Drive systems optimization in electric, hybrid and fuel cell vehicles
(English) We are currently immersed in the fourth industrial revolution that involves, among others, technology to prevent climate change, transformation of the transport sector, digitization and artificial intelligence. An important question regarding vehicles design optimization and environmental...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/691920 |
| Acceso en línea: | http://hdl.handle.net/10803/691920 https://dx.doi.org/10.5821/dissertation-2117-412770 |
| Access Level: | acceso abierto |
| Palabra clave: | Design optimization Efficiency Electric vehicle Fuel cell vehicle Genetic Algorithm Hybrid electric vehicle Micromobility Plug-in hybrid electric vehicle Range Smart City Àrees temàtiques de la UPC::Enginyeria mecànica 621 629 |
| Sumario: | (English) We are currently immersed in the fourth industrial revolution that involves, among others, technology to prevent climate change, transformation of the transport sector, digitization and artificial intelligence. An important question regarding vehicles design optimization and environmental care are energy management strategy and efficiency determination. There are different types of technology to alleviate this problem such as those used in pure electric, hybrid, plug-in hybrid or fuel cell vehicles. Automotive brands work with a wide range of technologies and electrified mobility is considered to be one of the solutions to the growing environmental question. My doctoral thesis contributes to technological development, to accelerate the design of ecological vehicles and to its introduction in smart cities. It is presented a novel approach to a model that forecasts specifications data of a future car to allow finding its optimal structure regarding its volume, weight and/or cost to reduce any social, environmental and/or economic impacts and, consequently, mitigates climate change and contributes to general conservation strategies. The objective of my doctoral thesis is to develop a flexible, expandable and simple mathematical methodology of vehicle design optimization, capable to maximize a vehicle range with finest computational effort, thanks to a genetic algorithm, and to give predictive information to minimize cost, volume and weight of the drive-train in vehicle structure and according to the designer’s desire. Initially, the desired range, specifications, and architecture of the vehicle must be known, as well as the drive cycle (time and speed). The reliability of the system has been verified making a component-to-component revision through commercially available 4-wheeled vehicles (e.g. Tesla X P90D, MIA electric, Twizy 45 and Twizy 80, BMW i3 Rex, Toyota Prius, Ford C-MAX Energi, Toyota Mirai, etc.) taking into account their technical specifications such as electric motor type (e.g. induction, permanent magnet and hybrid electric motor), technology of energy storage system (e.g. NiMH or lithium-ion battery and fuel cell), with different degrees of electrification (e.g. pure electric vehicle, series/parallel/series-parallel (plug-in) hybrid electric vehicle and fuel cell vehicle) and their category (light quadricycle [L6e], heavy quadricycle [L7e], passenger cars [M1], vans [N1] and low-speed vehicle) for different well-known/standard (i.e. EPA cycle, NEDC, JC08, WLTP, etc.) or customised drive cycles. All requisite data gathered is fed into the genetic algorithm to obtain the results. The methodology returns to the user the best choice, taking into account the desired requirements, in relation to the technology of the electric motor and the energy storage system, the price of each of them in ,€their volume in m3 and their weight in kg. Besides, an analysis of vehicle efficiency, energy consumption, battery and fuel cells costs and energy densities, electric motor costs and power densities is performed. Calculation of the carbon dioxide (CO2) formed from producing the electricity necessary to charge the batteries or produce H2 for the fuel cells. This information also helps designers when deciding the structure of a vehicle if they cannot decide on the primary energy source from which electricity is obtained. The results obtained demonstrates that the model is capable to give a result with great effectiveness and efficiency because the dispersion of the values of the discrepancy between real values and the calculated ones is less than 15 %. The future research work is going to be focused on expanding the casuistry in relation to vehicle configurations and the number of components that make up the drive train of an EV, (P)HEV and FCV. It is also planned to develop an application with the procedure and developed software. |
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