Conformalized Transfer Learning for Li-ion Battery State of Health Forecasting under Manufacturing and Usage Variability

Samuel Filgueira da Silva; Mehmet Fatih Ozkan; Faissal El Idrissi; Marcello Canova

cs.LGMar 25, 2026

Conformalized Transfer Learning for Li-ion Battery State of Health Forecasting under Manufacturing and Usage Variability

Authors: Samuel Filgueira da Silva, Mehmet Fatih Ozkan, Faissal El Idrissi, Marcello Canova

Organizations: Department of Mechanical and Aerospace Engineering, Center for Automotive Research, The Ohio State University, Columbus, OH, USA

Abstract

Accurate forecasting of state-of-health (SOH) is essential for ensuring safe and reliable operation of lithium-ion cells. However, existing models calibrated on laboratory tests at specific conditions often fail to generalize to new cells that differ due to small manufacturing variations or operate under different conditions. To address this challenge, an uncertainty-aware transfer learning framework is proposed, combining a Long Short-Term Memory (LSTM) model with domain adaptation via Maximum Mean Discrepancy (MMD) and uncertainty quantification through Conformal Prediction (CP). The LSTM model is trained on a virtual battery dataset designed to capture real-world variability in electrode manufacturing and operating conditions. MMD aligns latent feature distributions between simulated and target domains to mitigate domain shift, while CP provides calibrated, distribution-free prediction intervals. This framework improves both the generalization and trustworthiness of SOH forecasts across heterogeneous cells.

Figures & tables

Batch	C-rate Operating Set
Batch 1 ( $\mathcal{B}_{1}$ )	{3C, 4C, 5C}
Batch 2 ( $\mathcal{B}_{2}$ )	{1C, 2C, 3C}
Batch 3 ( $\mathcal{B}_{3}$ )	{C/3, C/2, 1C, 2C, 3C, 4C, 5C}
Batch 4 ( $\mathcal{B}_{4}$ )	{C/3, C/2, 1C}

Table 1: C-rate operating sets for each batch

Figure 1: Joint distribution of negative and positive electrode active material volume fractions across batches.

Figure 2: SOH trajectories for each batch.

Figure 3: Proposed transfer learning framework combining LSTM-based encoder–decoder with MMD-based domain adaptation.

Parameters	Value
Sliding window size	10
Mini-batch size	32
Hidden size	256
Learning rate	10 -3

Table 2: Model and training hyperparameters

Figure 4: Predictive performance under target domain

Model	RMSE [%]	R 2 Score
LSTM (Source)	0.083	0.999
LSTM (Target)	1.637	0.833
LSTM + TL Fine Tuning (Source)	0.084	0.999
LSTM + TL Fine Tuning (Target)	1.591	0.843
LSTM + Proposed TL (Source)	0.085	0.999
LSTM + Proposed TL (Target)	0.781	0.962

Table 3: Comparison of models under testing in source and target domains

Panel of Figure 5 — Figure 5: Conformal prediction for target cells

Lithium-Ion Batteries

Lithium-ion batteries are rechargeable energy-storage systems studied through their materials, performance, aging, safety, and recycling.

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Dynamic Loss Balancing for Joint SOH and RUL Prediction of Lithium-Ion Batteries via a Rotary SOH-Injected Prior Battery Transformer

Shuhao Chen, Tianyu Shi, Yiwen Huang, Chengyi Tu

School of Economics and Management, Zhejiang Sci-Tech University, Hangzhou, 310018, China.

RUL Estimation Predictive Maintenance Multi-Task Learning Adaptive Loss Weighting Lithium-Ion Batteries

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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.

Abstract

Lithium-Ion Batteries

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