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A proactive energy management strategy for battery-powered autonomous systems

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  • Li, Heng
  • Liu, Zheng
  • Yang, Yingze
  • Yang, Huihui
  • Shu, Boyu
  • Liu, Weirong

Abstract

Battery energy management systems have been studied in control communities for many years. This paper proposes a new perspective by integrating control and scheduling for battery-powered autonomous systems. This is motivated by the observations that battery closed-loop control can significantly improve the DC-bus stability but reduce the battery durability, while load scheduling can considerably improve the battery durability by smoothing the load power. In view of the above findings, we propose a proactive energy management strategy for the battery energy management system to improve both the DC-bus stability and battery durability. We first analyze the schedulability of load tasks to check if they are schedulable. Then, the power of schedulable loads is scheduled with an active load scheduling algorithm to smooth the fluctuation of battery current and extend the battery lifetime. Thereafter, the scheduled load power is integrated with a feedforward control to restrain DC-bus voltage fluctuation. In addition to the classical sporadic/periodic load tasks model, we propose a new aperiodic load task model to characterize the load power triggered by events in practical applications. An experimental platform is built to verify the effectiveness of the proposed proactive energy management method. Experimental results show that the proposed proactive energy management method can suppress the 15.71% DC-bus voltage fluctuation and reduce the 8.93% battery current fluctuation, extending the battery lifetime by 7.57% compared to existing energy management strategies.

Suggested Citation

  • Li, Heng & Liu, Zheng & Yang, Yingze & Yang, Huihui & Shu, Boyu & Liu, Weirong, 2024. "A proactive energy management strategy for battery-powered autonomous systems," Applied Energy, Elsevier, vol. 363(C).
    • Handle: RePEc:eee:appene:v:363:y:2024:i:c:s0306261924003787
    DOI: 10.1016/j.apenergy.2024.122995
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    1. Alok Ranjan & Sanjay Bodkhe & Gaurav Goyal & Archana Belge & Sneha Tibude, 2024. "Experimental Study on Heuristics Energy Management Strategy for Hybrid Energy Storage System," Energies, MDPI, vol. 17(23), pages 1-15, November.

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