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Optimization of the fuel economy and emissions for plug in hybrid electric recreational boat energy management strategy using genetic algorithm
Open accessAbstract
Today, the transportation sector has undergone a change from conventional vehicle to hybrid electric vehicle especially land-based with the aim to reduce fuel consumption and emissions. However, water transportation is also one of the contributors of excessive use of fuel and emissions. Therefore, water transport needs changes as it has been done on land transport, especially cars. In this paper, plug in hybrid electric recreational boat (PHERB) is introduced. PHERB is a special model because in PHERB powertrain configuration, it only needed one EM compared to existing configuration with energy management strategy (EMS). In this work, the optimal EMS for PHERB are presented via genetic algorithm (GA). To estimate the fuel economy and emissions, the model of PHERB is employed numerically in the MATLAB/SIMULINK environment with a special EMS using Kuala Terengganu (KT) river driving cycle. Simulation result of PHERB optimization using GA improve to 15% for KT river driving cycles without violating the PHERB performance.
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Data from California indicate that ToU tariffs produced a shift in EV charging, with 25-30% of charging taking place in nighttime hours.
“For example, data from California, indicating that ToU tariffs produced a shift in EV charging: 25–30% of charging took place in the nighttime hours,”
From Economic Impacts and Mitigation for the Grid Connected Electric Vehicle Charging: A Technical and Operational Overview (2026) · cites this paper as [82]
The PHERB Simulation and Optimization sections discuss a recreational-boat driving cycle and fuel consumption, not California EV charging. Searches for California, time-of-use tariffs, and the 25–30% nighttime figure found no passage addressing the claim. (LLM, conf=0.78)
Experimental — accuracy not yet measuredML algorithms that learn from data are emerging as core components of EV charging management systems by improving predictive, adaptive and decentralized control strategies.
“ML algorithms that learn from the data are emerging as the core components of EV charging management systems by improving the predictive, adaptive and decentralized control strategies [82], [97]-[103].”
From Economic Impacts and Mitigation for the Grid Connected Electric Vehicle Charging: A Technical and Operational Overview (2026) · cites this paper as [82]
The Introduction and PHERB Energy Management System sections (pages not given) describe genetic-algorithm optimization and operating modes for a hybrid recreational boat. Searches for data-learning algorithms and predictive, adaptive, or decentralized EV charging management found no passage addressing the attributed claim. (LLM, conf=0.76)
Experimental — accuracy not yet measuredReinforcement learning performs better than rule-based systems in suppressing peak demand and satisfying users through personalized charging plans.
“For example, reinforcement learning has been evidenced to perform better than rule-based systems in terms of suppressing the peak demand and satisfying the users through personalized charging plans [82].”
From Economic Impacts and Mitigation for the Grid Connected Electric Vehicle Charging: A Technical and Operational Overview (2026) · cites this paper as [82]
The Introduction describes a genetic-algorithm energy-management strategy for a plug-in hybrid recreational boat, and the Conclusion reports reduced fuel use and emissions. Searches for reinforcement learning, rule-based comparisons, peak-demand suppression, and personalized charging found no passage addressing the attributed claim. (LLM, conf=0.78)
Experimental — accuracy not yet measuredML-based systems support dynamic policy and demand-response mechanisms that adapt charging plans based on ToU rates or instantaneous grid frequency.
“Moreover, ML-based systems support dynamic policy and demand-response mechanisms that adapt to charging plans based on ToU rates or instantaneous grid frequency [82],[102].”
From Economic Impacts and Mitigation for the Grid Connected Electric Vehicle Charging: A Technical and Operational Overview (2026) · cites this paper as [82]
The Introduction describes a recreational boat’s energy-management modes, selected using battery state of charge and driving conditions. Searches for time-of-use rates, demand response, and instantaneous grid frequency found no passage about adapting charging plans to those signals. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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Research works printed but not linked: 9
- Minami, S., and N. Yamachika, "A practical theory of the performance of low velocity boat", Journal of Asian Electric Vehicles, Vol. 2, No. 1, pp.535-539, 2004.
- Minami, S., and N. Yamachika, "Experimental performance of a model River cruising electric boat electric-powered by a fuel Cell", Journal of Asian Electric Vehicles, Vol. 1, No. 2, pp.475-477, 2003.
- Minami, S., "Designing the river cruise electric boat", Journal of Asian Electric Vehicles, Vol. 1, No. 1, pp.131-138, 2003.
- Minami, S., T. Toki, N.Abdul Rahman S, Walker P.D., Zhang N., Zhu. J.G, and Du H. "A Comparative Study of Vehicle Drive Performance and Energy Efficiency". Sustainable Automotive Technologies. Pp.319-324; 2012.
- Yoshikawa, M. Hanada, S. Ashida, K. Kitada, and J. Tsukuda, "A Newly Developed Plug-in Hybrid Electric Boat (PHEB)", Journal of Asian Electric Vehicles, Vol. 8, No. 1, pp.1383-1392, 2011.
- Norbakyah JS, Atiq WH, and Salisa AR. "Power requirements for PHERB powertrain." IOP Conf. Series: Materials Science and Engineering 100. 2015.
- K. Somsai, A. Oonsivilai, A. Srikaew and T. Kulworawanichpong, "Optimal PI Controller Design and Simulation of a Static Var Compensator using MATLAB's SIMULINK," Proceedings of the 7th WSEAS International Conference on Power Systems, Beijing, China, September 15 -17, 2207, pp 30 -35.
- Abdul Rahman S, Zhang N., and Zhu. J.G. "Genetic Algorithm for UTS Plug-in Hybrid Electric Vehicle Parameter Optimization Abdul Rahman." 13th Asia Pacific Vibration Conference. 2009.
- Sidhartha Panda and Narayana Prasad Padhy, "MATLAB/SIMULINK Based Model of Single-Machine Infinite-Bus with TCSC for Stability Studies and Tuning Employing GA," International Journal of Computer Scniece and Engineering, pp 50 -59; 2007.
Cited by
Sorted by citations
Ikhajangbe, O. E., Dhulipati, H., & Liu, S. (2026) · IEEE Access · doi:10.1109/access.2026.3684508 · Cited by 5
Metadata
- Authors
- Norbakyah, J. S., & Salisa, A. R.
- Affiliations
- Universiti Malaysia Terengganu (MY)
- Venue
- International Journal of Power Electronics and Drive Systems (IJPEDS)
- Published
- 2019-06-01
- Volume
- 10
- Issue
- 2
- Pages
- 792
- ISSN
- 2088-8694 (ISSN-L)
- OpenAlex
- W2941409021
- Cited by
- 6
- References
- 0
- Citations by year
- 2023: 1 · 2022: 1 · 2021: 2 · 2020: 2
- FWCI
- 0.57
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- International Journal of Power Electronics and Drive Systems (IJPEDS) (publishedVersion, cc-by-sa)
- Funders
- Universiti Malaysia Terengganu
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Electric and Hybrid Vehicle Technologies
- Physical Sciences › Environmental Science › Environmental Engineering › Maritime Transport Emissions and Efficiency
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
- Keywords
- energy management strategy, fuel economy, emission reduction, genetic algorithm optimization, electric motors, water transportation, hybrid electric powertrain
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