Advanced Research Projects Agency-Energy Research Articles

Research Opportunities for CO2 Utilization and Negative Emissions at the Gigatonne Scale

Arun Majumdar is the Jay Precourt Professor in the Department of Mechanical Engineering and Co-director of the Precourt Institute for Energy at Stanford University. He served as the Founding Director (2009–2012) of the Advanced Research Projects Agency – Energy (ARPA-E) and the Acting Undersecretary for Energy (2011–2012) under Secretary Steven Chu in the US Department of Energy. He also served as Vice Chair of the Advisory Board (2014–2017) to Secretary Ernest Moniz. Arun Majumdar has published widely on the science and engineering of conversion, storage, and transport of energy, especially in nanostructured materials and devices. He is a member of the US National Academy of Engineering and the American Academy of Arts and Science. John Deutch is an emeritus Institute Professor at the Massachusetts Institute of Technology where he has been a member of the faculty since 1970. He has served as Chairman of the Department of Chemistry, Dean of Science, and Provost. In the Carter Administration, he served as Director of Energy Research (1977–1979), Acting Assistant Secretary for Energy Technology (1979), and Undersecretary (1979–1980) in the US Department of Energy. He has been a member of the President's Nuclear Safety Oversight Committee (1980–1981), the White House Science Council (1985–1989), the President's Committee of Advisors on Science and Technology (1997–2001), and the Secretary of the Energy Advisory Board (2008–2016). John Deutch has published widely on technical and policy aspects of energy and the environment and has been a member of the board of directors or of the technical advisory committees of several energy companies.

Direct Estimation of Parameters from Charge-Discharge Curves of Lithium-Ion Batteries Using Pseudo-2 Dimensional (P2D) Models

Lithium-ion batteries play a vital role in electric vehicles and energy storage systems. In order to monitor, predict and control the performance of lithium-ion batteries, intensive efforts by various researchers are being pursued to develop an electrochemical model-based battery management systems (BMS)1. The accuracy and predictability of the model used are of great importance to these systems, which heavily depends on the precision of the parameters needed for these models. Estimation of the model parameters is critical for the BMS to perform efficiently. Moreover, the parameters of a battery might change as the battery ages. Tracking those parameters is required to maximize the efficiency of the BMS. In addition to this, the parameters of the battery need to be estimated online. Estimating parameters for the lithium-ion batteries is challenging due to the complexity in solving the governing equations, and the possibility of degeneracy, with multiple sets of parameter values that might provide the same accuracy for fitting charge/discharge curves. Parameter estimation of various lithium-ion battery systems has been done for different models, including equivalent circuit model2, single particle model3 and pseudo 2D (P2D) model4,5. However, most of these models are built on known open circuit voltage curves for individual electrodes, along with geometric and transport base parameters for cathode/anode thicknesses, porosities etc. In this presentation, we propose a method to estimate various parameters of the P2D model, along with the open circuit potential of the cathode. The parameters are estimated using the experimental charge/discharge curves for two different types of cell formats. The accuracy of the parameters is validated by implementing a dynamic charging profile, and measuring the error in the voltage-time curves experimentally. We also attempt to address the possibility and relative importance/impact of estimating all the parameters needed for the P2D model using just charge/discharge curves. This will be facilitated using our past results in fast simulation of battery models6,7. Acknowledgements The authors would like to thank the United States Department of Energy (DOE) for the financial support for this work through the Advanced Research Projects Agency – Energy (ARPA-E) award #DE-AR0000275. References V. Ramadesigan, P. W. C. Northrop, S. De, S. Santhanagopalan, R. D. Braatz and V. R. Subramanian, J. Electrochem. Soc., 159, R31 (2012).Y. Hu, S. Yurkovich, Y. Guezennec and B. J. Yurkovich, Control Eng. Pract., 17, 1190 (2009).A. P. Schmidt, M. Bitzer, Á. W. Imre and L. Guzzella, J. Power Sources, 195, 5071 (2010).J. C. Forman, S. J. Moura, J. L. Stein and H. K. Fathy, J. Power Sources, 210, 263 (2012).S. Santhanagopalan, Q. Guo and R. E. White, J. Electrochem. Soc., 154, A198 (2007).V. R. Subramanian, V. Boovaragavan, and V. D. Diwakar, Electrochem. Solid St., 10, A255 (2007).M. T. Lawder, V. Ramadesigan, B. Suthar and V. R. Subramanian, Computers & Chemical Engineering, 82, 283 (2015).

