Ali Ashtari

PEV Charging Profile Prediction and Analysis Based on Vehicle Usage Data

Present-day urban vehicle usage data recorded on a per second basis over a one-year period using GPS devices installed in 76 representative vehicles in the city of Winnipeg, Canada, allow predicting the electric load profiles onto the grid as a function of time for future plug-in electric vehicles. For each parking occurrence, load profile predictions properly take into account important factors, including actual state-of-charge of the battery, parking duration, parking type, and vehicle powertrain. Thus, the deterministic simulations capture the time history of vehicle driving and parking patterns using an equivalent 10 000 urban driving and parking days for the city of Winnipeg. These deterministic results are then compared to stochastic methods that differ in their treatment of how they model vehicle driving and charging habits. The new stochastic method introduced in this study more accurately captures the relationship of vehicle departure, arrival, and travel time compared to two previously used stochastic methods. It outperforms previous stochastic methods, having the lowest error at 3.4% when compared to the deterministic method for an electric sedan with a 24-kWhr battery pack. For regions where vehicle usage data is not available to predict plug-in electric vehicle load, the proposed stochastic method is recommended. In addition, using a combination of home, work, and commercial changing locales, and Level 1 versus Level 2 charging rates, deterministic simulations for urban run-out-of-charge events vary by less than 4% for seven charging scenarios selected. Using the vehicle usage data, charging scenarios simulated have no significant effect on urban run-out-of-charge events when the battery size for the electric sedan is increased. These results contribute towards utilities achieve a more optimal cost balance between: 1) charging infrastructure; 2) power transmission upgrades; 3) vehicle battery size; and 4) the addition of new renewable generation to address new electric vehicle loads for addressing energy drivers.

Using a priori Information for Regularization in Breast Microwave Image Reconstruction

Regularization methods are used in microwave image reconstruction problems, which are ill-posed. Traditional regularization methods are usually problem-independent and do not take advantage of a priori information specific to any particular imaging application. In this paper, a novel problem-dependent regularization approach is introduced for the application of breast imaging. A real genetic algorithm (RGA) minimizes a cost function that is the error between the recorded and the simulated data. At each iteration of the RGA, a priori information about the shape of the breast profiles is used by a neural network classifier to reject the solutions that cannot be a map of the dielectric properties of a breast profile. The algorithm was tested against four realistic numerical breast phantoms including a mostly fatty, a scattered fibroglandular, a heterogeneously dense, and a very dense sample. The tests were also repeated where a 4 mm × 4 mm tumor was inserted in the fibroglandular tissue in each of the four breast types. The results show the effectiveness of the proposed approach, which to the best of our knowledge has the highest resolution amongst the evolutionary algorithms used for the inversion of realistic numerical breast phantoms.

Radar signal design using chaotic signals

The use of chaotic signals in radar imaging applications present particular advantages as they behave like pseudo noise, have a wide band, and are easy to generate. A chaotic frequency modulated (FM) sine wave is an example of a chaotic signal that can yield higher transmitted mean power when peak-power limited transmitters are used. Unlike the random FM signal, the behavior of chaotic FM signals is not fully understood. In this paper, two approaches for analyzing the spectrum of chaotic FM signals are discussed. The first approach approximates the chaotic signal with noise and the second one, deals with the condition for the chaotic signal to remain chaotic after frequency modulation and consequently have a wide band spectrum.

Using Large Driving Record Samples and a Stochastic Approach for Real-World Driving Cycle Construction: Winnipeg Driving Cycle

The challenges in the development of plug-in electric vehicle PEV powertrains are efficient energy management and optimum energy storage, for which the role of driving cycles that represent driver behaviour is instrumental. Discrepancies between standard driving cycles and real driving behaviour stem from insufficient data collection, inaccurate cycle construction methodology, and variations because of geography. In this study, we tackle the first issue by using the collected data from real-world driving of a fleet of 76 cars for more than one year in the city of Winnipeg Canada, representing more than 44 million data points. The second issue is addressed by a proposed novel stochastic driving cycle construction method. The third issue limits the results to mainly Winnipeg and cities that have similar features, but the methodology can be used anywhere. The methodology develops the driving cycle using snippets extracted from recorded time-stamped speed of the vehicles from the collected database. The proposed Winnipeg Driving Cycle WPG01 characteristics are compared to eight existing standard driving cycles and are more able to represent aggressive driving, which is critical in PEV design. An attempt is made to isolate how many differences could be attributed to the sample size and the methodology. The proposed construction methodology is flexible to be optimized for any selection of driving parameters and thus can be a recommended approach to develop driving cycles for any drive train topology, including internal combustion engine vehicles, hybrid vehicles, plug-in hybrid, and battery electric vehicles. Characterization of vehicle parking durations and types of parking home, work, shopping, critical for duty cycles for PEV powertrains, are reported elsewhere. Here, the focus is on the mathematical approach to develop a drive cycle when a large database with high resolution of driving data is available.

