Parametric optimization of a hybrid electric vehicle - Muhd Firdause bin Mangun (Kulliyyah of Engineering, International Islamic University Malaysia, 2017)
Over recent years, the threat of global warming and water-energy nexus issue alerted many developed nations. In response to this issue, especially in automotive technology, many researchers are working to improve hybrid electric vehicle (HEV) fuel economy. Works on sizing of HEV powertrain components to achieve high fuel economy is among the improvements. However, as the research and development process incur high cost, computer-based methods offer reduction in cost. Therefore, in this work, parametric optimization using software and simulation to determine component sizes is the chosen method to design HEV. Mainly, this work presents a study on fuel economy improvement by parametric optimization. Parallel topology is adopted for the hybrid electric vehicle since it has promising fuel economy with simple configuration. Firstly, HEV modelling is based on Quasi-static (QSS) backward-facing approach. Fuel consumption is calculated from a known drive cycle instead of driver input. A battery-supercapacitor system is used for energy storage. Adding supercapacitors allow efficient capturing of regenerative braking energy and perfect source of power for energy burst operations such as acceleration. Secondly, Fuzzy Logic power management controller (FLPMC) is implemented to manage sources of power or energy from main powertrain components. These components include the engine, electric motor, battery and supercapacitor. Thirdly, Genetic Algorithm (GA) method is used to optimize HEV powertrain components. Parametric optimization set up includes drive cycle combination (city and highway), performance constraints (time to accelerate from 0 to 100 km/h and gradeability), vehicle mass estimation, and different optimization objectives (minimization of fuel consumption or equivalent fuel consumption). For better global optimum search in this parametric optimization, GA initial settings are varied to broaden the design space. Based on the simulation results, when FLPMC performance is benchmarked against dynamic programming (DP) method, a comparable pattern is obtained. This was observed especially on gearbox torque required and final battery state of charge. In comparison with DP, FLMPC has a higher cumulative fuel consumption but simpler. Moreover, adding a supercapacitor and modifying FLPMC improves fuel economy by 10.7% for NEDC drive cycle and 17.2% for combined NEDC-HWFET drive cycle. Hence, the FLMPC design is comparable and appropriate for HEV parametric optimization process. Based on the optimization results, improvement of 18.9% in MPG (miles per gallon) and 16.0% in MPGe (miles per gallon equivalent) were observed when using combined city-highway drive cycle. The final optimized HEV powertrain design accomplishes smaller component size, gain benefits of supercapacitor implementation and most importantly, higher fuel economy with slight reduction in vehicle performance. In conclusion, the output of this study suits development purposes of HEV technology.