Capacity Estimation Model Research Articles

Seismic capacity evaluation of fire-damaged cabinet facility in a nuclear power plant

This study is to evaluate the seismic capacity of the fire-damaged cabinet facility in a nuclear power plant (NPP). A prototype of an electrical cabinet is modeled using OpenSees for the numerical simulation. To capture the nonlinear behavior of the cabinet, the constitutive law of the material model under the fire environment is considered. The experimental record from the impact hammer test is extracted trough the frequency-domain decomposition (FDD) method, which is used to verify the effectiveness of the numerical model through modal assurance criteria (MAC). Assuming different temperatures, the nonlinear time history analysis is conducted using a set of fifty earthquakes and the seismic outputs are investigated by the fragility analysis. To get a threshold of intensity measure, the Monte Carlo Simulation (MCS) is adopted for uncertainty reduction purposes. Finally, a capacity estimation model has been proposed through the investigation, which will be helpful for the engineer or NPP operator to evaluate the fire-damaged cabinet strength under seismic excitation. This capacity model is presented in terms of the High Confidence of Low Probability of Failure (HCLPF) point. The results are validated by the proper judgment and can be used to analyze the influences of fire on the electrical cabinet.

Study on the Capacity Fading Effect of Low-Rate Charging on Lithium-Ion Batteries in Low-Temperature Environment

By taking a cylindrical LiFePO4 power battery as the research object, the cycle performance test was conducted under different charging current aging paths in a preset low-temperature environment and combined with EIS results to analyze the dynamic characteristics of the battery during the aging process, using the PDF (Probability Density Function) curve to analyze the change of battery energy storage characteristics, and analyze the aging mechanism of the power battery by analyzing the change in the lithium precipitation energy difference. The experimental results showed that under a low-temperature environment, the effect of increasing the charge rate is mainly reflected in slowing down the phase transformation reaction. From the analysis of lithium precipitation of the battery, it can be seen that the main mechanism of the aging of the battery is the loss of active lithium under the conditions of low-rate cycling at sub-zero temperature. The products from the side reaction between the lithium plating and the electrolyte build up on the SEI (Solid Electrolyte Interphase) film, which significantly increases the battery impedance late in the cycle. The work in this paper complements the mechanistic studies of lithium-ion batteries under different aging paths and is also useful for capacity estimation models and research on battery health.

Capacity Modeling of Permitted Left-Turn Signalized Intersections with Probabilistic Priority

Probabilistic yielding behavior is commonly observed at permitted left-turn signalized intersections in China. Nevertheless, its impact on traffic capacity has not been explored in existing research. The uniqueness of this traffic operation is that neither left-turn traffic nor through traffic holds an absolute priority. Based on queuing theory, this research developed an analytical capacity model that took into account the probabilistic priority phenomenon at permitted left-turn signals. To validate the developed capacity model, stochastic simulations with different combinations of left-turn traffic yielding rates and through-traffic flow rates were performed. It was found that modeling results from the analytical model precisely matched the stochastic simulation results. In comparison with traditional capacity estimation models that assumed through traffic holds an absolute priority, this research revealed that when through-traffic flow rate is around 1000 vehicles per hour (vph), the capacity of left-turn traffic increased from 233 vph to 536 vph when left-turn traffic yielding rates decrease from 1 to 0.2; whereas when left-turn traffic yielding rates decreased from 0.2 to 0, left-turn capacity increased sharply to 1200 vehicles per hour (vph). In this regard, it is critical to take into account the probabilistic left-turn yield behavior when developing permitted left-turn signal warrants.

An Improved Recursive Total Least Squares Estimation of Capacity for Electric Vehicle Lithium-Iron Phosphate Batteries

A battery’s capacity is an important indicator of its state of health and determines the maximum cruising range of electric vehicles. It is also a crucial piece of information for helping improve state of charge (SOC) estimation, health prognosis, and other related tasks in the battery management system (BMS). In this paper, we propose an improved recursive total least squares approach to online capacity estimation, which is based on the constrained Rayleigh quotient in terms of battery capacity. This approach accounts for errors in both the SOC and accumulated current measurements not traditionally considered in the battery capacity model to give an unbiased estimation. Moreover, the forgetting factor, updated by minimizing the Rayleigh quotient of the capacity estimation model, is applied to track the changes in the model and get a more precise estimation of the capacity. Finally, the performance of the proposed algorithm is validated via simulation and experimental studies on lithium-iron phosphate batteries. The estimation results show that the proposed algorithm improves capacity estimation accuracy.

