CAREER: An Ecologically-Inspired Approach to Battery Lifetime Analysis and Testing

National Science Foundation

Overview
Batteries are increasingly relied upon to provide multiple services during applications (e.g. traction in an electric vehicle, vehicle-to-grid, ancillary services) and to act as the ultimate resiliency element (e.g. electric vehicles used as power units during Hurricane Sandy). However, the ability to perform these diverse services is compromised by battery aging phenomena that eventually lead to failure. Understanding of how service conditions and context affect battery aging is limited due to a) battery high context dependency on generation and load dynamics, and environmental conditions; b) the multi-scale cell and module nature of battery systems; and c) the fact that a battery itself varies with age, as batteries are repurposed after a first life (e.g. electric vehicle) into a second life (e.g. grid or residential).

This CAREER project aims to understand battery aging dynamics as context-dependent, and to provide a unified theory that links application-level events and conditions with cell- and module-level aging events. The Pl hypothesizes that a battery electrochemical nature and aging, multi-scale system, observability challenges, and its context-dependency can all be modeled using ecological tools, with ecology defined as a branch of biology that explores organism relationships to one another and to their environment. Therefore, methods proven useful to study ecological relationships are well suited to study battery life, and can provide new knowledge, testing and estimation techniques. This project draws from two pertinent areas in ecology: 1) multi-scale field testing and 2) modeling of interrelationships among ecosystem elements to understand coupled effects and improve remaining life predictions. Hence, the research objectives are: 1 ) Identify a battery context and its observability through sensors and data in real deployment conditions for two lives (electric vehicle and grid); 2) Optimize a methodology to translate real-life conditions into the laboratory; 3) Design a large multi-scale testing platform in the laboratory for new and aged cells and modules that mimics real-life conditions; 4) Explore multi-scale battery dynamics and aging by developing reasoning networks that capture the whole battery context variations throughout its scales, reaching the application level; develop theories that link these networks across lives; design battery management systems that can learn to construct and apply these networks to improve their decision making and prediction.

Intellectual Merit
This novel project will provide knowledge and perspectives to two fields by capitalizing upon the similarities between battery context-dependencies, battery life, and ecological systems. This new outlook will provide a unified theory for testing, estimation and management of batteries across cell, module, pack, and application scales and life scales in a research field that up to this point has been disconnected between scales. Testing approaches, interrelationship models, and estimation methods used in ecology are predicted to improve upon present, state-of-the-art battery research methods to provide economic, resiliency and environmental benefits by better understanding and leveraging the unique, time-dependent relationships each battery has with its context.

Broader Impacts
This work will benefit all battery portable, transportation, and grid applications as well as multiple sectors. It will include the emerging battery repurposing sector, by providing tangible methods to improve testing, estimation and management techniques. The result will be longer battery life, better performance, and less environmental waste. Educational impacts include active learning opportunities for undergraduate and graduate students via research and educational interactions with individualized testing boards linked to the newly created large multi-scale testing platform. This strategy will enable low cost, highly distributed testing environments. The Pl will disseminate tools via national education conferences to improve the nearly nonexistent battery testing training of students. This project will facilitate new paths in multi-disciplinary graduate courses. The Pl has a passion to increase representation of Hispanic females in STEM. Outreach will include hosting 4 diverse Community College students for summer research through the Michigan College and University Partnership, and participating in Society for Hispanic Professional Engineers conferences, specifically in the female Hispanic track.

Investigator: Lucia Gauchia

“CRISP Type 2: Revolution through Evolution: A Controls Approach to Improve How Society Interacts with Electricity.”

