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Summary: The battery pack of electric vehicles (EV) is generally composed of multiple cells in series. Due to the inconsistency between the cells in the production process and use stage, the capacity and state‐of‐charge (SOC) of the cells will be different. We propose an online estimation method based on the charging curve similarity principle in this paper. The proposed method uses a series of charging time differences (CTD) during the charge. By analyzing the CTD curve, the capacity and SOC difference can be achieved. The first‐order resistance circuit model is used for the series charging curve simulation. Further experimental verification is conducted using two groups of four cells in series. In simulations and experiments, the error of the proposed capacity estimation method and the initial SOC error are less than 1%. Finally, the robustness of the proposed method is verified using EV cloud data. The results demonstrate that the proposed method has good robustness at the level of EV cloud data. [ABSTRACT FROM AUTHOR]

Summary: For state‐of‐charge (SOC) estimation, the resistance deterioration and continuous capacity loss can lead to erroneous estimation results. In this paper, an SOC estimator of lithium‐ion battery based on the fractional‐order model and adaptive dual Kalman filtering algorithm is proposed first. Then, to improve the accuracy of SOC estimation considering capacity loss, the particle filter algorithm is applied to update capacity online in real time. Then, an SOC estimation method is proposed considering battery capacity loss. The simulation results show that the accuracy of battery capacity prediction based on particle filter is high under the condition of capacity loss. [ABSTRACT FROM AUTHOR]

Two apparent features that prevail at signalized intersections in China are green signal countdown device and long cycle lengths. The objective of this study is to investigate the impacts of green signal countdown device and long cycle length on queue discharge patterns and to discuss its implications on capacity estimation in the context of China's traffic. At five typical large intersections in Shanghai and Tianjin, 11 through lanes were observed, and 9251 saturation headways were obtained as valid samples. Statistical analyses indicate that the discharge process of queuing vehicles can be divided into three distinct stages according to the discharge flow rate: a start-up stage, a steady stage, and a rush stage. The average time for queuing vehicles to reach a stationary saturation flow rate, that is, the start-up stage, was found to be approximately 20-30 seconds; the rush stage usually occurs during the phase transition period. The finding is contrary to the conventional assumption that the discharge rate reaches a maximum value after the fourth vehicle is discharged and then remains constant during the green time until the queue is completely dissolved. The capacity estimation errors that might arise from the conventional methods are discussed through a comparative study and a sensitivity analysis that are based on the identified queue discharge patterns. In addition, a piecewise linear regression method was proposed in order to reduce such errors. The proposed method can be used for capacity estimation at signalized intersections with the identified queue discharge patterns. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Many road authorities work with static values for road capacities, while it has been proven that capacity is not a fixed quantity. At the same time, there is an increasing need for accurate stochastic input for traffic models, such as the variation in road capacity. In this paper, a methodological framework with a conceptual model for practical stochastic capacity estimation is presented, and a quantification of motorway capacity variation is given for the influence of day-type specific variations in capacity values. The results of the analysis show that there is a reduction in motorway breakdown capacity of 4% on weekend days in comparison with workdays. Furthermore, a capacity decrease of 8% was found for the discharge capacity in comparison with workdays. The analysis further shows that the breakdown capacity on holidays is not significantly lower than on workdays. Discharge capacity and capacity drops are also derived in each case. The results show that the capacity is significantly different depending on the type of day. A quantification of these differences is given in the form of a Weibull capacity estimation fit for each type-of-day scenario. Further consideration of the implications and applications of the framework is also given. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY This paper addresses some important issues related to airport apron capacity planning and management. An overview of existing apron models for supporting planning studies and for optimizing available resources utilization is given, with an emphasis on analytical models for apron capacity estimation. Constraints on apron usage, physical and operational with respect to different users, are discussed in detail, together with their impact on apron capacity. Simple extension of existing apron capacity estimation models is suggested accounting for constraints both on aircraft types and dominant users. Further on, instead of expressing apron capacity through a single number, an apron capacity envelope is used to illustrate capacity changes, that is, an apron's ability to accept various mixes of dominant users in demand. The apron capacity envelope provides information on capacity for one apron configuration (with respect to stand size and policy of usage) and a given fleet mix, for different shares of dominant users in demand. Finally, apron capacity flexibility is discussed with respect to its role in apron capacity planning and management. It is suggested how to express and interpret apron capacity flexibility. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]