Hybrid binary-real GA for microwave breast tomography

The recent information about breast tissue properties presented in [1] was used to design a new inverse scattering method for the application of breast imaging. The proposed method uses the combination of real and binary GAs which reduces the computational cost. The binary GA was used for the discrete search space that only finds the type of the tissue and real GA was used to find the water content percentage which covers a domain of real variables. The method was tested on a simulated example and showed promising results. Future studies will be performed to verify this method with experimental data.

3D breast microwave imaging based on wavefront reconstruction.

Currently, breast cancer is the leading cause of cancer death in women between the ages of 15 and 54, and the second cause of cancer death in women 55 to 74. In recent years, Breast Microwave Imagery (BMI) has shown its potential as a promising breast cancer detection technique. This imaging technology is based on the electrical characteristic differences that exist between normal and malignant breast tissues at the microwave frequency range. A novel reconstruction approach for the formation of 3D BMI models is proposed in this paper. This technique uses the phase differences introduced during the collection of target responses in order to determine the correct spatial location of the different scatterers that constitute the final image. The proposed method yielded promising results when applied to simulated data

Generation of quasi-normal variables using discrete chaotic maps

We evaluated two random number generator algorithms using first-order and second-order chaotic maps. The first algorithm, which is based on the central limit theorem, allows us to approximate a Gaussian random variable as the sum of a given chaotic sequence. We considered two first-order maps (Bernoulli, Tent) and two second-order maps (Logistic, and Quadratic). In each instance, we verified that the sequence of random numbers had kurtosis of 3. In the case of the Bernoulli map, we determined that the statistical independence of samples is dependent on the map parameter B. The second algorithm, which is based on Von Neumanns Method, allowed us to reject samples from a chaotic sequence with uniform distribution to obtain a Gaussian distribution within a specific range (U, V). For the first-order maps, we estimated their probability density function in this range and computed deviations from the theoretical Gaussian density. In summary, we determined that samples generated via these two algorithms satisfied statistical tests for normal distributions, thus demonstrating that chaotic maps can be effectively to generate Gaussian samples.

Global Optimization: Differential Evolution, Genetic Algorithms, Particle Swarm, and Hybrid Methods

This chapter discusses common global optimization methods, such as differential evolution, genetic algorithms, and particle swarm optimization. It provides a survey of the many different strategies utilized in developing and improving these methods. In some sense, global optimization methods are by nature all heuristic-based approaches, as in infinitely sized search spaces with many ill-formed possible local minima it is not possible to analytically provide an optimal solution, as per the no-free-lunch theorem. Because of this, it is not possible to find one single heuristic which will always perform the best for all search spaces. This leads to many various heuristic approaches for different optimization problems. This chapter further provides a survey describing the many hybrid approaches taken to combine different types of both global optimization methods and global optimization methods with local optimization methods in attempts to improve convergence rates and expand exploration. It is important for the reader to recognize that performing global optimization is a balancing act between exploring the search space, to prevent premature convergence to local minima, and exploiting well-performing areas of the search space to quickly converge to a solution. Modifying the heuristics and hybridizing the search methods provide more parameters to tweak to change how the optimization technique explores and exploits areas, which will improve its performance on some problems, but potentially decrease its performance on others.

A method for combining focused monostatic and bistatic GPR to reduce multipath effects

Imaging of buried objects using subsurface microwave technology can result in images with numerous undesirable artifacts due in part to noise and multipath scattering. In order to alleviate the problem of multipath scattering, the authors propose the combined use of monostatic and bistatic systems. Focusing both images and compensating the bistatic system enables us to place the direct path scatterers at the same position as in the monostatic case. A multiplication of the final images will attenuate the scatterers that are formed by multiple reflections and will therefore reduce artifacts. Results are shown using simulations in which the signatures of several point scatterers overlap for the direct reflections and where the multipath signatures do not; thus allowing the multiplication to enhance the final image.

Benchmarking Parallel Evolutionary Algorithms

This chapter presents a simulation framework and it is used to examine the effectiveness of various asynchronous optimization methods on simulated distributed computing environments. Four benchmark functions were used to evaluate asynchronous versions of differential evolution, genetic algorithms, and particle swarm optimization. Given large-scale homogeneous and heterogeneous computing environments, asynchronous optimization is shown to have superior scalability and performance compared to synchronous implementations, even scaling to potentially millions of processors.