Is There Room for Theory in Data Science? Encoding Physics into Machine Learning Algorithms

Batteries are complex electrochemical devices whose short-term and long-term operational lives are highly dependent on their construction, materials, and use cases. Models have been developed and used to improve the performance of batteries by providing different material design, model based control and model based battery management systems1. There is a recent trend in data-science-based approaches for modeling different devices and processes2,3. It has been demonstrated that using machine learning techniques can retain 99.9% of the accuracy of a complex model while improving solve time by four orders of magnitude, which can allow for the use of high accuracy models in a real-time control scenario3. When data science techniques, and machine learning in particular, are implemented in battery systems, they are typically used as black boxes to predict battery behavior4. In this talk, we will highlight why this approach is ill suited for use in physics-based battery models, which are highly nonlinear, and how an understanding of the system can improve the result of the machine learning effort for use in optimization and control, while still allowing for the prediction of important internal states. Additionally, we will highlight ways to employ the flexibility of machine learning algorithms to allow for more accurate state estimation and voltage prediction. Real-time model-based control has demonstrated significant improvements in time remaining, state of charge, and state of health, even using simple equivalent circuit models5. By reducing the order and solve time of higher complexity models, we believe that better predictability is possible. Acknowledgments The authors thank the United States Department of Energy (DOE) for the financial support for this work though the Advanced Research Projects Agency – Energy (ARPA-E) award #DEAR0000275, as well as the Clean Energy Institute (CEI) through the DIRECT program at the University of Washington. References Y. Dai, and V. Srinivasan,. J. Electrochem. Soc., 163, A406 (2016).D. A. C. Beck, J. M. Carothers, V. R. Subramanian, and J. Pfaendtner, “Data science: Accelerating innovation and discovery in chemical engineering,” AIChE J., vol. 62, no. 5, pp. 1402–1416, May 2016.Blake R. Hough, author. Jim Pfaendtner, degree supervisor. 2016 Thesis (Ph. D.)--University of Washington,2016.J. Liu, A. Saxena, K. Goebel, B. Saha, and W. Wang, “An Adaptive Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-ion Batteries,” NATIONAL AERONAUTICS AND SPACE ADMINISTRATION MOFFETT FIELD CA AMES RESEARCH CENTER, NATIONAL AERONAUTICS AND SPACE ADMINISTRATION MOFFETT FIELD CA AMES RESEARCH CENTER, Oct. 2010.Williard, Nick, Wei He, and Michael Pecht. "Model Based Battery Management System for Condition Based Maintenance." Technical Program for MFPT 2012, The Prognostics and Health Management Solutions Conference - PHM: Driving Efficient Operations and Maintenance, 2012