Modification of SIDRA Model for Capacity Estimation of Urban Roundabouts under Mixed Traffic Condition in Sri Lanka

Modification of SIDRA Model for Capacity Estimation of Urban Roundabouts under Mixed Traffic Condition in Sri Lanka

Research on online estimation of available capacity of lithium batteries based on daily charging data

The available capacity of a lithium battery reflects its actual capacity under certain constraints. It serves as an important deciding factor for the electric vehicles’ energy management system. Online estimation allows the construction of a mathematical model with easily measurable variables as input to estimate the main variables that are difficult to measure in a complex environment. Current research applies the trained model of another battery to the current testing battery. Due to differences across batteries, the same model is unable to deliver an accurate description of the chemical processes of the same type of batteries. In order to solve the problem of model adaptability, an iterative EKF-GPR method is proposed based on the full charging curve of the battery being estimated and the daily charging voltage, current and time data based on the assumption that there are no abrupt changes in the lithium battery charging curves. The method is to establish a model for the estimation of the available current charging capacity of the lithium battery. It converts macro-time machine learning into the prediction and estimation of single charging curve on the micro-time scale, thus guaranteeing the accuracy of available capacity estimation models for any batch of batteries.

A Quick Method for Estimation of Level of Service at Manually Operated Tollbooths under Mixed Traffic Conditions

The present study attempted to develop a methodology to quickly estimate the level of service (LOS) at manually operated tollbooths where approaching traffic volume is mixed in nature in the same lane. Videography data collected at five toll plazas located in northern and western part of country. The vehicle approaching at toll plaza were divided into seven categories and headway data extracted from the video. The time headway is measured by taking the time difference of two consecutive vehicles at the same tollbooth. Field observation shows that there is a wide variation observed in headway for different vehicle type as well as within same vehicle type as leader and follower vehicle combinations. This may be due to varying toll rate, vehicle characteristics, drivers and tollbooth operators’ personal attributes. The present study developed tollbooth capacity estimation model by taking harmonic mean of traffic composition present at tollbooth with respect to the capacity achieved of respective vehicle class using headway method. Using approach volume and capacity estimated with the help of developed model in the volume to capacity (V/C) ratio is calculated and LOS thresholds are defined using clustering technique. The field observed V/C ratio ranges from 0.28 to 1.30. The study proposes six LOS ranges from LOS ‘A’ to ‘F’ at toll plaza based on V/C ratio. A threshold V/C ratio of less than 0.50 has been defined for LOS ‘A’ whereas more than 1.05 represents LOS ‘F’. The study outcome will be useful for toll plaza management and field engineers to quickly evaluate LOS at toll plaza with less efforts and cost.

Capacity of Urban Arterial Weaving Sections under Lane Signal Control Strategy

An urban arterial weaving section has been considered a bottleneck in urban roadway systems, which is one of the key contributing factors to the occurrence of the capacity-drop phenomenon. This paper proposes a novel lane signal control method to regulate the weaving traffic flow at urban arterial weaving sections. With consideration of the operational characteristics of the weaving section and the imbalance of traffic at each entrance lane, a reduction coefficient of the weaving traffic was adopted. A capacity calculation model for weaving traffic under lane signal control was developed. The optimization model was established to derive the optimal lane signal control scheme. The objective function was to maximize the weaving section’s capacity, and the constraints were the safe time headway between two adjacent phases. The proposed capacity estimation model was validated using field data collected at a representative urban arterial weaving section in Chongqing City, China; it was found that the mean absolute percent error was 4.7%. Modeling results demonstrate that the actual capacity of the weaving section significantly increased after adopting the lane signal control strategy. During the morning peak period, the actual capacity was 3,265 pcph (passenger cars per hour), which improves by 28.14% under the optimal scheme of two-phase lane signal control. The weaving capacity improvement for the three-phase lane single control is found to be 3,458 pcph, which improves by 35.71% under the three-phase control strategy. This paper used capacity reduction as index to test the robust of entrance lane, facing the increasing demand for traffic.

