ^ ^ Estimator V ( t ), T ( t ) ElectroChemical Controller (ECC) The Parameter (a.k.a. SOH) Estimation Problem Given measurements of current I ( t ) , voltage V ( t ) , and temperature T ( t ) , estimate uncertain parameters related to SOH. Exs: cyclable lithium (capacity fade) volume fraction (capacity fade) solid-electrolyte interface resistance (power fade) Scott Moura | UC Berkeley ElectroChemical model based Control (ECC) March 29, 2017 | Slide 13
ElectroChemical Controller (ECC) The Constrained Control Problem Measurements I r ( t ) V ( t ), T ( t ) I ( t ) EChem-based Battery Cell Controller + Innovations _ EChem-based Estimated ^ ^ x ( t ), θ ( t ) States & Params State/Param ^ ^ Estimator V ( t ), T ( t ) ElectroChemical Controller (ECC) The Constrained Control Problem Given measurements of current I ( t ) , voltage V ( t ) , and temperature T ( t ) , control current such that critical electrochemical variables are maintained within safe operating constraints. Exs: saturation/depletion of solid phase and electrolyte phase side-reaction overpotentials internal temperature Scott Moura | UC Berkeley ElectroChemical model based Control (ECC) March 29, 2017 | Slide 14
Outline BACKGROUND & BATTERY ELECTROCHEMISTRY FUNDAMENTALS 1 ESTIMATION AND CONTROL PROBLEM STATEMENTS 2 ELECTROCHEMICAL MODEL 3 STATE ESTIMATION 4 CONSTRAINED OPTIMAL CONTROL 5 SUMMARY AND OPPORTUNITIES 6 Scott Moura | UC Berkeley ElectroChemical model based Control (ECC) March 29, 2017 | Slide 15
Battery Electrochemistry Model The Doyle-Fuller-Newman (DFN) Model - - sep 0 L 0 sep + 0 + L L x Anode Separator Cathode c s - ( r ) c s + ( r ) e - Li + e - c ss - c ss + r r Li x C 6 c e ( x ) Li 1-x MO 2 Electrolyte Key References: K. Thomas, J. Newman, and R. Darling, Advances in Lithium-Ion Batteries. New York, NY USA: Kluwer Academic/Plenum Publishers, 2002, ch. 12: Mathematical modeling of lithium batteries, pp. 345-392. N. A. Chaturvedi, R. Klein, J. Christensen, J. Ahmed, and A. Kojic, “Algorithms for advanced battery-management systems,” IEEE Control Systems Magazine, vol. 30, no. 3, pp. 49-68, 2010. J. Newman. (2008) Fortran programs for the simulation of electrochemical systems. [Online]. Available: http://www.cchem.berkeley.edu/jsngrp/fortran.html Scott Moura | UC Berkeley ElectroChemical model based Control (ECC) March 29, 2017 | Slide 16
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