A B S T R A C TBackgrounds: Up to February 16, 2020, 355 cases have been confirmed as having COVID-19 infection on the Diamond Princess cruise ship. It is of crucial importance to estimate the reproductive number (R0) of the novel virus in the early stage of outbreak and make a prediction of daily new cases on the ship. Method: We fitted the reported serial interval (mean and standard deviation) with a gamma distribution and applied "earlyR" package in R to estimate the R0 in the early stage of COVID-19 outbreak. We applied "projections" package in R to simulate the plausible cumulative epidemic trajectories and future daily incidence by fitting the data of existing daily incidence, a serial interval distribution, and the estimated R0 into a model based on the assumption that daily incidence obeys approximately Poisson distribution determined by daily infectiousness. Results: The Maximum-Likelihood (ML) value of R0 was 2.28 for COVID-19 outbreak at the early stage on the ship. The median with 95% confidence interval (CI) of R0 values was 2.28 (2.06-2.52) estimated by the bootstrap resampling method. The probable number of new cases for the next ten days would gradually increase, and the estimated cumulative cases would reach 1514 (1384-1656) at the tenth day in the future. However, if R0 value was reduced by 25% and 50%, the estimated total number of cumulative cases would be reduced to 1081 (981-1177) and 758 (697-817), respectively. Conclusion: The median with 95% CI of R0 of COVID-19 was about 2.28 (2.06-2.52) during the early stage experienced on the Diamond Princess cruise ship. The future daily incidence and probable outbreak size is largely dependent on the change of R0. Unless strict infection management and control are taken, our findings indicate the potential of COVID-19 to cause greater outbreak on the ship.

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Abstract:The battery internal temperature estimation is important for the thermal safety in applications, because the internal temperature is hard to measure directly. In this work, an online internal temperature estimation method based on a simplified thermal model using a Kalman filter is proposed. As an improvement, the influences of entropy change and overpotential on heat generation are analyzed quantitatively. The model parameters are identified through a current pulse test. The charge/discharge experiments under different current rates are carried out on the same battery to verify the estimation results. The internal and surface temperatures are measured with thermocouples for result validation and model construction. The accuracy of the estimated result is validated with a maximum estimation error of around 1 K.

The open-circuit voltage (OCV) of batteries is a crucial characteristic parameter that reflects many aspects of a battery's performance, such as capacity, state-of-charge (SOC) and state-of-health. OCV is most widely used to determine the SOC when the battery works in a charge-depleting state. However, the application of the OCV to SOC estimation can be difficult because of the need for a long rest time for full relaxation. In this study, based on the analysis on the curve shape of battery voltage relaxation, a new adaptive model for simulating the voltage relaxation process is developed to predict the final static OCV in a few minutes instead of via the traditional long-term rest method. Avoiding this disadvantage, the SOC can be deduced from the predicted OCV via the corresponding relationship obtained in a short amount of time. A working condition experiment is performed to validate the new methods and the results are very accurate.

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