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297 / 2024-03-15 01:44:03
Prediction of Li-ion Battery Performance Degradation Using Data-driven Methods for Optimal Energy Management of Electrified Propulsion
Li-ion batteries,global optimization,performance degradation
4、智能化装备技术 > 4、智能制造装备技术
摘要录用
博 逄 / University of Victoria
Electrification of vehicular and marine propulsion systems using an electric drive and battery energy storage system (BESS) can substantially increase energy efficiency, reduce petroleum fuel use and lower greenhouse gas (GHG) emissions. However, the increased level of electrification leads to a large BESS and high investment and replacement costs. This research introduced new data-driven modelling and prediction methods for predicting Lithium-ion (Li-ion) batteries' performance and capacity degradations under different use patterns. The improved performance and capacity degradation predictions can better support the development of optimal energy management strategies (EMS) for hybrid and electric vehicles and marine vessels to achieve minimum lifecycle costs (LCC). Several data-driven modelling techniques have been applied, improved, and compared to address the present physics or semi-empirical battery degradation models' shortcomings and illustrate the advantages of the new approaches. The newly introduced data-driven battery performance and capacity modelling techniques have been applied to the optimal EMS development of a hybrid electric vehicle and passenger ferry. The resulting optimal EMS can strike the best balance between fuel efficiency and battery life to considerably lower the LCC for this type of heavy-duty propulsion system with a large and costly BESS, thus facilitating the adoption of clean propulsion technologies.
重要日期

  • 会议日期

    05月29日

    2024

    至

    06月01日

    2024

  • 05月08日 2024

    初稿截稿日期

主办单位
中国矿业大学
历届会议
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