首页|Researchers from University of Malaysia Pahang Describe Findings in Machine Lear ning (Optimal Energy Management Strategies for Hybrid Electric Vehicles: a Recen t Survey of Machine Learning Approaches)
Researchers from University of Malaysia Pahang Describe Findings in Machine Lear ning (Optimal Energy Management Strategies for Hybrid Electric Vehicles: a Recen t Survey of Machine Learning Approaches)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on Machine Learning are discussed in a new report. According to news originating from Pekan, Malaysi a, by NewsRx correspondents, research stated, "Hybrid Electric Vehicles (HEVs) h ave emerged as a viable option for reducing pollution and attaining fuel savings in addition to reducing emissions. The effectiveness of HEVs heavily relies on the energy management strategies (EMSs) employed, as it directly impacts vehicle fuel consumption." Financial supporters for this research include Malaysian Ministry of Higher Educ ation, University Malaysia Pahang. Our news journalists obtained a quote from the research from the University of M alaysia Pahang, "Developing suitable EMSs for HEVs poses a challenge, as the goa l is to maximize fuel economy yet optimize vehicle performance. EMSs algorithms are critical in determining power distribution between the engine and motor in H EVs. Traditionally, EMSs for HEVs have been developed based on optimal control t heory. However, in recent years, a rising number of people have been interested in utilizing machinelearning techniques to enhance EMSs performance. This artic le presents a current analysis of various EMSs proposed in the literature. It hi ghlights the shift towards integrating machine learning and artificial intellige nce (AI) breakthroughs in EMSs development. The study examines numerous case stu dies, and research works employing machine learning techniques across different categories to develop energy management strategies for HEVs. By leveraging advan cements in machine learning and AI, researchers have explored innovative approac hes to optimize HEVs' performance and fuel economy. Key conclusions from our inv estigation show that machine learning has made a substantial contribution to sol ving the complex problems associated with HEV energy management."
PekanMalaysiaAsiaCyborgsEmerging TechnologiesMachine LearningUniversity of Malaysia Pahang
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