Modelling the region‐based VoLTE cell capacity estimation using resource optimisation

In this study, the authors introduce analytical models for region-based Voice over Long Term Evolution (VoLTE) cell capacity estimation. Using the authors' proposed analytical models, they estimate cell capacity in different regions by using several user-specific voice traffic characteristics such as Voice Activity Factor (VAF) and Silent Insertion Descriptor (SID), and different advanced techniques like Packet bundling and Transmission Time Interval (TTI) bundling. It also incorporates Hybrid automatic repeat request (HARQ) retransmission. In this work, the cell is divided into multiple regions, each corresponding to the area in which user equipment generates a certain level of Channel Quality Indication (CQI) value based on Signal to Interference plus Noise Ratio (SINR). This proposed model for region-based VoLTE capacity estimation optimises the network resource utilisation and enhances the number of concurrent active VoLTE users within the cell in each region. In this work, they have defined packet bundling and TTI bundling factors for appropriate regions to optimally allocate the radio resources for capacity estimation. Numerical results obtained using their proposed model considering different radio specific parameters are found improving the VoLTE cell capacity significantly. Further, the impact of HARQ on region-based VoLTE cell capacity is also obtained.

A novel capacity estimation method for lithium-ion batteries using fusion estimation of charging curve sections and discrete Arrhenius aging model

Practical open-loop capacity estimation models, such as the Arrhenius aging model, require parameter updating for the real-world capacity estimation in order to guarantee the estimation accuracy. In this paper, a novel capacity estimation method for lithium-ion batteries, based on the fusion estimation of charging curve sections and the discrete Arrhenius aging model using sequential extended Kalman filters, is proposed. The estimation method based on fractional charging curves is developed to estimate the battery capacity during vehicle charging, and the estimation results serve as the feedback using the first Kalman filter to update the model parameters of the discrete Arrhenius aging model. Then, the second Kalman filter makes a fusion capacity estimation based on the results of charging curve sections and the discrete Arrhenius aging model with the modified parameters. The results of the cycle life tests show that the proposed algorithm can modify the parameters of the discrete Arrhenius aging model online. And the fusion capacity estimation error is less than 1% when the model parameters reach a steady state.

Evaluation of Resource and Environmental Carrying Capacity of China’s Rapid-Urbanization Areas—A Case Study of Xinbei District, Changzhou

The evaluation of resource and environmental carrying capacity (RECC) is the foundation for the rationale behind the arrangement of land spaces for production, living, and ecological uses. In this study, based on various natural, economic, and social factors, an integrated Multi-Factor assessment model was developed to evaluate the RECC of Xinbei district of Changzhou. Meanwhile, we also calculated the population carrying capacity estimation model restricted by food security. The study comprehensively analyzed the current status and land resource characteristics of a rapid urbanization area and the RECC restrictions for protection and development. The results indicate that the comprehensive carrying capacity of Xinbei showed distinct spatial heterogeneity, with a decreasing trend from the riverside protection area to urban areas, then to mountain areas. Combined with the secure food supply provided by future land resources, it was estimated that the population carrying index of Xinbei would be as high as 1.25 and 1.22 in 2035 and 2050, respectively, indicating that both years would experience a population overload. Therefore, an urgent adjustment to the structure and layout of territorial space and resources of the Xinbei District is necessary.

Microsimulation Analysis of Traffic Operations at Two Diamond Interchange Types

The operational performance of standard Single Point Urban Interchange (SPUI) and Tight Diamond Interchange (TDI) has already been widely studied. In general, SPUI is more efficient than a TDI in terms of increased capacity and decreased traffic delays even though building a SPUI generally incurs a higher cost. However, due to right-of-way constraints, a standard SPUI may not be implementable at locations with restricted land use; thus, the variations of SPUI are usually considered. Currently, there is no established methodology or guideline available on the performance evaluation of variations of SPUI. This paper aims to investigate the operational efficiency of SPUI with frontage roads (SPUI-F, a variation of SPUI). Based on a field case, the performance of SPUI-F was investigated using microsimulation. An analytical model for capacity estimation, which considered early return and discharge flow rate, was also established and validated based on microsimulation. Multiple traffic scenarios were analyzed and their performance measures were compared against the equivalent TDI design. Simulation results revealed that TDI outperformed SPUI-F in terms of average delay, speed, and queue length, and the proposed analytical model can be used for reliable capacity and delay estimation. The findings from this study can aid the decision-makers choosing an appropriate interchange type for achieving the best benefit-cost ratio.

