Increased understanding of hybrid vehicle design through modeling, simulation, and optimization

2010 Fall.Includes bibliographical references.Vehicle design is constantly changing and improving due to the technologically driven nature of the automotive industry, particularly in the hybridization and electrification of vehicle drive trains. Through enhanced design vehicle level design constraints can result in the fulfillment of system level design objectives. These constraints may include improved vehicle fuel economy, all electric range, and component costs which can affect system objectives of increased national energy independence, reduced vehicle and societal emissions, and reduced life-cycle costs. In parallel, as computational power increases the ability to accurately represent systems through analytical models improves. This allows for systems engineering which is commonly quicker and less resource consuming than physical testing. As a systems engineering technique, optimization has shown to obtain superior solutions systematically, in opposition to trial-and-error designs of the past. Through the combination of vehicle models, computer numerical simulation, and optimization, overall vehicle systems design can greatly improve. This study defines a connection between the system level objectives for advanced vehicle design and the component- and vehicle-level design process using a multi-level design and simulation modeling environment. The methods and information pathways for vehicle system models are presented and applied to dynamic simulation. Differing vehicle architecture simulations are subjected to a selection of proven optimization algorithms and design objectives such that the performance of the algorithms and vehicle-specific design information and sensitivity is obtained. The necessity of global search optimization and aggregate objective functions are confirmed through exploration of the complex hybrid vehicle design space. Whether the chosen design space is limited to available components or expanded to academic areas, studies can be performed for numerous design objectives and constraints. The combination of design criteria into quantifiable objective functions allows for direct optimization comparison based on any number of design goals. Integrating well-defined objective functions into high performing global optimization search methods provides increased probability of obtaining solutions which represent the most germane designs. Additionally, key interactions between different components in the vehicular system can easily be identified such that ideal directions for gain relative to minimal cost can be achieved. Often times vehicular design processes require lower order representations or consist of time and resource consuming iterations. Through the formulation presented in this study, more details, objectives, and methods become available for comparing advanced vehicles across architectures. The main techniques used for setting up the models, simulations and optimizations are discussed along with results of test runs based on chosen vehicle objectives. Utility for the vehicular design efforts are presented through comparisons of available simulation and future areas of research are suggested

Hybrid microgrid energy management and control based on metaheuristic-driven vector-decoupled algorithm considering intermittent renewable sources and electric vehicles charging lot

Energy management and control of hybrid microgrids is a challenging task due to the varying nature of operation between AC and DC components which leads to voltage and frequency issues. This work utilizes a metaheuristic-based vector-decoupled algorithm to balance the control and operation of hybrid microgrids in the presence of stochastic renewable energy sources and electric vehicles charging structure. The AC and DC parts of the microgrid are coupled via a bidirectional interlinking converter, with the AC side connected to a synchronous generator and portable AC loads, while the DC side is connected to a photovoltaic system and an electric vehicle charging system. To properly ensure safe and efficient exchange of power within allowable voltage and frequency levels, the vector-decoupled control parameters of the bidirectional converter are tuned via hybridization of particle swarm optimization and artificial physics optimization. The proposed control algorithm ensures the stability of both voltage and frequency levels during the severe condition of islanding operation and high pulsed demands conditions as well as the variability of renewable source production. The proposed methodology is verified in a state-of-the-art hardware-in-the-loop testbed. The results show robustness and effectiveness of the proposed algorithm in managing the real and reactive power exchange between the AC and DC parts of the microgrid within safe and acceptable voltage and frequency levels

A Bi-Layer Multi-Objective Techno-Economical Optimization Model for Optimal Integration of Distributed Energy Resources into Smart/Micro Grids

The energy management system is executed in microgrids for optimal integration of distributed energy resources (DERs) into the power distribution grids. To this end, various strategies have been more focused on cost reduction, whereas effectively both economic and technical indices/factors have to be considered simultaneously. Therefore, in this paper, a two-layer optimization model is proposed to minimize the operation costs, voltage fluctuations, and power losses of smart microgrids. In the outer-layer, the size and capacity of DERs including renewable energy sources (RES), electric vehicles (EV) charging stations and energy storage systems (ESS), are obtained simultaneously. The inner-layer corresponds to the scheduled operation of EVs and ESSs using an integrated coordination model (ICM). The ICM is a fuzzy interface that has been adopted to address the multi-objectivity of the cost function developed based on hourly demand response, state of charges of EVs and ESS, and electricity price. Demand response is implemented in the ICM to investigate the effect of time-of-use electricity prices on optimal energy management. To solve the optimization problem and load-flow equations, hybrid genetic algorithm (GA)-particle swarm optimization (PSO) and backward-forward sweep algorithms are deployed, respectively. One-day simulation results confirm that the proposed model can reduce the power loss, voltage fluctuations and electricity supply cost by 51%, 40.77%, and 55.21%, respectively, which can considerably improve power system stability and energy efficiency.</jats:p

Detection of Lying Electrical Vehicles in Charging Coordination Application Using Deep Learning

The simultaneous charging of many electric vehicles (EVs) stresses the distribution system and may cause grid instability in severe cases. The best way to avoid this problem is by charging coordination. The idea is that the EVs should report data (such as state-of-charge (SoC) of the battery) to run a mechanism to prioritize the charging requests and select the EVs that should charge during this time slot and defer other requests to future time slots. However, EVs may lie and send false data to receive high charging priority illegally. In this paper, we first study this attack to evaluate the gains of the lying EVs and how their behavior impacts the honest EVs and the performance of charging coordination mechanism. Our evaluations indicate that lying EVs have a greater chance to get charged comparing to honest EVs and they degrade the performance of the charging coordination mechanism. Then, an anomaly based detector that is using deep neural networks (DNN) is devised to identify the lying EVs. To do that, we first create an honest dataset for charging coordination application using real driving traces and information revealed by EV manufacturers, and then we also propose a number of attacks to create malicious data. We trained and evaluated two models, which are the multi-layer perceptron (MLP) and the gated recurrent unit (GRU) using this dataset and the GRU detector gives better results. Our evaluations indicate that our detector can detect lying EVs with high accuracy and low false positive rate