National Science Foundation

This CRISP project addresses the challenges associated with the rapid evolution of the electricity grid to a highly distributed infrastructure. The keystone of this research is the transformation of power distribution feeders, from relatively passive channels for delivering electricity to customers, to distribution microgrids, entities that actively manage local production, storage and use of electricity, with participation from individual customers. Distribution microgrids combine the advantages of the traditional electricity grid with the advantages of emerging distributed technologies, including the ability to produce and use power locally in the event of grid outages. The project will result in a unified model that incorporates key aspects of power generation and delivery, information flow, market design and human behavior. The model predictions can be used by policymakers to guide a transition to clean energy via distribution microgrids. The expectation is to enable at least 50% of electric power to come from renewable resources. This cannot be done with either the traditional grid, due to its limited capacity to accommodate intermittent renewable power sources, or with fully decentralized approaches, which would not be affordable for most utility customers.

This project addresses many socio-technological gaps necessary to translate from research discovery to commercial applications. To date, there is no theoretical framework to ensure system stability as renewable energy routed through power electronics replaces traditional rotating machinery. To achieve an optimal mix of storage performance and information bandwidth and to design nonlinear controllers, we will use Hamiltonian Surface Shaping Power Flow Control theory. We will study methods to detect malicious tampering with information flows. The complex interaction of intermittent resources, human behavior and market structures will be modeled in an agent-based simulation. System inputs will be provided by utility and meteorological data, and by behavioral models that incorporate information obtained by surveys, interviews and metering data. Emergent system dynamics will be abstracted and studied using dynamical complex network theory, to explore stability limits as a function of human behavior and market design. Finally, the effect of enhanced controllability of distribution systems on the robustness of large energy-information-social networks will be analyzed using interdependent Markov-chain models. Graduate students involved in this program will be exposed to a unique combination of skills from engineering, data analysis and social sciences; such cross-disciplinary training will prepare them for leadership roles in the emerging energy economy of tomorrow.

Investigators: Laura Brown, Chee-Wooi Ten, Wayne Weaver

Revolution through Evolution: A Controls Approach to Improve How Society Interacts with Electricity

September 20, 2015

Laura Brown (PI) received a $699,796 NSF grant. The title of the project is “CRISP Type 2: Revolution through Evolution: A Controls Approach to Improve How Society Interacts with Electricity.” The co-principal investigators of this project are Chee-Wooi Ten (ECE) and Wayne Weaver (ECE). This is a three-year collaborative project with four other institutions with a total budget of $2,499,801. The project addresses the rapid evolution of the electricity grid, from one based on few centralized generators providing power to millions of users to one where many distributed energy resources. The keystone of this research is the transformation of power distribution feeders, from relatively passive channels that deliver electricity from the transmission grid to customers, to distribution microgrids, highly intelligent entities that actively manage production, storage and use of electricity.

 

 

Distributed Agent-Based Management of Agile Microgrids

US Department of Defense, Army Research Laboratory

This project plan (APP) describes the third year of the four year program for distributed agent-based management of agile microgrids. In year 1, the team has evaluated modeling and forecasting techniques for renewable energy sources as well as developed relevant case studies. In year 2 the further developed the models and forecasting techniques as well as begin implementation of simulations and hardware test cases.

The existing simulation models a user-definable, network ofmicrogrids and the Autonomous Agile Microgrid (AAM) control system. The AAM has three main components – (1) a low-level, asset control system (Decentralized Closed Loop Controller agent- DCLC), (2) a mid-level, optimal, grid state-change solver (Decentralized Model Based Control agent – DMBC) and the highest level reasoning layer, (Distributed Grid Management agent- DGM).
The entire system is “driven” by a user-configurable, time-history of prioritized loads and events based on field data.

The focus of the year three plan is to (1) increase the reasoning capability of the DGM, (2) develop an optimal power flow strategy at the DMBC level and (3) design a human-in-the-loop interface that permits real-time interaction with the simulation.

Deliverable 1. The AAM uses a command line approach to execute the simulation and observe the grid’s evolution based on a pre-defined time history scenario of events and loads. While the process for designing rich scenarios has a well-defined workflow, the system currently lacks the ability to respond to real-time inputs from a user. The deliverable is demonstration of a new human-in-the-loop capability for the AAM simulator. It will permit one user to “actuate” the power grid manually, or in an AAM-assist mode where the user can optionally decide to implement the AAM’s recommended actions. A second user will be able to trigger events and load changes in real-time, including policy and scenario changes in the DGM. The intent for year three is to create an environment for more complete testing of the AAM and demonstrate its capabilities. This feature would then be available for future studies to increase the reasoning functions of the DGM using human-in-the-loop training.

