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平衡任务，在多数据集上显著优于基线，是通用高效适用于实时BMS部署的方案。
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。传统的多任务学习框架无法平衡SOH估计的有界、低方差噪声与长期RUL预测的无界、非线性扩展不确定性。本文提出了旋转SOH注入先验电池变压器（RoSIP-Batt），这是一个统一的联合估计框架，通过将联合预测公式化为贝叶斯多任务目标，引入了同方差不确定性加权机制，基于学习到的残差噪声水平动态缩放特定任务梯度。架构利用解耦双分类令牌和逐维门控融合机制，并通过梯度分离算子防止高方差RUL更新破坏稳定的SOH表示空间。为了在不依赖绝对循环步骤的情况下捕捉电化学降解模式，将旋转位置嵌入（RoPE）纳入共享变压器主干以对平移不变的相对时间轮廓进行建模。关键是，中间SOH估计作为物理降解先验直接注入RUL回归头。在NASA、MIT-Stanford和HUST数据集上的评估表明，RoSIP-Batt显著优于现有基线，在NASA上将SOH估计误差降低到1.994% MAE，在斯坦福大学将RUL预测误差限制在62.85个循环。这些发现确立了RoSIP-Batt作为一种高度通用、计算高效的解决方案，适用于实时嵌入式BMS部署。
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.