Load Sharing and Wayside Battery Storage for Improving AC Railway Network Performance, With Generic Model for Capacity Estimation, Part 1

Load sharing between feeder stations has the ability to significantly improve the performance of ac electric railways by reducing the number of traction power network grid connections and minimizing losses and plant size. In this paper, the existing rail power conditioner technology, traditionally used for load balancing, is investigated from the new perspective of providing reactive voltage support and active power sharing between stations. The concept is validated through a feasibility study conducted for a heavy haul ac railway network.

Lifetime Rapid Evaluation Method for Lithium-Ion Battery with Li(NiMnCo)O2 Cathode

This paper conducts battery cyclic life tests under a full state-of-charge range (0-100%) and five partitioned state-of-charge ranges (0–20%, 20%–40%, 40%–60%, 60%–80% and 80%–100%). The attenuation of state-of-health indicator parameters along with cycle times under different state-of-charge ranges is compared. For the batteries cycled under 20% state-of-charge depth, loss of lithium inventory (LLI) is the leading factor resulting in capacity degradation. Expanding cycle state-of-charge depth accelerates the loss of electrode active material, but has little effects on LLI. When suffering identical cycle times, the sum of the decrements of state-of- health indicator parameter related to LLI under the five partitioned state-of-charge ranges is equal to 0–100% state-of-charge, which proves the additivity of LLI. Considering the linear correlation between capacity and state-of-health indicator parameters, a linear regression capacity model is established. Then, based on the additivity of LLI and capacity estimation model, an innovative evaluation method of battery lifetime with less testing time consumption is proposed, which conducts cycle aging tests under the partitioned state-of-charge ranges replacing full state-of-charge range to reduce testing time. Using this method, time consumption can be reduced by 75%, and the estimation error of capacity and lifetime is 3% and 10% respectively.

Load Sharing and Wayside Battery Storage for Improving AC Railway Network Performance With Generic Model for Capacity Estimation—Part 2

This paper investigates the use of large-scale wayside energy storage to reduce operational costs of ac electric traction power networks. This paper also introduces a method of estimating the battery capacity, based on manufacturer's charging and discharging specifications. The battery model is simple and easy to incorporate into load flow simulation algorithms. The concept and the battery model are validated through a feasibility study within Aurizon's heavy haul electric railway network.

On the uncertainty analysis of uplift capacity of suction caissons in clay based on the fuzzy sets theory

Uncertainties in the input parameters of predictive models for uplift capacity of suction caisson lead to uncertain performance. To account for the influence of such uncertainties on the suction caisson stability, a fuzzy model is employed. The input uncertainties are introduced to the predictive formulas by triangular membership functions. To obtain the extreme values of the uplift capacity, optimization problems with many objectives are solved by the coupling of genetic algorithm (GA) with the uplift capacity estimation models. The relationships derived by different methods for the prediction of the uplift capacity, are analyzed by the fuzzy approach using a compiled database. Through the membership functions of several statistical measures, it is inferred that small input uncertainties can significantly affect the responses. Also, the recently proposed “M5-GP” - based prediction models are found to be vulnerable to input uncertainties, hence, a new revision called “Improved M5GP” model is developed. Finally, it is shown that the presented model is the best among various models regarding the reliability as well as the accuracy.