Deliverable 2. The DMBC currently computes an optimal solution to transition the power grid from its current state to a new state as requested by the DGM. These requests are based on load, generation and storage forecast agent calculations. The DMBC also triggers a new solution based on high-tempo changes to the bus voltage, independent of the DGM, due to unforecasted changes in loads or generation. All DMBC solutions are based on the assumption of a fully functioning, well-defined set of loads, generation, and storage assets. The DMBC does not compute optimal redirection of power flow based on catastrophic generation or load failures. The deliverable is the development and demonstration of a scheme to optimally redistribute power flow for contingency and catastrophic events including equipment faults and attack damage. This redistribution strategy control may be at the level of the DMBC, DCLC or both. The human-in-the-loop capability, described in Deliverable I, will be used to demonstrate this new feature by instantaneously removing generation assets and loads.

Deliverable 3. The DGM relies on data-driven load and generation forecasts to compute grid state change requests for the DMBC. The forecasts will be improved with the inclusion of additional knowledge of inventory, asset models, and situational information. While load prioritization is accommodated, there is not functionality for addressing situations where there is not enough power to accommodate all of the highest priority loads. Policies and negotiation protocols for the DGM multi-agent system that enable power sharing among microgrids will be explored. Additional policies for fine control of load shedding will be examined and simulated. The ERDC-CERL VFOB project can potentially provide a rich source of data and models to the DGM design that support more elaborate forecasts and reasoning under conflict. In addition, the data can drive methods for scenario classification (prediction of the current and future events of the base, e.g., patrols, heightened alerts, etc.). The deliverable is a report and demonstration of improved forecasting agents and conflict resolution handling, through the power scheduling agent, based on knowledge-based reasoning mechanisms and statistical risk analysis metrics. The report will document the agent models and reasoning strategies along with a description of opportunities and gaps for implementing a fully autonomous, resilient power grid.

Investigators: Gordon Parker, Laura Brown, Wayne Weaver, Steven Goldsmith

SGAS Drive Train Model Calibration

IMECO

Introduction
Calibration is an important step in creating a physical model that can be used for predictive control system design. IMECO has a MATLAB/Simulink model of their Steering Gear Actuation System (SGAS). It contains parameters that can be classified as known (e.g. control system gains), known with uncertainty (e.g. mass properties) and unknown (e.g. damping coefficients). IMECO has also obtained experimental data that can be used to run the model and compare model outputs to sensor measurements. An optimization-based method for identifying the model parameters is needed to help automate the calibration process.

Statement of Work
Using the model and experimental data supplied by IMECO, calibrate the model using advanced numerical optimization strategies. Separate calibration parameters for several data sets will be developed in addition to a single calibration across multiple data sets. While the calibration is of primary importance, development of a methodology for automating the process will also be developed.

Investigators: Gordon Parker, Ed Trinklein

Secure Intelligent Architectures for Coordinating Agile Microgrids Research

Overview

Agile microgrids allow variable, distributed sources and loads to effectively interoperate over a broad range of conditions. Enabling large numbers of autonomously-managed micro-generators and loads must be accomplished through information-intensive architectures that create significant challenges regarding coordination and cyber security.

Active Research Projects

Applications

Research is concerned with developing concepts, techniques, and tools for enabling the design of secure and effective multi-agent systems for agile microgrids:

  • Combine cyber security, secure software, and system design, distributed control, and computational modeling to achieve a resilient and reliable control system design for agile microgrids.
  • Design and implement multi-agent system incorporating advanced distributed controls, intrinsic cyber security and safety.
  • Develop simulation-based microgrid design tools that utilize advanced in secure multi-agent distributed control to assist in microgrid development projects involving variable sources and controlled loads

SecureIntelligentArchitecture