Model Based Battery Management Systems (BMS) – from Theory to Practice

Lithium-ion batteries are now extensively being used as the primary storage source. Capacity and power fade, and slow recharging times are key issues that restrict its use in many applications. Battery management systems (BMS) are critical to address these issues, along with ensuring its safety.1 An efficient BMS should be able to accurately predict the internal states of the battery such as the sate-of-charge (SOC), state-of-health (SOH) using the voltage and current measurements from the battery, along with maintaining safe operations of the battery.2 The accurate determination of these internal states holds the key for the optimal performance of batteries. The effect of inaccurate state estimation leads to conservative use of batteries and at worst, may cause potentially unsafe situations leading to thermal runaway.2,3 This necessitates the development of accurate models that could predict different states of the battery more accurately, which at the same time are not as computationally complicated to be deployable in current BMS.3-5 In our past work, we have developed physics based fast-solving electrochemical models which are computationally efficient.6,7 We have also shown the development of optimal charging protocols8-11 using these models, and have experimentally shown that such an approach to could lead to a reduction in battery footprint by atleast 20%, or an enhancement in cycle life by 40%. We have also demonstrated the fast-solving models and the optimal operation of batteries, on low-cost microcontrollers with a low memory footprint, which could control the batteries in real time, mimicking an actual BMS. Based on a detailed technology to market analysis, this decrease in footprint by 20% has significant impact in EVs, HEVs and grid applications. CAPEX cost can be significantly reduced for microgrids, making it more attractive to install the batteries. This talk is aimed at the commercialization pathway of the developed next-generation BMS to the EV, consumer electronics and grid-scale market through a student-founded UW-based startup. The interplay between the fundamental depth in modeling, choice of numerical algorithms, application driven problem formulation and impact in industries will be presented. Acknowledgements The work presented herein was funded in part by the Advanced Research Projects Agency – Energy (ARPA-E), U.S. Department of Energy, under Award Number DE-AR0000275 along with the Clean Energy Institute (CEI) at the University of Washington (UW). References K. Kelly, M. Mihalic and M. Zolot, in Proceeding of the 17th Annual Battery Conference on Applications and Advances (2002).V. Pop, H. J. Bergveld, D. Danilov, P. P. L. Regtien and P. H. L. Notten, Battery management systems: accurate state-of-charge indication for battery powered applications, Springer Verlag (2008).J. Newman, K. E. Thomas, H. Hafezi, and D. R. Wheeler, J. Electrochem. Soc., 150, A176 (2003).M. Doyle, T. F. Fuller and J. Newman, J. Electrochem. Soc., 140, 1526 (1993).T.F. Fuller, M. Doyle and J. Newman, J. Electrochem. Soc., 141, 1 (1994).P.W.C. Northrop, V. Ramadesigan, S. De, and V. R. Subramanian, J. Electrochem. Soc., 158(12), A1461 (2011).P.W.C. Northrop, M. Pathak, D. Rife, S. De, S. Santhanagopalan and V. R. Subramanian, J. Electrochem. Soc., 162(6), A940 (2015).L.T. Biegler, A.M. Cervantes, and A. Wachter, Advances in simultaneous strategies for dynamic process optimization, Chemical Engineering Science, 57(4):575, 2002.B. Suthar, V. Ramadesigan, S. De, R. D. Braatz and V.R. Subramanian, Phy.Chem.Chem. Phy., 16(1), 277 (2014).B. Suthar, P.W.C. Northrop, R.D. Braatz and V.R. Subramanian, J. Electrochem. Soc., 161(11), F3144 (2014).M. Pathak, D. Sonawane, S. Santhanagopalan, R.D. Braatz and V.R. Subramanian, ECS Trans., 75(23), 51 (2017).

Probing the Size-Exclusion Properties of Redox Flow Battery Separator Membranes Using Metal-Ligand Complexes

