Cost functions for degradation control of electric motors in electric vehicles

This paper introduces a novel set of electric motor degradation cost functions based on energy usage, energy loss and work output, against their continuous operation rated values recommended by the manufacturer. Unlike conventional electric motor degradation indicators such as the bearing life and insulation life based service factors, these cost functions account for the quantified time in the degradation process. The cost functions are evaluated throughout the operational life of the motor using real-time measurements. Hence, they give a very accurate indication, which may be adapted for online controller tuning. This solid establishment of a degradation cost function also enables the system designer to give the user a choice between performance and degradation minimization. The proposed cost function scheme has experimentally been verified using a hardware-in-the-loop electric powertrain test-rig where standard drive cycles are used to conduct the experiments. The experimental results reveal that the degradation cost functions Cumulative Input Energy Ratio (CIER), Cumulative Loss Ratio (CLR) and Cumulative Work Ratio (CWR) accurately represent the electric motor degradation both qualitatively and quantitatively

Turbidometric evaluation of polyene-azole antagonism in C. albicans

published_or_final_versio

Oral bacteria modulate Candida biofilm formation

published_or_final_versio

Stochastic on-time arrival problem in transit networks

This article considers the stochastic on-time arrival problem in transit networks where both the travel time and the waiting time for transit services are stochastic. A specific challenge of this problem is the combinatorial solution space due to the unknown ordering of transit line arrivals. We propose a network structure appropriate to the online decision-making of a passenger, including boarding, waiting and transferring. In this framework, we design a dynamic programming algorithm that is pseudo-polynomial in the number of transit stations and travel time budget, and exponential in the number of transit lines at a station, which is a small number in practice. To reduce the search space, we propose a definition of transit line dominance, and techniques to identify dominance, which decrease the computation time by up to 90% in numerical experiments. Extensive numerical experiments are conducted on both a synthetic network and the Chicago transit network.Comment: 29 pages; 12 figures. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0

Impacts of Covid-19 mode shift on road traffic

This article is driven by the following question: as the communities reopen after the COVID-19 pandemic, will changing transportation mode share lead to worse traffic than before? This question could be critical especially if many people rush to single occupancy vehicles. To this end, we estimate how congestion will increases as the number of cars increase on the road, and identify the most sensitive cites to drop in transit usage. Travel time and mode share data from the American Community Survey of the US Census Bureau, for metro areas across the US. A BPR model is used to relate average travel times to the estimated number of commuters traveling by car. We then evaluate increased vehicle volumes on the road if different portions of transit and car pool users switch to single-occupancy vehicles, and report the resulting travel time from the BPR model. The scenarios predict that cities with large transit ridership are at risk for extreme traffic unless transit systems can resume safe, high throughput operations quickly.Comment: 14 pages, 11 figure