发表机构
Ton Duc Thang University; AIWARE Limited Company; Van Lang University（孙德胜大学; AIWARE有限公司; 万朗大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对电池剩余放电时间预测中负载未知的难题，提出相对放电阶段（RDS）分类指标，结合SOC估计与轻量级时序网络，在两个公开数据集上实现超过80%的分类准确率。
AI 中文摘要
在实际电池应用中，由于未来负载曲线未知且高度动态，准确预测剩余放电时间（RDT）具有挑战性。为解决连续RDT回归的不确定性，本文引入相对放电阶段（RDS），这是一种电池管理指标，通过五个可解释类别表示剩余放电状态：正常、良好、中等、低和需要充电。与反映当前荷电状态的荷电状态（SOC）不同，RDS表征剩余放电过程，在推理期间无需未来电流信息。提出了一种物理信息驱动的RDS分类框架，将SOC估计与轻量级时序学习相结合。SOC估计组件包括二阶等效电路模型（ECM）状态和端电压预测、迟滞和开路电压（OCV）温度校正、核心温度估计以及自适应扩展卡尔曼滤波器（AEKF）状态校正，并辅以OCV评估、在线STC-ECM参数自适应和预训练神经残差电压校正。将测量电流、端电压、表面温度和估计SOC排列成滑动观测窗口，并由轻量级时序卷积网络处理。在两个公开锂离子电池数据集上的实验表明，在不同负载和热条件下，RDS分类准确率超过80%。
英文摘要
Accurate remaining discharge time (RDT) prediction is challenging in real-world battery applications because future load profiles are unknown and highly dynamic. To address the uncertainty of continuous RDT regression, this paper introduces Relative Discharge Stage (RDS), a battery-management indicator that represents the remaining discharge condition using five interpretable classes: Normal, Good, Moderate, Low, and Recharge Required. Unlike state of charge (SOC), which reflects the current charge level, RDS characterizes the remaining discharge process without requiring future-current information during inference. A physics-informed RDS classification framework is proposed, combining SOC estimation with lightweight temporal learning. The SOC-estimation component includes second-order ECM state and terminal-voltage prediction, hysteresis and OCV temperature correction, core-temperature estimation, and AEKF state correction, supported by OCV evaluation, online STC-ECM parameter adaptation, and pretrained neural residual-voltage correction. The measured current, terminal voltage, surface temperature, and estimated SOC are arranged into a sliding observation window and processed by a lightweight temporal convolutional network. Experiments on two public lithium-ion battery datasets demonstrate robust RDS classification, with accuracy exceeding 80% under varying load and thermal conditions.
