TY - GEN
T1 - Energy Management Strategy for Cross-Domain Vehicles Based on Multimodal Sensing under Large Disturbance Conditions
AU - Pang, Shengzhao
AU - Zhao, Siyu
AU - Ren, Xiaoran
AU - Song, Kaiyin
AU - Chen, Yingxue
AU - Mao, Zhaoyong
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - The Cross-Domain Vehicle (CDV) refers to a vehicle that can navigate underwater, on the water surface, and in the air. Also known as Unmanned Aerial-Underwater Vehicle (UAUV) by many researchers. It can enter and leave the water many times during the working process. This paper focuses on the intricate energy management issue of CDVs amidst large disturbances. Multimodal sensing technology is employed to collect comprehensive data from a variety of sensors, including motion, environmental, and energy-related sensors. A Improved Deep Q-Network algorithm is then utilized to optimize the energy distribution strategy. And attempt to address the lack of interpretability in deep learning models when dealing with practical problems. Simulation results demonstrate that this approach can effectively improve the energy utilization rate by 6.6% and reduce the SOC fluctuation of lithium battery 2.6%, thus offering an innovative solution for the energy management of cross-media vehicles.
AB - The Cross-Domain Vehicle (CDV) refers to a vehicle that can navigate underwater, on the water surface, and in the air. Also known as Unmanned Aerial-Underwater Vehicle (UAUV) by many researchers. It can enter and leave the water many times during the working process. This paper focuses on the intricate energy management issue of CDVs amidst large disturbances. Multimodal sensing technology is employed to collect comprehensive data from a variety of sensors, including motion, environmental, and energy-related sensors. A Improved Deep Q-Network algorithm is then utilized to optimize the energy distribution strategy. And attempt to address the lack of interpretability in deep learning models when dealing with practical problems. Simulation results demonstrate that this approach can effectively improve the energy utilization rate by 6.6% and reduce the SOC fluctuation of lithium battery 2.6%, thus offering an innovative solution for the energy management of cross-media vehicles.
KW - Cross-domain vehicle
KW - deep learning
KW - energy management
KW - hybrid electric vehicles
KW - reinforcement learning
KW - unmanned aerial-underwater vehicle
UR - https://www.scopus.com/pages/publications/105011033201
U2 - 10.1109/IAS62731.2025.11061455
DO - 10.1109/IAS62731.2025.11061455
M3 - 会议稿件
AN - SCOPUS:105011033201
T3 - Conference Record - IAS Annual Meeting (IEEE Industry Applications Society)
BT - 2025 IEEE Industry Applications Society Annual Meeting, IAS 2025
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2025 IEEE Industry Applications Society Annual Meeting, IAS 2025
Y2 - 15 June 2025 through 20 June 2025
ER -