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020			_aTHE0010165 (Local) _qHardback
040			_aUMPSA _beng _cUMPSA _erda
090			_aFTKEE .I37 2025 r Thesis
100	1		_aMuhammad Ikram Bin Mohd Rashid, _eauthor.
245	1	0	_aMonte carlo hyper-heuristics optimisation for energy management strategy (ems) of hybrid electric vehicle / _cMuhammad Ikram Bin Mohd Rashid
264		1	_aKuantan, Pahang : _bUMPSA, _c2025
264		4	_c© 2025
300			_axiv, 168 pages : _billustrationS ; _c30 cm. + _e1 CD-ROM
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
347			_2rda _atext file _bPDF
500			_aFaculty of Electrical & Electronics Engineering Technology
502			_aThesis (Doctor of Philosophy) -- Universiti Malaysia Pahang – 2025
504			_aIncludes bibliographical references
520	3		_aWith increasing concern over the environment and ever-stringent emissions regulations, the hybrid electric vehicle (HEV) and battery electric vehicle (BEV) has been investigated as an alternative form of transportation. However, these vehicles still suffer from relatively low fuel consumption efficiency, making it an unacceptable solution to automotive consumers. The challenge is to develop an efficient energy management strategy (EMS) to satisfy the objectives while maintaining vehicle performance. So far, there has not been enough literature to discuss the energy management strategy (EMS) control schemes for HEV. Motivated by the above background, the Monte Carlo Hyper-heuristics optimisation for energy management strategy (EMS) of Hybrid Electric Vehicle(HEV) is considered in this study. The HEV is discussed in a new perspective from the EMS point of view. The Hyper-heuristics Trajectory-based Optimisation in Energy Management Strategy (EMS) for the HEV system was this research's focus. The optimal power loss value demonstrates a capacity for electrical efficiency enhancement from its initial value to improve the fuel consumption of HEV. It was observed that there was a higher performance when the above-mentioned technique was used to tune the gain value for the HEV system compared with that for a normal system. These single-agent selector techniques such as Random Search (RS), Simulated Annealing (SA), Simultaneous Perturbation Stochastic Approximation (SPSA) and Safe Experimentation Dynamics (SED) had compared each other to assess whether it can provide improved performance on EMS of HEV. After that, Monte Carlo hyper-heuristics optimisation was developed and evaluated to energy management strategy for the HEV model. The results show that the proposed optimisation method appeared to be an efficient and robust optimisation algorithm to search for an optimal solution for the EMS of the proposed HEV system. The results show that Monte Carlo Hyper-heuristics is situated in the first ranking based on the performance comparison, SED second, SPSA third, SA fourth and lastly RS. Lastly, the performance proposed hyper-heuristics trajectory-based optimisation was verified on recognition advanced vehicle simulator software such as ADvanced VehIcle SimulatOR (ADVISOR). Series hybrid model in ADVISOR has been chosen as a testbed. The Monte Carlo Hyper-heuristics has shown an improvement on fuel consumption compared before optimisation. The Monte Carlo Hyper-heuristics optimisation investigation for energy management strategy (EMS) of Hybrid Electric Vehicle(HEV) has disclosed that the scheme is significantly improved and appropriate for future HEV or BEV system operational and topology revolutions.
610	2	0	_aFaculty of Electrical & Electronics Engineering Technology _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aTheses _xDissertations
942			_2lcc _cTHESIS
999			_c103783 _d103789