RESEARCH BACKGROUND
Li-Ion batteries offer unique advantages that allow pushing the frontiers of energy storage essential for aerospace missions and are of significant importance due to green energy initiatives. Work performed under this position is associated with aerospace applications such as design of smart batteries for future astronaut space suits and safe and reliable batteries for unmanned electric aircraft. Health management algorithms are being developed and demonstrated in relevant flight environments. Densely packed with enormous amounts of energy these batteries can be dangerous due to various failure modes they often experience. The objective is to develop methods to understand battery failures and predict the remaining useful life of failing batteries. These batteries (or cell packs within) fail due to aging or accidental damages after which it becomes critical to determine how long they can sustain before they absolutely need replacement or may lead to catastrophic failures otherwise. Prognostics Center of Excellence (PCoE) at NASA Ames is involved in developing diagnostic and prognostic approaches for a variety of systems and Li-ion batteries being one of them. Following two positions will support these activities by adding testbeds and simulation models to already existing aging platform. The students are expected to work in a collaborative group environment.

POSITION - 1
Title: Research Intern – Battery Simulation Testbed
Organization: Prognostics Center of Excellence, Intelligent Systems Division
Location: NASA Ames Research Center, CA
Duration: 3-6 Mo.
http://prognostics.nasa.gov

TASK DESCRIPTION
Developing a hardware testbed to simulate a battery pack consisting of individual Li-Ion cells connected in a reconfigurable series and parallel combination will enable understanding the effects of various types of battery faults. The testbed will accommodate several cells and allow remotely configurable connections to simulate different types of packs with cells at different states-of-health. This will allow studying the effects of cell imbalance, weak cells, open connections, corrosion, etc. on the overall battery performance. The testbed will include instrumentation of individual cells and packs with temperature, current, and voltage measurements. Furthermore, provisions will be made for allowing electrochemical impedance spectroscopy (EIS) measurements using existing EIS equipment by providing access to individual cell terminals. The testbed will simulate battery packs that can be rated for high voltage (up to 50V) and current (~100A) levels, and therefore will require complying with proper safety standards. Such testbed will also include a suitable data acquisition system and interface with an existing database to log experimental data in a reliable and accessible manner. Help in designing the testbed will be available; however candidate’s experience in building electrical hardware testbed is extremely important in this project. Data collected from the testbed will be compared with a computer simulation model to test and validate the implementation.
This internship research position is scheduled for a period of 3-6 months and may be extended on need basis.

REQUIREMENTS
- Circuit design and implementation (compliant with safety standards)
- CAD (electronics design documentation and simulation)
- Industrial electronic control (relay logic or PLC)
- Experience with testbed integration
- Laboratory testing procedures
- Search and procurement of suitable testbed components
- Electrical wiring
- Instrumentation and data acquisition knowledge
- NI equipment integration
- Labview based interface (control, DAQ, UI) development
- Team player
- USA work Authorization preferred

Following skills are considered a plus
- Working knowledge of signal processing, filtering, and optimization techniques
- An understanding of battery health management principles
- Physics modeling skills and experience with modeling in Matlab/Simulink
- Strong verbal and written communication skills

EDUCATION & EXPERIENCE
Mechanical/Electrical engineering, Industrial Automation or similar at the graduate level.

POSITION - 2
Title: Research Intern – Battery Modeling & Simulation
Organization: Prognostics Center of Excellence, Intelligent Systems Division
Location: NASA Ames Research Center, CA
Duration: 3-6 Mo.
http://prognostics.nasa.gov

TASK DESCRIPTION
Modeling of Li-batteries as a combination of individual cells will enable simulation of various cell configurations, which will then allow using cell level models to simulate battery level effects. The overall battery model will allow injecting various faults both at cell and module levels. For real-time applications low computational complexity is a key requirement and therefore simpler lumped parameter (equivalent circuit) models are used at the cell level. Intrinsic faults develop within cells due to overstress from operating conditions or manufacturing defects. Degraded performance of faulty cell will affect battery performance, which would be simulated using aforementioned battery model. Further the effects of unbalanced cells and other mechanical failures will be modeled as well in the simulation. Lab experiments will be conducted to stress these batteries and collect realistic failure data. These data will be used to validate and improve these models. Once these models are fine tuned they will be used to run simulation experiments to develop battery level health management algorithms. A Matlab/Simulink based simulation environment is preferred for easy integration with the existing prognostic framework and algorithms. Specific tasks include battery modeling, battery fault modeling, analyze data and develop algorithms to detect failures and predict life.
This internship research position is scheduled for a period of 3-6 months and may be extended on need basis.

REQUIREMENTS
- An understanding of the principles of Li-ion battery operation
- Strong physics modeling skills and experience with modeling in Matlab/Simulink
- Strong verbal and written communication skills
- Team player
- USA work Authorization preferred
Following skills are considered a plus
- Working knowledge of signal processing, filtering, and optimization techniques
- Hands-on experience with lab work, DAQ, integration
- An understanding of battery health management principles

EDUCATION & EXPERIENCE
Physics, engineering, computer science, applied mathematics or similar at the graduate level.