Redox flow batteries (RFBs) are among one of the most promising technologies being explored for grid-scale energy storage from discontinuous power sources such as wind and solar.1 This has resulted in a need for continued development and understanding of electrolytes and membranes employed in RFBs as a means of improving performance while reducing cost. New approaches in membrane design rely on size-exclusion to reduce parasitic losses resulting from water and electrolyte crossover that presently plagues ionomer membranes within the perfluorosulfonic acid (PFSA) family (e.g. Nafion, Aquivion, and 3M PFSA).2 The structure-property relationships of PFSAs are well-known and studied,3 providing the initial framework for utilization of ligands to understand transport through next-generation membranes. In this work, we discuss our efforts in developing a systematic approach towards probing the effective size-exclusion properties of the polymeric membranes by employing iron-ligand complexes. A series of aminopolycarboxylic acid ligands, shown in Figure 1, that strongly complex with iron were selected to explore the effect of molecule size on crossover in PFSAs with varying domain sizes, controlled via side chain length. We show decreases in permeability with increased size of the iron-ligand complex, providing information regarding the hydrophilic domain network of these ionomer membranes. By measuring the crossover of these complexes using diffusion cells, we explore the size-exclusion properties of PFSAs using relevant molecules for future iron RFB applications. Acknowledgements This work was funded as a sub-contract under the Integration and Optimization of Novel Ion-Conducting Solids program (IONICS), by the Advanced Research Projects Agency-Energy (ARPA-E), of the US Department of Energy under award number DE-AR0000773. Perry, M. L.; Weber, A. Z., Advanced Redox-Flow Batteries: A Perspective. J Electrochem Soc 2016, 163 (1), A5064-A5067.Darling, R.; Gallagher, K.; Xie, W.; Su, L.; Brushett, F., Transport Property Requirements for Flow Battery Separators. J Electrochem Soc 2015, 163 (1), A5029-A5040.Kusoglu, A.; Weber, A. Z., New Insights into Perfluorinated Sulfonic-Acid Ionomers. Chem Rev 2017, 117 (3), 987-1104. Figure 1

Safe Impact Resistant Electrolytes (SAFIRE)

We present work on the development of inherently safer multifunctional electrolytes which are designed to undergo a massive rheological response upon application of a shear force. These electrolytes are built from colloidal particles stabilized in an aprotic electrolyte. Upon impact the particles align forming an apparent solid. This solid barrier prevents electrodes from touching during an impact resulting in a spontaneous discharge and thermal runaway. This presentation will talk about the chemistry of these materials, the cycling of full cells, and ionic conductivity of the materials. The work presented herein was funded by the Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy, under Award Number DE-AR-0869-1617. The authors particularly thank Ping Liu, Susan Babinec, and Julian Sculley for their helpful discussions.

(Invited) Kinetic Characterization of Ir, Pt, and Irpt-Alloy Nanocatalysts for Ammonia Oxidation Reaction

Direct ammonia fuel cells (DAFC) is an attractive alternative of hydrogen fuel cells because of lower cost in storage and distribution of liquid fuel than hydrogen gas. A drawback is the sluggish kinetics of ammonia oxidation reaction (AOR). Based on previous DAFC studies and measurements using rotating disk electrodes (RDEs), Ir, Pt, and IrPt-alloy nanoparticles are considered as the most active AOR catalysts. However, many questions remain open in correlating RDE results obtained at ambient temperature with the catalysts’ performance in DAFC at elevated temperatures. This is due to low sustainable AOR currents at sufficiently low potentials at ambient temperature and complicated temperature effect on AOR kinetics. Here, we report an AOR study using gas diffusion electrode (GDE) in 1 M KOH solution at temperatures up to 60 oC. We kept ammonia concentration constant with time at temperatures higher than the boiling point of ammonia by saturating the solution with argon gas that bubbled through concentrated ammonia before entering the electrochemical cell. Three commercial nanocatalysts, Ir, Pt, and PtIr 1:1 alloy particles with 40 wt% metal on carbon support, were used to establish test protocol and to benchmark the platinum group metal (PGM) mass activity. The three catalysts exhibited distinct kinetic behaviors in terms of onset potential, maximum current, and activity enhancement factor with increasing temperature. Clearly characterized AOR kinetic behavior can be explained using the reaction pathways proposed based on previous DFT calculations. We’ll discuss in detail why Ir has lower onset potential, but lower peak current than Pt does, why temperature enhancement factor is higher for Ir than Pt, what are the inactive intermediates and how catalysts deactivated due to increased coverage of less active AOR intermediates at potentials above and below 0.6 V. New insights can help developing advanced AOR catalysts and making effective DAFCs. The method and concepts developed are generally applicable for studying oxidation of other liquid fuels, such as, methanol and ethanol as their oxidation reactions also involve multiple intermediates and pathways. Acknowledgements This research was funded by the Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy, under Award Number DE-AR0000805 and by the Division of Chemical Sciences, Geosciences and Biosciences Division, US Department of Energy under contract DE-SC0012704.

