Browsing by Subject "Batteries"

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  • Suzuki, Kosuke; Suzuki, Shunta; Otsuka, Yuji; Tsuji, Naruki; Jalkanen, Kirsi; Koskinen, Jari; Hoshi, Kazushi; Honkanen, Ari-Pekka; Hafiz, Hasnain; SakuraI, Yoshiharu; Kanninen, Mika; Huotari, Simo; Bansil, Arun; Sakurai, Hiroshi; Barbiellini, Bernardo (2021)
    Compton scattering imaging using high-energy synchrotron x rays allows the visualization of the spatiotemporal lithiation state in lithium-ion batteries probed in operando. Here, we apply this imaging technique to the commercial 18650-type cylindrical lithium-ion battery. Our analysis of the line shapes of the Compton scattering spectra taken from different electrode layers reveals the emergence of inhomogeneous lithiation patterns during the charge-discharge cycles. Moreover, these patterns exhibit oscillations in time where the dominant period corresponds to the timescale of the charging curve.
  • Li, Tong; Zhang, Mingyang; Li, Yong; Lagerspetz, Eemil; Tarkoma, Sasu; Hui, Pan (2021)
    The outbreak of Covid-19 changed the world as well as human behavior. In this article, we study the impact of Covid-19 on smartphone usage. We gather smartphone usage records from a global data collection platform called Carat, including the usage of mobile users in North America from November 2019 to April 2020. We then conduct the first study on the differences in smartphone usage across the outbreak of Covid-19. We discover that Covid-19 leads to a decrease in users' smartphone engagement and network switches, but an increase in WiFi usage. Also, its outbreak causes new typical diurnal patterns of both memory usage and WiFi usage. Additionally, we investigate the correlations between smartphone usage and daily confirmed cases of Covid-19. The results reveal that memory usage, WiFi usage, and network switches of smartphones have significant correlations, whose absolute values of Pearson coefficients are greater than 0.8. Moreover, smartphone usage behavior has the strongest correlation with the Covid-19 cases occurring after it, which exhibits the potential of inferring outbreak status. By conducting extensive experiments, we demonstrate that for the inference of outbreak stages, both Macro-F1 and Micro-F1 can achieve over 0.8. Our findings explore the values of smartphone usage data for fighting against the epidemic.