TY - GEN
T1 - Spatial Feature-Enhanced Base Station Deployment Optimization Method for Wireless Battery Management Systems
AU - Liu, Jiajie
AU - Cao, Huangkai
AU - Wu, Yingying
AU - Sun, Lei
N1 - Publisher Copyright:
© 2026 IEEE.
PY - 2026
Y1 - 2026
N2 - Wireless Battery Management Systems (wBMS) represent a transformative paradigm shift for enhancing modularity in Energy Storage Systems (ESS). However, signal attenuation and anisotropic multipath interference within metallic enclosures remain critical bottlenecks. While existing research predominantly focuses on protocol-layer logic, the pivotal impact of physical Base Station (BS) placement on link quality is often overlooked. This paper proposes a BS layout optimization framework underpinned by spatial feature enhancement. A Segmented Waveguide-Multiwall (SWMW) model is established to characterize the complex internal propagation, while a Conditional Diffusion Model (CDM) is leveraged to augment sparse RSSI fingerprints into high-resolution spatial maps. Finally, the deployment task is formulated as a multi-objective optimization problem and resolved via an Adaptive Sine-mapped Nutcracker Optimization Algorithm (AS-NOA). Experimental results demonstrate that the proposed method achieves excellent performance in spatial resolution and system robustness.
AB - Wireless Battery Management Systems (wBMS) represent a transformative paradigm shift for enhancing modularity in Energy Storage Systems (ESS). However, signal attenuation and anisotropic multipath interference within metallic enclosures remain critical bottlenecks. While existing research predominantly focuses on protocol-layer logic, the pivotal impact of physical Base Station (BS) placement on link quality is often overlooked. This paper proposes a BS layout optimization framework underpinned by spatial feature enhancement. A Segmented Waveguide-Multiwall (SWMW) model is established to characterize the complex internal propagation, while a Conditional Diffusion Model (CDM) is leveraged to augment sparse RSSI fingerprints into high-resolution spatial maps. Finally, the deployment task is formulated as a multi-objective optimization problem and resolved via an Adaptive Sine-mapped Nutcracker Optimization Algorithm (AS-NOA). Experimental results demonstrate that the proposed method achieves excellent performance in spatial resolution and system robustness.
KW - Conditional diffusion model
KW - Multi-objective optimization
KW - Spatial augmentation
KW - Wireless battery management system
UR - https://www.scopus.com/pages/publications/105048966647
U2 - 10.1109/AAIEE69872.2026.11649910
DO - 10.1109/AAIEE69872.2026.11649910
M3 - Conference contribution
AN - SCOPUS:105048966647
T3 - 2026 IEEE 2nd International Symposium on the Application of Artificial Intelligence in Electrical Engineering, AAIEE 2026
SP - 332
EP - 337
BT - 2026 IEEE 2nd International Symposium on the Application of Artificial Intelligence in Electrical Engineering, AAIEE 2026
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE International Symposium on the Application of Artificial Intelligence in Electrical Engineering, AAIEE 2026
Y2 - 10 April 2026 through 13 April 2026
ER -