Modified Interpolation Method of Multi-Reference Station Tropospheric Delay Considering the Influence of Height Difference

In network real-time kinematic (NRTK) positioning, atmospheric delay information is critical for generating virtual observations at a virtual reference station (VRS). The traditional linear interpolation method (LIM) is widely used to obtain the atmospheric delay correction. However, even though the conventional LIM is robust in the horizontal direction of the atmospheric error, it ignores the influence of the vertical direction, especially for the tropospheric error. If the height difference between the reference stations and the rover is large and, subsequently, tropospheric error and height are strongly correlated, the performance of the traditional method is degraded for tropospheric delay interpolation at the VRS. Therefore, considering the height difference between the reference stations and the rover, a modified linear interpolation method (MLIM) is proposed to be applied to a conventional single Delaunay triangulated network (DTN). The systematic error of the double-differenced (DD) tropospheric delay in the vertical direction is corrected first. The LIM method is then applied to interpolate the DD tropospheric delay at the VRS. In order to verify the performance of the proposed method, we used two datasets from the American NOAA continuously operating reference stations (CORS) network with significant height differences for experiments and analysis. Results show that the DD tropospheric delay interpolation accuracy obtained by the modified method is improved by 84.1% and 69.6% on average in the two experiments compared to the conventional method. This improvement is significant, especially for low elevation satellites. In rover positioning analysis, the traditional LIM has a noticeable systematic deviation in the up component. Compared to the conventional method, the positioning accuracy of the MLIM method is improved in the horizontal and vertical directions, especially in the up component. The accuracy of the up component is reduced from tens of centimeters to a few centimeters and demonstrates better positioning stability.

Performance of Existing Methods in Baselining Demand Response from Commercial Building HVAC Fans

Commercial buildings generally have large thermal inertia, and thus can provide services to power grids (e.g., demand response (DR)) by modulating their Heating, Ventilation, and Air Conditioning (HVAC) systems. Shifting consumption on timescales of minutes to an hour can be accomplished through temperature setpoint adjustments that affect HVAC fan consumption. Estimating the counterfactual baseline power consumption of HVAC fans is challenging but is critical for assessing the capacity and participation of DR from HVAC fans in grid-interactive efficient buildings (GEBs). DR baseline methods have been developed for whole-building power profiles. This work evaluates those methods on total HVAC fan power profiles, which have different characteristics than whole-building power profiles. Specifically, we assess averaging methods (e.g., Y-day average, HighXofY, and MidXofY, with and without additive adjustments), which are the most commonly used in practice, and a least squares-based linear interpolation method recently developed for baselining HVAC fan power. We use empirical submetering data from HVAC fans in three University of Michigan buildings in our assessment. We find that the linear interpolation method has a low bias and by far the highest accuracy, indicating that it is potentially the most effective existing baseline method for quantifying the effects of short-term load shifting of HVAC fans. Overall, our results provide new insights on the applicability of existing DR baseline methods to baselining fan power and enable more widespread contribution of GEBs to DR and other grid services.

An experimental study on optimal spark timing control for improved performance of a flex fuel vehicle engine

In the American continent, the most frequently used alternative fuel is ethanol. Especially in Brazil, various blends of gasoline–ethanol fuels are widely spread. The vehicle using blended fuel is called flexible fuel vehicle. Because of several selections for the blending ratios in gas stations, the fuel properties may vary after refueling depending on a driver’s selection. Also, the combustion characteristics of the flexible fuel vehicle engine may change. In order to respond to the flexible fuel vehicle market in Brazil, a study on blended fuels is performed. The main purpose of this study is to enhance performance of the flexible fuel vehicle engine to target Brazilian market. Therefore, we investigated combustion characteristics and optimal spark timings of the blended fuels with various blending ratios to improve the performance of the flexible fuel vehicle engine. As a tool for prediction of the optimal spark timing for the 1.6L flexible fuel vehicle engine, the empirical equation was suggested. The validity of the equation was investigated by comparing the predicted optimal spark timings with the stock spark timings through engine tests. When the stock spark timings of E0 and E100 were optimal, the empirical equation predicted the actual optimal spark timings for blended fuels with a good accuracy. In all conditions, by optimizing spark timing control, performance was improved. Especially, torque improvements of E30 and E50 fuels were 5.4% and 1.8%, respectively, without affecting combustion stability. From these results, it was concluded that the linear interpolation method is not suitable for flexible fuel vehicle engine control. Instead of linear interpolation method, optimal spark timing which reflects specific octane numbers of gasoline–ethanol blended fuels should be applied to maximize performance of the flexible fuel vehicle engine. The results of this study are expected to save the effort required for engine calibration when developing new flexible fuel vehicle engines and to be used as a basic strategy to improve the performance of other flexible fuel vehicle engines.

Vehicle Trajectory Reconstruction for Signalized Intersections with Low-Frequency Floating Car Data

Floating car data are beneficial in estimating traffic conditions in wide areas and are playing an increasing role in traffic surveillance. However, widespread application is limited by low-sample frequency which makes it hard to get a complete picture of a vehicle’s motion. An accurate and reliable reconstruction of a vehicle’s trajectory could effectively result in a higher sampling frequency enabling a more accurate estimation of road traffic parameters. Existing methods require additional information such as nearby vehicles, signal timing strategies, and queue patterns which are not always available. To address this problem, this paper presents a method used with low-sample frequency data to reconstruct vehicle trajectories through intersections, without the need for extra information. Furthermore, the additional parameters for the speed-time curve distributions for deceleration rate and acceleration rate are generated. A piecewise deceleration and acceleration model is developed to calculate the acceleration rate for different travel modes in the trajectory. The distribution parameters of the acceleration data for each travel mode are then estimated using a new Expectation Maximization (EM) algorithm. The acceleration statistics are then used to reconstruct the corresponding parts of the trajectory. Compared to the reference trajectories (truth), the test results show that the method developed in this paper achieves improvement in accuracy ranging from 16 to 67% over the commonly used linear interpolation method. In addition, the proposed method is not very sensitive to the sampling interval of the floating car data, unlike the linear interpolation method where the error grows rapidly with increasing sampling interval.