Energy Management System For Three-Wheel Light Electric Vehicle Using Multi-Sources Energy Models

• Main Author: Azidin, Farid Arafat
• Format: Thesis
• Language: English; English
• Published: UTeM 2016
• Subjects: T Technology (General); TL Motor vehicles. Aeronautics. Astronautics
• Online Access: http://eprints.utem.edu.my/id/eprint/18845/1/Energy%20Management%20System%20For%20Three-Wheel%20Light%20Electric%20Vehicle%20Using%20Multi-Sources%20Energy%20Models%2024%20Pages.pdf; http://eprints.utem.edu.my/id/eprint/18845/2/Energy%20Management%20System%20For%20Three-Wheel%20Light%20Electric%20Vehicle%20Using%20Multi-Sources%20Energy%20Models.pdf; http://eprints.utem.edu.my/id/eprint/18845/; https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=101823&query_desc=au%2Cwrdl%3A%20Farid%20Arafat%20Azidin

Hybrid electric vehicles, plug-in hybrid electric vehicles, battery electric vehicles, fuel-cell vehicles are just a few technologies that are being researched worldwide today. Applying renewable energy such as battery, fuel cell and super-capacitor in the electric vehicle is a smart and ideal solution. However, battery as a single-source in electric vehicle has many disadvantages such as limited travel distance and longer charging time. Besides, battery reduces its electrical characteristics through high current flow, high temperature, self-discharge and low battery capacity level. Fuel cell has low power response during sudden energy demand and requires an expensive infrastructure for refueling. In case of light fuel cell vehicle, small tank is practical for exchange tank. In super-capacitor side, it cannot support enough energy for a single powered electric vehicle purposes, however can be used as secondary power supply. Thus, an intelligent energy management system (EMS) of various sources is necessary to counterbalance the drawback of the sources. To solve the problem, the objective of the research is to develop an intelligent EMS which can conduct multi-sources for three wheel light electric vehicle (LEV). A rule-based control algorithm which contains eight states in EMS is designed to control power switches and to ensure sufficient energy is delivered to the load. The work of this research begins by electrical analysis in PSPICE simulation which focuses in circuit design and testing the state condition. A close loop vehicle system implemented with intelligent EMS is designed in MATLAB/Simulink. The simulation model is simulated with a real three wheel scooter specification which has capacity of 5.4 kW DC machine. To show effectiveness of the developed vehicle system, the performance and efficiency of the vehicle simulation is compared with standard drive cycle such as ECE-47 and ECE-15. To justify the simulation model, a scaled-down lab test bench model is designed using dSPACE DS 1104. The LEV model with 18 W load power is implemented in the developed test bench prototype. As a result, the vehicle system specification for the lab test bench model is reduced accordingly to the ratio of load power. The power specifications of the multi-source models such as 30 W for fuel cell, 3 Ah for rechargeable sealed lead acid battery and 100F for super-capacitors have been used. An EMS hardware is designed to offer a bridge between MATLAB/Simulink and dSPACE DS 1104. In the EMS hardware design, the switching relay is used for selection of the sources and current transducers which are used for measuring load current and battery capacity. All input and output signals from the EMS hardware design are connected to the dSPACE DS 1104 for data presentation in graphical user interface. For the uphill simulation test, using ECE-47 drive cycle, multi-source energy models shows that the power effectiveness is 94.6% where as for the battery, as a single-source, it is 84.9%. The lab test bench model also proved that in extension of 33% of speed ECE-47 drive cycle, the energy efficiency of multi-source LEV is 80.2% which is better performance than that of combustion engine energy efficiency of 29.2%. Therefore, the system equipped with an intelligent control algorithm has promising potential in vehicle energy management applications for the future.