Organizations: Infocomm Technology Cluster Singapore Institute of Technology Singapore · School of Science and Technology Singapore University of Social Sciences Singapore · College of Smart Energy Shanghai Jiao Tong University Shanghai, China · Institute of Advanced Intelligence and Computing (IAIC), Agency for Science, Technology and Research (A*STAR), Fusionopolis Way, #16–16 Connexis, Singapore 138632, Republic of Singapore · Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), Fusionopolis Way, Innovis #08-03, Singapore 138634, Republic of Singapore
Battery state of health (SoH) forecasting is important for battery management, but remains challenging due to nonlinear degradation and heterogeneity across batteries. Existing data-driven approaches primarily use temporal models to learn from numerical battery time series, and higher-level degradation characteristics are often not explicitly represented. These characteristics, however, can provide degradation guidance to support reliable forecasting and make the influence of degradation more interpretable. In this paper, we propose \textsc{Sera}, a \underline{se}mantic \underline{r}epresentation \underline{a}ggregation framework that complements temporal modelling with degradation semantics. Guided by battery domain expertise, \textsc{Sera} extracts degradation semantics from time series and constructs two complementary representations using rule-based knowledge and LLM-based interpretation. The representations are independently encoded and integrated with the representation learned by temporal models through gated aggregations. Experiments on the mainstream benchmark across multiple prediction horizons and different temporal models show that \textsc{Sera} consistently improves forecasting performance, achieving up to a 37.3% reduction in prediction error over the temporal baseline and enhanced generalizability. Counterfactual analysis examines how forecasts respond to changes in degradation semantics to assess interpretability. The results show that prediction responses are consistent with the meanings of key degradation descriptors across tested horizons. Together, these findings demonstrate that structured degradation semantics and effective aggregation can improve forecasting accuracy and support reliable and interpretable battery health forecasting for advanced battery management.
Figures & tables
Figure 1 . Overall architecture of Sera . Given a window of battery time series, semantic features are extracted to construct rule-based and LLM-based degradation semantics. Temporal and semantic information is encoded separately and then aggregated for SoH forecasting.
Dominant electrochemical or thermal degradation behaviour inferred from the observed feature patterns.
Severity
mild , moderate , severe
Magnitude of SoH slope, mean SoH level
Strength of the degradation evidence observed within the input window.
Change Point
yes , no
SoH acceleration ratio, SoH acceleration flag
Indication of a possible transition in the observed degradation behaviour.
Key Metric
SoH_slope , Tstd , IRstd , CTslp , QDslp
Selected diagnostic indicator
Most informative feature supporting the current degradation interpretation.
Table 1 . Structured semantic representation schema for battery degradation. Each semantic component is described by its candidate values, supporting source features, and degradation meaning.
H=30
H=50
H=70
H=90
Model
MAE
RMSE
MAE
RMSE
MAE
RMSE
MAE
RMSE
TS
1.556
2.309
2.317
2.942
3.043
3.818
3.565
4.727
Rule only
11.384
12.904
12.064
13.578
10.962
12.174
11.641
13.172
LLM only
12.157
14.225
12.806
14.755
11.526
13.296
12.150
14.515
TS + Rule
0.912
1.369
1.883
2.463
2.350
3.135
2.593
3.349
TS + LLM
1.408
2.036
2.108
2.744
2.522
3.202
2.946
3.869
Table 2 . Comparison of forecasting performance in terms of test MAE and RMSE across different prediction horizons. Lower values indicate better performance, with the best results highlighted in bold. All values are ×10−3 .
Figure 2 . Semantic contribution and generalization analysis across prediction horizons. Fig. 2(a) : reduction in test MAE relative to the TS baseline. Fig. 2(b) : generalization gap, measured as the difference between test and training MAE.
Model
LSTM
TFM
PatchTST
H=30
H=90
H=30
H=90
H=30
H=90
TS
1.394
3.578
1.599
3.421
1.556
3.565
TS + Rule
1.505
3.097
1.375
2.708
0.912
2.593
TS + LLM
1.535
3.336
2.013
3.246
1.408
2.946
Sera (ours)
1.229
3.098
1.350
2.636
1.015
2.234
Table 3 . Comparison of test MAE across temporal encoders at H=30 and H=90 . Lower values are better, and the best results are highlighted in bold. All values are ×10−3 .
Figure 3 . Qualitative comparison of SoH trajectories for representative short- (Fig. 3(a) ) and long-lifespan (Fig. 3(b) ) batteries. The results compare the ground truth trajectories with predictions from the temporal baseline, semantic variants, and models with temporal and semantic representations.