ARPA-E funding for high-efficiency SOFC DG system projects

The US Department of Energy's Advanced Research Projects Agency-Energy (ARPA-E) has announced $16 million in funding for eight projects as part of its Innovative Natural-gas Technologies for Efficiency Gain in Reliable and Affordable Thermochemical Electricity-generation (INTEGRATE) programme.

Acoustic sensing of macroalgae (kelp) for large-scale marine biomass production

An important component of the future use of large-scale offshore farms to grow macroalgae (kelp) will be remote monitoring of infrastructure, the environment, and plant health over areas so large that manual inspection is not practical. A new program, the Advanced Research Projects Agency-Energy's Macroalgae Research Inspiring Novel Energy Resources (ARPA-E MARINER), has the long-term goal of domestic energy production using biofuel derived from macroalgae. As part of that program, an integrated sensing system is being developed for unmanned underwater vehicle (UUV) monitoring of infrastructure, macroalgae growth, water properties, and associated organisms in experimental offshore macroalgae farms occupying areas square kilometers in size. A critical component of this monitoring system is acoustic sensing using a split-beam sonar system. Time-of-flight and volume backscattering data from the echosounder will be used to determine the thickness of growth and percentage volume inhabited of macroalgae. The objective is to provide a map correlated to biomass distribution variability across the farm area. Data will also be collected on aggregations of fish and zooplankton both within and outside the farm. Early results from local tests on sugar kelp will be presented, including initial research into the correlation between these acoustic data and biomass.

Simultaneous Pursuit of Discovery and Invention in the US Department of Energy

The division of “basic” and “applied” research is embedded in federal R&D policy, exemplified by the separation of science and technology in the organizational structure of the US Department of Energy (DOE). In this work, we consider a branch of DOE that shows potential to operate across this boundary: the Advanced Research Projects Agency – Energy (ARPA-E). We construct a novel dataset of nearly 4,000 extramural financial awards given by DOE from 2010 to 2015, primarily to businesses and universities. We collect the early knowledge outputs of these awards from Web of Science and the United States Patent and Trademark Office. Compared to similar awards from other parts of DOE, ARPA-E awards are more likely to jointly produce both a publication and a patent, with at least 5 times higher odds. ARPA-E awards have been productive in creating new technology, without a detrimental effect on the production of new scientific knowledge. This observation suggests the unity of research activities which are often considered separate: that which produces discoveries and that which produces inventions.

Uncertainty and Individual Discretion in Allocating Research Funds

There is a long-standing tradition in public research funding agencies of distributing funds via peer review, which aggregates evaluations of proposed research ideas from a group of external experts. Despite complaints that this process is biased against novel ideas, there is poor understanding of an alternative system that may overcome this bias: the use of individual discretion. Here, we conduct the first quantitative study of how individual discretion affects a research funding portfolio. Using internal project selection data from the Advanced Research Projects Agency-Energy (ARPA-E), we describe how a portfolio of projects selected by individual discretion differs from a portfolio of projects selected by traditional peer review. We show that ARPA-E program directors prefer to fund proposals with greater disagreement among experts, especially if at least one reviewer thinks highly of the proposal. This preference leads ARPA-E to fund more uncertain and creative research ideas, which supports the agency’s mission of pursuing novel ideas for transformational energy technology.

