AI 中文总结
研究锂离子电池SOH和RUL联合预测受任务异方差性阻碍的问题，提出RoSIP-Batt框架，通过贝叶斯多任务目标、同方差不确定性加权等机制，结合RoPE等，有效平衡任务，在多数据集上显著优于基线，是高效通用的实时BMS解决方案。
AI 中文摘要
可靠的锂离子电池管理系统的部署对于加速电气化至关重要，但健康状态（SOH）和剩余使用寿命（RUL）的联合预测仍受到任务异方差性的严重阻碍。传统多任务学习框架无法平衡SOH估计中有界、低方差噪声与长期RUL预测中无界、非线性扩展的不确定性。本文提出了旋转SOH注入先验电池变压器（RoSIP-Batt），一个统一的协同估计框架来解决这些优化冲突。通过将联合预测表述为贝叶斯多任务目标，引入同方差不确定性加权机制基于学习到的残余噪声水平动态缩放特定任务梯度。该架构利用解耦双分类令牌和逐维度门控融合机制，由梯度分离算子确保高方差RUL更新不会破坏稳定的SOH表示空间。通过将旋转位置嵌入（RoPE）纳入共享变压器主干来捕捉电化学降解模式。关键是，将中间SOH估计作为物理降解先验直接注入RUL回归头。在NASA、MIT-斯坦福和HUST数据集上的评估表明，RoSIP-Batt显著优于现有基线，在NASA上SOH估计误差降至1.994%平均绝对误差，在斯坦福将RUL预测误差限制在62.85个周期。这些发现表明RoSIP-Batt是适用于实时嵌入式BMS部署的高度通用、计算高效的解决方案。
英文摘要
The deployment of reliable lithium-ion battery management systems is crucial for accelerating electrification, yet the joint prognosis of State of Health (SOH) and Remaining Useful Life (RUL) remains severely hindered by task heteroscedasticity. Conventional multi-task learning frameworks fail to balance the bounded, low-variance noise of SOH estimation with the unbounded, nonlinearly expanding uncertainty of long-term RUL predictions. Here, we present the Rotary SOH-Injected Prior Battery Transformer (RoSIP-Batt), a unified co-estimation framework that resolves these optimization conflicts. By formulating joint prediction as a Bayesian multi-task objective, RoSIP-Batt introduces a homoscedastic uncertainty weighting mechanism to dynamically scale task-specific gradients based on learned residual noise levels. The architecture leverages decoupled dual classification tokens and a per-dimension gated fusion mechanism, secured by a gradient-detachment operator to prevent high-variance RUL updates from corrupting the stable SOH representation space. To capture electrochemical degradation patterns without relying on absolute cycle steps, Rotary Position Embedding (RoPE) is incorporated into a shared Transformer backbone to model translation-invariant relative temporal profiles. Crucially, the intermediate SOH estimate is directly injected into the RUL regression head as a physical degradation prior. Evaluations across the NASA, MIT-Stanford, and HUST datasets show that RoSIP-Batt significantly outperforms state-of-the-art baselines, reducing SOH estimation error to 1.994% MAE on NASA and restricting RUL prediction error to 62.85 cycles on Stanford. These findings establish RoSIP-Batt as a highly generalizable, computationally efficient solution suitable for real-time embedded BMS deployment.
