Critical Speeds of Electric Vehicles for Regenerative Braking

Efficient regenerative braking of electric vehicles(EVs)can enhance the efficiency of an energy storage system(ESS)and reduce the system cost.To ensure swift braking energy recovery,it is paramount to know the upper limit of the regenerative energy during braking.Therefore,this paper,based on 14 typical urban driving cycles,proposes the concept and principle of confidence interval of“probability event”and“likelihood energy”proportion of braking.The critical speeds of EVs for braking energy recovery are defined and studied through case studies.First,high-probability critical braking speed and high-energy critical braking speed are obtained,compared,and analyzed,according to statistical analysis and calculations of the braking randomness and likelihood energy in the urban driving cycles of EVs.Subsequently,a new optimized ESS concept is proposed under the frame of a battery/ultra-capacitor(UC)hybrid energy storage system(HESS)combined with two critical speeds.The battery/UC HESS with 9 UCs can achieve better regenerative braking performances and discharging performances,which indicates that a minimal amount of UCs can be used as auxiliary power source to optimize the ESS.After that,the efficiency regenerative braking model,including the longitudinal dynamics,motor,drivetrain,tire,and wheel slip models,is established.Finally,parameters optimization and performance verification of the optimized HESS are implemented and analyzed using a specific EV.Research results emphasize the significance of the critical speeds of EVs for regenerative braking.

Suspected Close Contacts as the Pilot Indicator of the Growth Trend of Confirmed Population During the COVID-19 Pandemic: A Simulation Approach

Regarding to the actual situation of the new coronavirus disease 2019 epidemic,social factors should be taken into account and the increasing growth trend of confirmed populations needs to be explained.A proper model needs to be established,not only to simulate the epidemic,but also to evaluate the future epidemic situation and find a pilot indicator for the outbreak.The original susceptible-infectious-recover model is modified into the susceptible-infectious-quarantine-confirm-recover combined with social factors(SIDCRL)model,which combines the natural transmission with social factors such as external interventions and isolation.The numerical simulation method is used to imitate the change curve of the cumulative number of the confirmed cases and the number of cured patients.Furthermore,we investigate the relationship between the suspected close contacts(SCC)and the final outcome of the growth trend of confirmed cases with a simulation approach.This article selects four representative countries,that is,China,South Korea,Italy,and the United States,and gives separate numerical simulations.The simulation results of the model fit the actual situation of the epidemic development and reasonable predictions are made.In addition,it is analyzed that the increasing number of SCC contributes to the epidemic outbreak and the prediction of the United States based on the population of the SCC highlights the importance of external intervention and active prevention measures.The simulation of the model verifies its reliability and stresses that observable variable SCC can be taken as a pilot indicator of the coronavirus disease 2019 pandemic.