A two-layer hierarchical optimization framework for the operational management of diesel/battery/supercapacitor hybrid powered vehicular propulsion systems

Zhang, Yifei; Li, Diao; Jin, Zheming; Xu, Chen; Pei, Haoying; Wu, Qiya; Zhang, Jia

doi:10.1016/j.jclepro.2022.134658

Cited by 8 publications

(1 citation statement)

References 39 publications

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“…This has been reported in the recent works, although quite limited. By implementing the big data-based management, vehicle operations can be better coordinated, power distribution can be optimized, and energy waste can be reduced [166,167].…”

Section: B Big Data Fault Diagnostic and Safety Warningmentioning

confidence: 99%

Multilevel Data-Driven Battery Management: From Internal Sensing to Big Data Utilization

Wei,

Liu,

Liu

et al. 2023

IEEE Trans. Transp. Electrific.

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Battery management system (BMS) is essential for the safety and longevity of lithium-ion battery (LIB) utilization. With the rapid development of new sensing techniques, artificial intelligence and the availability of huge amounts of battery operational data, data-driven battery management has attracted ever-widening attention as a promising solution. This review article overviews the recent progress and future trend of datadriven battery management from a multi-level perspective. The widely-explored data-driven methods relying on routine measurements of current, voltage, and surface temperature are reviewed first. Within a deeper understanding and at the microscopic level, emerging management strategies with multidimensional battery data assisted by new sensing techniques have been reviewed. Enabled by the fast growth of big data technologies and platforms, the efficient use of battery big data for enhanced battery management is further overviewed. This belongs to the upper and the macroscopic level of the data-driven BMS framework. With this endeavor, we aim to motivate new insights into the future development of next-generation data-driven battery management.

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Section: B Big Data Fault Diagnostic and Safety Warningmentioning

confidence: 99%