用前9个循环预测铁铬液流电池全生命周期充电曲线，提前诊断健康状态。
Early Cycle Charge Trajectory Generative Prediction and Full Life Cycle Health Management of Iron-Chromium Flow Batteries Based on FlowBD-E1
- 基于多尺度卷积与时序注意力的生成框架，从早期循环预测全程电压电流轨迹。
- 仅用前9次充放电，后续全生命周期电压电流预测误差低于0.731%。
- 适合工业级液流电池健康监控，可将短周期数据转化为长期诊断依据。
长时储能需要能在显著容量衰减前检测退化的电池。铁铬液流电池因活性物质丰富且成本低而具吸引力，但其运行受铬反应动力学缓慢、析氢、膜渗透和电解液失衡等耦合过程影响，逐渐改变完整的充放电电压/电流（V/I）轨迹。现有电池寿命预测研究多聚焦锂离子电池，或将老化简化为单一容量或健康度（SOH）指标。本文研究了一套工业级33 kW Fe-Cr液流电池系统，提出FlowBD-E1：一种基于前几圈循环的早期生成式预测框架，可从初始少量循环数据中预测整个生命周期的完整充放电V/I轨迹。该模型结合多尺度卷积编码器、生命周期Transformer与年龄感知FiLM解码器，对比三种部署策略：单步潜空间外推（SLE）、递归潜空间预测（RLF）和教师强制更新（TFU）。使用前9/289个循环数据，RLF在剩余生命周期内实现联合V/I平均绝对百分比误差（MAPE）为0.731%，并使SOH估计误差低于1% MAPE。消融实验与独立序列测试表明，该年龄感知生成架构优于LSTM与TCN基线，在工业验证下仍保持亚百分级误差。结果表明，早期循环轨迹生成可将短周期调试数据转化为长期诊断信号，用于液流电池健康管理。
原文摘要 · Abstract (English)
Long-duration stationary energy storage requires batteries whose degradation can be detected before substantial capacity loss has accumulated. Iron-chromium redox flow batteries are attractive for this role because they use abundant and low-cost active species, yet their operation is shaped by slow chromium kinetics, hydrogen evolution, membrane crossover and electrolyte imbalance. These coupled processes gradually reshape the full charge voltage/current (V/I) trajectory, but most battery prognostic studies either focus on lithium-ion cells or compress ageing into scalar capacity and state-of-health (SOH) labels. Here we study an industrial 33 kW Fe-Cr redox flow battery and introduce FlowBD-E1, an early-cycle generative forecasting framework that predicts complete future charge V/I trajectories from only the first few cycles. The model combines a multi-scale convolutional encoder, a lifecycle Transformer and an age-aware FiLM decoder, and we compare three deployment strategies: single-step latent extrapolation (SLE), recursive latent forecasting (RLF) and teacher-forced updating (TFU). Using the first 9 of 289 cycles, RLF achieved a joint V/I mean absolute percentage error (MAPE) of 0.731% over the remaining lifecycle and produced SOH estimates below 1% MAPE. Ablation and independent-sequence tests showed that the age-aware generative architecture outperformed LSTM and TCN baselines and retained sub-percent errors under industrial validation. These results suggest that early-cycle trajectory generation can turn a short commissioning record into a long-horizon diagnostic signal for flow-battery management.
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