A Capacity Estimation Model for a Contraflow Left-Turn Pocket Lane at Signalized Intersections

Alternative intersection designs are increasingly proposed and adopted by different agencies to meet the needs of growing traffic demand and constrained transportation resources. The left turn (LT) is one of the most critical movements at signalized intersections from both a safety and operations perspective. Heavy LT volumes are especially impactful to the operational efficiency of a signalized intersection and often result in queue spillback. A contraflow left-turn pocket lane (CLPL) is proposed to mitigate congestion caused by heavy LT demand and has been shown in simulation to greatly mitigate the impact of queue spillback. The CLPL dynamically uses the opposing through lane (OTL) as an additional LT lane within the signal cycle on a temporary basis when the OTL is not occupied by through traffic. While geometric design schematics and analytical procedures for estimating delay have been proposed and discussed in existing literature, methodologies for estimating capacity benefits and traffic operations are not yet well defined. This paper has three primary contributions to the literature: development of a probabilistic capacity estimation model, exploration of the impact of key characteristics (e.g., cycle length, LT demand, lane selection preference) on estimated intersection capacity, and recommendations for the real-world implementation of a CLPL. The simulation results indicate that the CLPL treatment can increase a signalized intersection’s throughput up to 25% and decrease the intersection’s average delay by 35%.

A Distributed PV System Capacity Estimation Approach Based on Support Vector Machine with Customer Net Load Curve Features

Most distributed photovoltaic systems (DPVSs) are normally located behind the meter and are thus invisible to utilities and retailers. The accurate information of the DPVS capacity is very helpful in many aspects. Unfortunately, the capacity information obtained by the existing methods is usually inaccurate due to various reasons, e.g., the existence of unauthorized installations. A two-stage DPVS capacity estimation approach based on support vector machine with customer net load curve features is proposed in this paper. First, several features describing the discrepancy of net load curves between customers with DPVSs and those without are extracted based on the weather status driven characteristic of DPVS output power. A one-class support vector classification (SVC) based DPVS detection (DPVSD) model with the input features extracted above is then established to determine whether a customer has a DPVS or not. Second, a bootstrap-support vector regression (SVR) based DPVS capacity estimation (DPVSCE) model with the input features describing the difference of daily total PV power generation between DPVSs with different capacities is proposed to further estimate the specific capacity of the detected DPVS. A case study using a realistic dataset consisting of 183 residential customers in Austin (TX, U.S.A.) verifies the effectiveness of the proposed approach.

Bus Capacity Estimation using Stochastic Queuing Models for Isolated Bus Stops in China

When serving passengers at a bus stop, a dwelling bus may cause a bottleneck that constrains traffic flow near the bus stop. A major element in bus system capacity is bus stop capacity (veh/h). Therefore, this study proposes a method estimating the capacity of bus stops isolated from the influences of traffic signals and other bus stops. Data collected for the seven most common bus stop configurations in China were used to validate the proposed model. The results indicated that the arrival process obeys a Poisson distribution and the service times fit a lognormal distribution. Stochastic queuing models for both single-berth and multi-berth stops were developed to estimate bus stop capacity and bus delay time. To enable comparison, the Highway Capacity Manual (HCM) model for bus stop capacity estimation was also used. The results showed that the proposed method was more accurate and reliable, with a mean absolute percentage error (MAPE) of 8.44% compared with a MAPE of 17.42% from the HCM method. In addition, the proposed method reduced root mean squared error (RMSE) by 58.8%, mean absolute error (MAE) by 57.9%, and variance of absolute percentage error (VAPE) by 52.6% compared with the values from the HCM model. Sensitivity analyses were also conducted to investigate the effects of bus arrival rate, bus service time, and the number of bus berths on the capacity of bus stops. The results indicated that the proposed method can be used as a guide for designing bus stop facilities under specified conditions.

Flow level performance evaluation in mobile networks: Analytical modeling and empirical validation

The purpose of this paper is to identify, through empirical validation, the best analytical model for QoS and capacity estimation in mobile data networks. We first present two different sets of models that have been proposed in the literature: the infinite source, Erlang-like models and the finite source, Engset-like models. While the former models are widely developed and adapted to mobile networks, the latter are less used in the mobile context. We thus derive novel analytical models to complete the finite source theory before moving to the comparison of the different models. We make use of network measurements originating from an HSPA network and compare the modeled performance with the observed one, for each of the available analytical models. Our results show that a Processor Sharing analysis that takes as cell capacity the harmonic average of the achievable throughputs and as traffic inputs the volumes generated in each CQI, gives results that are close to the field measurements. As of the finite source models, we observed a good match on some cells and a mismatch on other cells. This is due to the difficulty of extracting traffic parameters from the field. However, finite source models have a better predictive power than infinite source models, as their traffic characteristics can more easily be linked to changes in the network such as the introduction of new services or new devices.