Fuel Cells Operating at 200 to 500 Celsius: Lessons Learned from the ARPA-E REBELS Program

In 2014, the Advanced Research Projects Agency-Energy (ARPA-E) launched the Reliable Electricity Based on ELectrochemical Systems (REBELS) program with the goal of pursuing alternate fuel cell materials and operating conditions that could enable system cost reductions and new electrochemical functionality. This presentation will briefly revisit the original REBELS vision by elaborating the potential benefits of operation in an intermediate temperature range of 200-500 C. Trends in the U.S. electrical grid will be discussed, which reveal the benefits of enhanced fuel cell functionality such as in-situ charge storage and gas-to-liquids (GTL) capabilities. The most significant technical accomplishments from REBELS project teams will be highlighted, including several examples in which the program objective of a current density of 200 mA/cm2 at 0.78 V and 500 C on methane fuel was achieved. Furthermore, teams were able to demonstrate thousands of hours of operation on internally-reformed methane fuel at 500 C with no evidence of coke formation. Fuel cells with charge storage functionality in or near the anode showed a more rapid response to large changes in current density and a lower voltage decrease with high fuel utilization. The needs for further techno-economic assessment to evaluate this concept will be highlighted. Progress towards fuel cells that can also convert methane to liquid fuels will be summarized. Finally, overall lessons learned from the REBELS program will be discussed, such as the ability of ceramic proton conductors to deliver high current density at 500 C on non-hydrogen fuels with minimal degradation. The need for future work in this field will be stressed, especially a continued examination of whether stacks in this temperature range possess the appropriate combination of performance and cost to accelerate the commercialization of high efficiency fuel cells worldwide.

SLIPS Technologies raises new funds

SLIPS Technologies, a slippery-coatings start-up, has raised $8.6 million in new funding. The firm was founded in 2014 with technology from Joanna Aizenberg, a chemistry professor at Harvard University’s Wyss Institute. Close to two-thirds of the funding is from Anzu Partners, BASF Venture Capital, entrepreneur Hansjörg Wyss, and the Massachusetts Clean Energy Center. SLIPS got $3.0 million of the funding in a grant from the U.S. Department of Energy’s Advanced Research Projects Agency-Energy to test antifouling paints.

National Academies gives ARPA-E good grades

A federal energy research program that U.S. President Donald J. Trump has proposed to defund earned positive marks from the National Academies of Sciences, Engineering & Medicine. In a congressionally mandated report released on June 13, the National Academies assesses the Advanced Research Projects Agency-Energy (ARPA-E). The report says the program is delivering on its mission to develop early-stage energy innovations and pave a path to their commercialization. ARPA-E focuses on high-risk and potentially transformative technologies and opportunities that neither other federal agencies nor the private sector is funding. This, the report says, is one of ARPA-E’s strengths. “ARPA-E has made significant contributions to energy R&D that likely would not take place absent the agency’s activities,” said Pradeep Khosla, chair of the committee that wrote the report and chancellor of the University of California, San Diego. Congress created ARPA-E in 2007, but the program did not get significant funding until

Threatened energy research program gets high marks

A review of the Advanced Research Projects Agency–Energy finds that the program is meeting its goals. A review of the Advanced Research Projects Agency–Energy finds that the program is meeting its goals.

FuelCell Energy wins ARPA-E contract to develop SOFC energy storage solution

Connecticut-based FuelCell Energy has been awarded a $3.0 million contract from the Advanced Research Projects Agency-Energy (ARPA-E) of the US Department of Energy, to advance the commercialisation of cost-effective and clean long-duration energy storage based on FCE's solid oxide cell technology, which can alternate between electrolysis and fuel cell power generation.

Fuel Cells Operating at 200 to 500 Celsius: Lessons Learned from the ARPA-E REBELS Program

The Advanced Research Projects Agency-Energy (ARPA-E) launched the Reliable Electricity Based on ELectrochemical Systems (REBELS) program in 2014 to pursue alternate fuel cell materials and operating conditions that could enable system cost reductions and new electrochemical functionality. The program objective of a current density of 200 mA/cm2 at 0.78 V and 500oC on methane fuel in a button cell was achieved. Several teams demonstrated thousands of hours of operation on internally-reformed methane fuel at 500oC without coking, both with and without a significant steam co-feed. Fuel cells with charge storage functionality in or near the anode showed an improved response to variations in current density, with a lower voltage decrease at higher fuel utilization. Progress was also made on using fuel cells to convert methane to liquid fuels. Further research is needed to evaluate whether stacks in this temperature range possess the appropriate combination of performance and cost to accelerate the commercialization of high efficiency fuel cells.

