GA Optimization for Regression Modeling of Electromagnetic Performances Predicted by a Subdomain Model for SMPMSM in an Electric Vehicle

This paper investigates a nonlinear modeling optimization of 12s/8p surface-mounted permanent magnet synchronous machines (SMPMSM) with a radial magnetization pattern. The modeling is based on subdomain model (SDM) computation, where the analytical models are developed to predict the electromagnetic (EM) performances, such as, average EM torque and EM torque ripple in PM machines. A genetic algorithm is applied to the proposed model in order to search for the optimal solutions. The objective function of the optimizations is obtaining a higher average EM torque and achieving the minimum EM torque ripple. The data, viz, and the average EM torque and its ripples predicted by SDM are employed in regression analysis in order to find the model of best fit. After that, the most suitable fit of the computing equation is selected. The preliminary and optimal designs of 12s/8p PM motors are also compared in terms of parameters and motor performance. As a result, the regression model and GA framework has reduced the use of magnet materials and the EM torque ripple of the SMPMSM, making it ideal for use in an electric car. Lastly, the proposed model can determine the appropriate configuration design parameters for SMPMSM in order to achieve the best motor performance.

New goods demand patterns in digital economy environment: The case of electric vehicles in Russia

The trend of using electric vehicles is changing the automotive industry. Electric cars are becoming the most environmentally friendly replacement for combustion vehicles. Knowing the preferences of potential consumers will allow developing effective solutions to create demand for this product. Generating demand should be based on estimating its potential and shaping the consumer profile of this type of transport for market of each country. New goods need special methods to generate demand, since their potential buyers have difficulties in purchase decision making. This paper presents results of a study on prospects in Russia for such new goods as electric vehicles. The study identified factors that ultimately determine the interest of those Russian consumers who have the financial ability to purchase electric vehicles in the near future in electric vehicles. The study demonstrates that consumer prejudices are still there against difficulties in operating electric vehicles. The study confirmed that a number of factors affect the purchase of an electric car in Russia. Expanding the presence of electric vehicles in carsharing companies will significantly improve experience in using this type of transport by potential users. Generating the demand for electric vehicles by applying influence marketing tools is the one of the best solutions.

USE OF NEURAL NETWORKS IN ADAPTIVE ELECTRIC CAR CONTROL

To overcome the lack of information about the parameters of the driving cycle of the electric car, neural networks are used, which provide adaptive control that allows you to adapt. electric car to external operating conditions, as well as to compensate for inaccuracies in mathematical models. Use of iterative optimization of parameters allows to adjust optimum work of power plant of the electric car (PEC) in the course of its movement. This method allows you to use a single approach to study different processes, regardless of the parametric features of electric vehicles. To accelerate adaptation, the neurocontroller and neural network model are trained using a reference control model, which is either an optimal strategy or a strategy based on logical rules of choice, obtained by methodical programming for a given driving cycle. Based on the results of the research, an adaptation algorithm is proposed. The expressions given in the article allow to carry out adaptation of the power plant on the basis of hybrid to the current driving cycle on the basis of the concept of training of the neuro-fuzzy controller with reinforcement. The expressions given in the article allow to carry out adaptation of the power plant on the basis of hybrid to the current driving cycle on the basis of the concept of training of the neuro-fuzzy controller with reinforcement. The purpose of training the neuro-fuzzy controller is the formation of such control effects of the power plant, which would reduce the quadratic value of the assessment of the quality of management.

Analysis of Fire Hazards Associated with the Operation of Electric Vehicles in Enclosed Structures

The article discusses the analysis of the possible development of hazards associated with the operation of vehicles equipped with an electric drive using the example of passenger cars. The authors review the problem of the safety of people and property resulting from the occurrence of a fire in an electric passenger car, in the context of fires that have occurred in recent years. Particular attention was paid to the analysis of the state of knowledge concerning the characteristics of the fire progression in an electric car, its heat release rate curve [HRR], total heat release [THR], heat of combustion and factors affecting the fire progression. In this paper, an attempt was made to compare the fire characteristics of an electric car and a passenger car equipped with an internal combustion engine together with an estimation, using CFD simulations, of the impact on the safety of people and property in closed structures such as underground garages or road tunnels. The need for further development of research on electric cars equipped with large lithium-ion batteries in the context of their fire safety is indicated. The authors pay attention to the insufficient amount of data available to understand the fire characteristics of modern electric cars, which would enable the appropriate design of fire safety systems in building structures.