H=30
H=50
H=70
H=90
Semantic Edit
%
ΔˉH
%
ΔˉH
%
ΔˉH
%
ΔˉH
Optimistic Bundle
90.5
+ 5.8
69.0
+ 7.5
86.5
+ 12.9
70.5
+ 11.9
Pessimistic Bundle
100.0
− 71.3
100.0
− 116.8
100.0
− 146.1
100.0
− 251.3
Slope → Mild
75.5
+ 5.6
84.5
+ 7.5
82.0
+ 13.2
71.0
+ 12.1
Slope → Steep
100.0
− 68.9
100.0
− 129.7
100.0
− 142.3
100.0
− 255.1
Trend → Stable
78.5
+ 0.9
93.0
+ 2.2
91.0
+ 4.2
61.0
+ 7.7
Table 4 . Counterfactual analysis of different semantic interventions. Higher semantic consistency (%) indicates that more predictions change in the expected direction after interventions. Positive (negative) ΔˉH values indicate an increase (decrease) in predicted SoH, with larger absolute values indicating stronger effects. ΔˉH values are in units of 10−3 .
Early battery degradation trajectory forecasting (BDTF), which predicts the full-life state-of-health trajectory from early operational data, is critical for battery optimization, manufacturing, and deployment. Battery degradation data exhibit two key characteristics. First, degradation data present a multi-level structure, including regularities shared within aging conditions and trajectory patterns shared across batteries. Second, degradation-related variations in voltage-current profiles are often localized to specific state of charge (SOC) intervals. Existing approaches often fail to explicitly model these characteristics. To bridge this gap, we propose BatteryMFormer, a multi-level Transformer for early BDTF. BatteryMFormer integrates (1) an aging-condition-aware decoder that injects aging-condition priors via aging-condition-informed queries and aging-condition-aware attention, (2) a meta degradation pattern memory that learns and retrieves trajectory prototypes to guide long-horizon forecasting, and (3) a dual-view encoder that jointly captures temporal dynamics and SOC-localized variations from voltage and current time series. Extensive experiments on four battery domains show that BatteryMFormer consistently outperforms state-of-the-art baselines, marking a significant step toward reliable BDTF. Our code is available at https://github.com/Ruifeng-Tan/BatteryMFormer.
Ruifeng Tan, Jintao Dong, Weixiang Hong +3
School of Computer Science and Engineering, Central South University · Material Genome Institute, Shanghai University
Accurate state of health (SOH) estimation is a critical diagnostic service for lithium-ion battery management. However, reliance on labor-intensive manual feature engineering and opaque black-box models hinders scalable industrial deployment. To address this, we introduce TC-SOH: a modular, plug-and-play service architecture for autonomous, end-to-end SOH prediction. TC-SOH employs a temporal-contrastive mechanism and a cross-window prediction pretext task to extract degradation-relevant representations directly from raw operational data. To improve transparency, we connect model efficacy with representation diagnostics: visualization, sensitivity analysis, redundancy analysis, bidirectional probing, future-SOH probing, and temporal shuffling show that learned features overlap with selected expert descriptors while retaining additional SOH-relevant variation, and that ordered temporal context improves subsequent-SOH prediction. Across four public datasets, TC-SOH outperforms the considered physics-informed and data-driven baselines, reducing MAPE by 1.91 times and RMSE by 2.13 times.
Junting Wen, Dan Li, Qihao Quan +8
School of Software Engineering, Sun Yat-sen University, Zhuhai 519082, China · Tianneng Battery Group Co., Ltd, Zhejiang, China · School of Communication Engineering, Hangzhou Dianzi University, China +1
Accurate prediction of lithium-ion battery state of health (SOH) is essential for reliable energy storage operation. However, purely data-driven models may generalize poorly across cycling protocols and produce physically implausible behavior during long-term extrapolation. We developed a physics-informed differentiable degradation modeling framework (PiDDM) for battery SOH prediction. PiDDM incorporates empirical Arrhenius degradation kinetics associated with solid electrolyte interphase growth and loss of lithium inventory into the training objective, encouraging physically consistent capacity fade under diverse operating conditions. The framework was evaluated using a public dataset of 55 batteries cycled under six operating protocols. PiDDM achieved the lowest average prediction error among the evaluated models and substantially reduced mean squared error relative to a multilayer perceptron and a baseline physics-informed neural network. For extrapolation, the models were trained on the first 90% of each battery's cycle life and evaluated on the unseen final 10%. PiDDM captured accelerated end-of-life degradation while avoiding the nonphysical capacity regeneration produced by the baseline models. These results show that incorporating degradation physics into neural network training improves predictive accuracy and physical consistency, providing a promising approach for practical battery health monitoring.
Zeping Chen, Ruda Jian, Sachin Sigdel +3
Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN, USA · Department of Mechanical Engineering, The University of Texas at Dallas, Richardson, TX, USA · Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN, USA +1