Science buoyed in 2017 U.S. spending legislation

Congress approved a bipartisan 2017 spending bill on May 4 that shows support for science, including many agencies that fund chemistry research and regulation. The legislation’s flat funding for many agencies feels like a win for research, in part because the situation for science has sounded dire in the months since President Donald J. Trump took office. The bill (H.R. 244) includes a $2 billion increase for the National Institutes of Health—a 6.2% boost over 2016 to $34.1 billion in 2017—and none of the other cuts Trump suggested. Just weeks ago, Trump proposed massive cuts to science for the current fiscal year, including a $2 billion cut for NIH. And his preliminary fiscal 2018 proposal would slash funding for research and for the Environmental Protection Agency and completely eliminate the Chemical Safety & Hazard Investigation Board (CSB) and the Advanced Research Projects Agency-Energy (ARPA-E). Congress needed to pass a fiscal

Generic Model Control for Lithium-Ion Batteries - II

Battery Management Systems (BMS) are critical to safe and efficient operation of lithium-ion batteries and accurate prediction of the internal states [1]. Smarter BMS that can estimate and implement optimal charging profiles in real-time are important for advancement of the Li-ion battery technology. Estimating optimal profiles using physics-based models is computationally expensive because of the non-linear and stiff nature of the model equations, involving the need for constrained non-linear optimization [2-12]. In this work, we present an alternative approach to control batteries applying the concept of differential geometry. This approach is known as Generic Model Control (GMC), or Reference System Synthesis [13,14]. This work enables robust stabilization and control of battery models to set-point as an alternative approach, eliminating the need to perform optimization of nonlinear models. This work builds on our previous work of implementation of GMC for differential variables occurring in a model consisting of differential algebraic equations (DAEs), and extends it to set point based control for algebraic variables. Conventional GMC is applicable for cases in which the output is typically independent of control [13]. We also extend it for cases for which the measured variable can be an explicit function of the manipulated variable. As compared to the generic model control approaches implemented by previous researchers, we have implemented the same concept using direct DAE numerical solvers [15]. The results are presented for single input single objective, and for constrained problems. The proposed approach is compared with standard control approaches and is found to be equally robust if not better. Acknowledgements The work presented herein was funded in part by the Advanced Research Projects Agency – Energy (ARPA-E), U.S. Department of Energy, under Award Number DE-AR0000275 and the Clean Energy Institute at the University of Washington, Seattle.

Using Data Science to Improve Battery Performance: How Data Analytics Can Help Drive Design and Use Decisions

Batteries are complex electrochemical devices whose short-term and long-term operational lives are highly dependent on their construction, materials, and use cases. Models have been developed and used to improve the performance of batteries by providing different material design, model based control and model based battery management systems1. There is a recent trend in data-science-based approaches for modeling different devices and processes2,3. In this talk, we will review different data science methods and approaches in the context of parameter estimation, optimal control and mechanism elucidation for lithium-ion batteries. In particular, we will discuss opportunities and challenges in applying the same for battery models – for model reduction, data set training, online estimation and control. It has been demonstrated that using machine learning techniques can retain 99.9% of the accuracy of a complex model while improving solve time by four orders of magnitude, which can allow for the use of high accuracy models in a real-time control scenario3. We will be attempting to demonstrate that this is true for battery models as well, which typically have large cpu time requirements. Real-time model-based control has demonstrated significant improvements in time remaining, state of charge, and state of health, even using simple equivalent circuit models4. By reducing the order and solve time of higher complexity models, we believe that better predictability is possible. Acknowledgments The authors thank the United States Department of Energy (DOE) for the financial support for this work though the Advanced Research Projects Agency – Energy (ARPA-E) award #DEAR0000275.