Reliability of lithium-ion (Li-ion) rechargeable batteries has been recognized as of high importance from a broad range of stakeholders, including manufacturers of battery-powered devices, regulatory agencies, researchers and the general public. Failures of Li-ion batteries could result in enormous economic losses and catastrophic events. To enable early identification and resolution of reliability issues and proactive prevention of failures, it is important to be able to diagnose, in a quantitative manner, degradation mechanisms of individual battery cells while the cells are in operation. This paper proposes a methodological framework for on-board quantitative analysis of degradation mechanisms of Li-ion battery using differential voltage analysis. In the framework, the task of on-board degradation analysis is decomposed into two phases: 1) offline high precision characterization of half-cell differential voltage (dV/dQ) behavior, which collects high precision voltage (V) and capacity (Q) data from positive and negative electrode half-cells; and 2) online (on-board) quantitative analysis of degradation mechanisms, which adopts recursive Bayesian filtering to online estimate degradation parameters based on measurement of full-cell dV/dQ curve. These degradation parameters quantify the degrees of degradation from the mechanisms. Simulation results obtained from LiCoO2/graphite Li-ion cells verify the effectiveness of the proposed framework in online estimation of the degradation parameters.
Distinct protein degradation mechanisms mediated by Cul1 and Cul3 controlling Ci stability in Drosophila eye development
