Problem
Research questions and friction points this paper is trying to address.
Remaining Discharge Time
Battery Management
Load Profile
Relative Discharge Stage (RDS) Classification: A Practical Indicator of Battery Discharge Progress
🤖 AI Summary
本文提出了一种基于物理信息的相对放电阶段(RDS)分类框架，通过结合SOC估计和轻量级时间学习来解决电池剩余放电时间预测的不确定性问题。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Relative Discharge Stage (RDS)
physics-informed RDS classification
lightweight temporal learning
state of charge (SOC) estimation
temporal convolutional network
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