Lithium-ion batteries (LIBs) are leading the energy storage market. Significant efforts are being made to widely adopt LIBs due to their inherent performance benefits and reduced environmental impact for transportation electrification. However, achieving this widespread adoption still requires overcoming critical technological constraints impacting battery aging and safety. Battery aging, an inevitable consequence of battery function, might lead to premature performance losses and exacerbated safety concerns if effective thermo-electrical battery management strategies are not implemented. Battery aging effects must be better understood and mitigated, leveraging the predictive power of aging modelling methods. This review paper presents a comprehensive overview of the most recent aging modelling methods. Furthermore, a multiscale approach is adopted, reviewing these methods at the particle, cell, and battery pack scales, along with corresponding opportunities for future research in LIB aging modelling across these scales. Battery testing strategies are also reviewed to illustrate how current numerical aging models are validated, thereby providing a holistic aging modelling strategy. Finally, this paper proposes a combined multiphysics- and data-based modelling framework to achieve accurate and computationally efficient LIB aging simulations.

Hospital acquired infections (HAIs) occur frequently in hospitalized patients. Staff compliance with hand hygiene (HH) policy during patient care has been shown to reduce HAIs. Currently, hospitals evaluate adherence to HH policies through direct observation by human auditors. The auditors do not have authority over staff members; thus, this process is more akin to sousveillance (watching from below) than surveillance (watching from above). When behaviour change occurs due to awareness of being observed, it is referred to as the “Hawthorne effect”. We quantified the effect of sousveillance by comparing the frequency of HH events with an auditor present to when no auditor was present. The data analysed in the present work is from an ongoing study on hand hygiene compliance monitoring. A monitoring network recorded 290,000 hand hygiene events over 6 months; auditors were present on five occasions for about an hour each visit. When using an exponential underlying distribution we found that the change in the HH event rate was significant (p <; 0.01) in 4 of the 5 auditor visits. Finally, with a hyper-exponential underlying distribution, 5 of 5 were significant (p <; 0.01). There was no significant change in the HH event rate among dispensers located within patient rooms (not visible to the auditor), irrespective of auditor's presence.