Gaussian Process Regression for In-situ Capacity Estimation of Lithium-ion Batteries

Accurate on-board capacity estimation is of critical importance in lithium-ion battery applications. Battery charging/discharging often occurs under a constant current load, and hence voltage vs. time measurements under this condition may be accessible in practice. This paper presents a data-driven diagnostic technique, Gaussian Process regression for In-situ Capacity Estimation (GP-ICE), which estimates battery capacity using voltage measurements over short periods of galvanostatic operation. Unlike previous works, GP-ICE does not rely on interpreting the voltage-time data as Incremental Capacity (IC) or Differential Voltage (DV) curves. This overcomes the need to differentiate the voltage-time data (a process which amplifies measurement noise), and the requirement that the range of voltage measurements encompasses the peaks in the IC/DV curves. GP-ICE is applied to two datasets, consisting of 8 and 20 cells respectively. In each case, within certain voltage ranges, as little as 10 seconds of galvanostatic operation enables capacity estimates with approximately 2-3% RMSE.Comment: 12 pages, 10 figures, submitted to IEEE Transactions on Industrial Informatic

Modular converter system for low-cost off-grid energy storage using second life Li-ion batteries

Lithium ion batteries are promising for small off- grid energy storage applications in developing countries because of their high energy density and long life. However, costs are prohibitive. Instead, we consider 'used' Li-ion batteries for this application, finding experimentally that many discarded laptop cells, for example, still have good capacity and cycle life. In order to make safe and optimal use of such cells, we present a modular power management system using a separate power converter for every cell. This novel approach allows individual batteries to be used to their full capacity. The power converters operate in voltage droop control mode to provide easy charge balancing and implement a battery management system to estimate the capacity of each cell, as we demonstrate experimentally.Comment: Presented at IEEE GHTC Oct 10-14, 2014, Silicon Valle

Cold and trapped metastable noble gases

We review experimental and theoretical work on cold, trapped metastable noble gases. We em- phasize the aspects which distinguish work with these atoms from the large body of work on cold, trapped atoms in general. These aspects include detection techniques and collision processes unique to metastable atoms. We describe several experiments exploiting these unique features in fields including atom optics and statistical physics. We also discuss precision measurements on these atoms including fine structure splittings, isotope shifts, and atomic lifetimes

Degradation Diagnostics for Commercial Lithium-Ion Cells Tested at − 10°C

Degradation of lithium ion (Li-ion) cells affects both performance and safety of Li-ion batteries. In order to avoid potential safety hazards, it is crucial to detect the onset and extent of critical degradation modes in commercial Li-ion cells. This work demonstrates the application of a diagnostic algorithm to identify and quantify degradation modes of commercial Li-ion pouch cells cycled at − 10°C and a C-rate of 2 C. Rapid loss of active negative electrode material was successfully identified and results were validated using 3-electrode cells, scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX). The positive electrode material was less strongly affected by the tests, as found by the diagnostic algorithm and confirmed with EDX and SEM results

Degradation diagnostics for lithium ion cells

Degradation in lithium ion (Li-ion) battery cells is the result of a complex interplay of a host of different physical and chemical mechanisms. The measurable, physical effects of these degradation mechanisms on the cell can be summarised in terms of three degradation modes, namely loss of lithium inventory, loss of active positive electrode material and loss of active negative electrode material. The different degradation modes are assumed to have unique and measurable effects on the open circuit voltage (OCV) of Li-ion cells and electrodes. The presumptive nature and extent of these effects has so far been based on logical arguments rather than experimental proof. This work presents, for the first time, experimental evidence supporting the widely reported degradation modes by means of tests conducted on coin cells, engineered to include different, known amounts of lithium inventory and active electrode material. Moreover, the general theory behind the effects of degradation modes on the OCV of cells and electrodes is refined and a diagnostic algorithm is devised, which allows the identification and quantification of the nature and extent of each degradation mode in Li-ion cells at any point in their service lives, by fitting the cells’ OC