Zobrazeno 1 - 10
of 1 529
pro vyhledávání: '"Siegel, P B"'
Autor:
Wong, Clement, Weng, Andrew, Pannala, Sravan, Choi, Jeesoon, Siegel, Jason B., Stefanopoulou, Anna
Diagnosing imbalances in capacity and resistance within parallel-connected cells in battery packs is critical for battery management and fault detection, but it is challenging given that individual currents flowing into each cell are often unmeasured
Externí odkaz:
http://arxiv.org/abs/2405.17754
The battery state of health (SOH) based on capacity fade and resistance increase is not sufficient for predicting Remaining Useful life (RUL). The electrochemical community blames the path-dependency of the battery degradation mechanisms for our inab
Externí odkaz:
http://arxiv.org/abs/2405.12028
Current imbalance in dissimilar parallel-connected batteries and the fate of degradation convergence
This paper proposes an analytical framework describing how initial capacity and resistance variability in parallel-connected battery cells may inflict additional variability or reduce variability while the cells age. We derive closed-form equations f
Externí odkaz:
http://arxiv.org/abs/2310.10396
This work proposes a semi-empirical model for the SEI growth process during the early stages of lithium-ion battery formation cycling and aging. By combining a full-cell model which tracks half-cell equilibrium potentials, a zero-dimensional model of
Externí odkaz:
http://arxiv.org/abs/2305.18722
Voltage-based battery metrics are ubiquitous and essential in battery manufacturing diagnostics. They enable electrochemical "fingerprinting" of batteries at the end of the manufacturing line and are naturally scalable, since voltage data is already
Externí odkaz:
http://arxiv.org/abs/2303.07088
In this work, we derive analytical expressions governing state-of-charge and current imbalance dynamics for two parallel-connected batteries. The model, based on equivalent circuits and an affine open circuit voltage relation, describes the evolution
Externí odkaz:
http://arxiv.org/abs/2211.04961
A data-driven model augmentation framework, referred to as Weakly-coupled Integrated Inference and Machine Learning (IIML), is presented to improve the predictive accuracy of physical models. In contrast to parameter calibration, this work seeks corr
Externí odkaz:
http://arxiv.org/abs/2207.10819
Autor:
Drallmeier, Joseph A., Wong, Clement, Solbrig, Charles E., Siegel, Jason B., Stefanopoulou, Anna G.
With the increased pervasiveness of Lithium-ion batteries, there is growing concern for the amount of retired batteries that will be entering the waste stream. Although these batteries no longer meet the demands of their first application, many still
Externí odkaz:
http://arxiv.org/abs/2203.12376
Fast charging of lithium-ion batteries is crucial to increase desirability for consumers and hence accelerate the adoption of electric vehicles. A major barrier to shorter charge times is the accelerated aging of the battery at higher charging rates,
Externí odkaz:
http://arxiv.org/abs/2108.07833
Internal short circuits are a leading cause of battery thermal runaway, and hence a major safety issue for electric vehicles. An internal short circuit with low resistance is called a hard internal short, which causes a high internal current flow tha
Externí odkaz:
http://arxiv.org/abs/2010.13519