Electrochemical energy storage in the form of large arrays of small to medium sized battery cells has the potential to improve U.S. energy independence, efficiency, and security by enhancing the capabilities of the electrical grid and increasing the viability and acceptance of widespread electric transportation. With the ongoing decentralization of the electrical grid and the growing penetration of low-carbon, but intermittent, renewable energy sources, electrochemical storage may play a critical role in maintaining grid stability while helping to manage energy flow between generation and end-load. However, modern battery systems are complex with a wide range electrochemical processes that underlie the simple metrics of cell voltage and current. Limitations of current battery management systems (BMS) result in system overdesign and operation well below maximum energy and power capabilities to minimize risk of catastrophic failure and meet operational targets. There is a clear need for transformational innovation in energy storage management technology, especially at the intersection of distributed power electronics architectures, control systems, and diagnostics. This project will support a collaborative effort that spans these areas of critical importance, while also supporting a range of broader impact activities through engagement in undergraduate teaching, K-12 students, and the general public.
In the collaborative project, researchers at Dartmouth will develop a new class of highly-integrated power electronics, used to manage individual cells in a large array, which will benefit from low-cost semiconductor batch fabrication, Moore's law scaling, and unprecedented performance in terms of efficiency versus power density. The Dartmouth team will also develop a multi-objective control system, implemented on top of the power electronics platform, to provide real-time diagnostics based on electrochemical impedance spectroscopy (EIS). Researchers at Princeton will support construction of the platform at the embedded systems and software level and will develop the diagnostic toolset used to measure the state-of-charge (SOC), state-of-health (SOH), and pending failure modes of individual cells in real-time. To characterize the system and study EIS failure mode signatures, the Princeton team will design a series of batteries with known characteristics and flaws. This will open new dimensions in state-of-health diagnosis and provide the ability to fingerprint important physical phenomena across multiple time-constant regimes. By providing a realistic roadmap to cost-effective, highly-granular management and diagnostics, the collaboration has the potential to improve safety, performance, and cycle life while supporting reductions in pack overbuild and overall cost.
