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Deep learning powered real-time state of charge monitoring for vanadium flow battery energy storage system 认领 引用
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作者 Tianyu Li Jiawei Sun +8 位作者 Shengkai Xu Xijun Wang Huacheng Wu Feng Xing Zhao Ma He Jiang Zonghao Liu Tao Liu Xianfeng Li 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2026年第7期259-265,I0008,共7页
Vanadium flow batteries(VFBs)are well suitable for grid-scale energy storage owing to their long lifespan,high efficiency and safety.State of charge(SOC)monitoring is essential for battery health assessment and system... Vanadium flow batteries(VFBs)are well suitable for grid-scale energy storage owing to their long lifespan,high efficiency and safety.State of charge(SOC)monitoring is essential for battery health assessment and system management.However,accurate SOC determination during operation remains challenging due to vanadium ion crossover and side reactions that disrupt the valence and concentration balance between positive and negative electrolytes.Herein,an inverted transformer model,namely iTransformer,is employed to predict the SOC in VFB systems during charge–discharge cycles.The iTransformer-SOC model can achieve high accuracy and robustness.Even with training limited to the first three cycles,the model predicts SOC for the next 21 cycles with mean absolute percentage error(MAPE)less than 0.03.It can adapt to power variations and electrolyte rebalancing scenarios.Most importantly,an iTransformer-based SOC monitoring system was validated and confirmed by a 10 kW VFB system,demonstrating superior performance in predicting SOC of complete charge–discharge cycles(MAPE<0.05,less than 1/3 of the traditional open-circuit voltage(OCV)method's error).This datadriven approach provides a robust framework for real-time SOC monitoring in VFB systems,serving as a complementary alternative to physics-based model without requiring prior knowledge of system dynamics. 展开更多
关键词 Vanadium flow battery State of charge Data-driven iTransformer-based SOC model Real-time SOC monitoring
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