Lithium-Ion Battery Time-Series Analysis

Updated 25 December 2025

Lithium-ion battery operational time-series are multi-channel, high-resolution data capturing voltage, current, temperature, and state-of-charge during cycling.
Data acquisition involves preprocessing steps such as outlier removal, imputation, and cycle synchronization to extract reliable performance metrics.
Advanced modeling techniques, including ensembles, deep neural networks, and Gaussian processes, enable precise battery health monitoring and fault detection.

Lithium-ion battery operational time-series refer to multi-channel, temporally resolved measurements that track the physical and chemical state of Li-ion cells (or packs) under operational cycling. These time-series constitute the primary data source for prognosis, health estimation, lifetime forecasting, and control algorithms in battery management systems. The acquisition, transformation, and modeling of these sequences underpin both traditional and state-of-the-art prediction techniques for battery longevity, safety, and fault detection.

1. Data Acquisition, Measurement Channels, and Preprocessing

Operational time-series for lithium-ion batteries are predominantly obtained during controlled cycling protocols or field deployments. Standard measurements per cycle or per timestamp include:

Voltage $V(t)$ : Monitored at the cell or sub-pack level.
Current $I(t)$ : Charge/discharge current imposed on the cell or pack.
Temperature $T(t)$ : Either internal (embedded sensors) or on-surface measures.
Capacity $Q(t)$ : Typically cumulative charge transferred during a cycle.
State-of-Charge (SOC): Often estimated by the BMS, based on coulomb counting or model-based observers.
Internal resistance $R(t)$ : Extracted via specialized pulses or inferred indirectly.

High-frequency data (1 Hz to kHz) are typical for laboratory cycling; field deployments can yield data volumes upward of $10^8$ points across hundreds of cells (Schaeffer et al., 2024). Preprocessing steps generally include:

Outlier removal, data gap imputation, event segmentation (charge/discharge), and normalization (zero mean/unit variance or min–max scaling), as in (Hilal et al., 2023, Shanxuan et al., 25 Apr 2025).
Time-alignment either by cycle number or by regular intervals, with cycle-level aggregation for downstream analysis.
For uneven cycle lengths, methods such as Dynamic Time Warping (DTW) enable cycle synchronization to fixed-length sequences, preserving degradation information (Zhou et al., 2021).

2. Feature Engineering and Representation

Raw operational sequences are distilled into engineered features prior to modeling. Typical strategies fall into several categories:

Handcrafted Statistical Descriptors: Moments (variance, skewness, kurtosis) of discharge capacity, slopes/intercepts of linear fits across cycles, extrema and delta features (e.g., minimum/maximum differences), and resistance change metrics (Hilal et al., 2023, Kim et al., 20 Jan 2025).
Domain-Informed Features: Differential discharge capacity ( $Q_\text{dlin}$ ), CC charging time, or integrated resistance proxies, identified as aging indicators by SHAP analysis (Shanxuan et al., 25 Apr 2025).
Time-Series Embeddings: Use of LSTM or CNN to embed each cycle’s high-dimensional voltage/current/temperature trajectory into low-dimensional summary vectors (Liu et al., 12 Oct 2025).
Signal Decomposition: Empirical Mode Decomposition (EMD), Hilbert transforms for trend extraction, or DLinear’s trend–seasonal splitting (Huotari et al., 2021, Kim et al., 20 Jan 2025).
Path Signature Extraction: Basis of survival analysis workflows, constructing higher-order iterated integrals from voltage–time curves (Xue et al., 17 Mar 2025).

Feature selection is typically accomplished via Pearson correlation, recursive filter/wrapper methods, or Shapley value attribution (SHAP), the latter robustly identifying variance in discharge voltage and temperature, and charging duration as features most correlated with aging across datasets (Kim et al., 20 Jan 2025, Shanxuan et al., 25 Apr 2025).

3. Modeling Approaches: Traditional ML, Deep Learning, Gaussian Processes, and Survival Analysis

The operational time-series form the substrate for diverse supervised and unsupervised ML workflows. The main modeling paradigms include:

Tree-based Ensembles: Random Forest, Gradient Boosting, XGBoost consistently achieve sub-10% MAPE on limited datasets with well-chosen handcrafted features (Hilal et al., 2023, Huotari et al., 2021).
Feedforward and Linear Decomposition Models: DLinear leverages trend/seasonality splitting and achieves $R^2 = 0.98$ on NASA datasets, outperforming LSTM and Transformers in scenarios with strong trend-dominated degradation (Kim et al., 20 Jan 2025).
Recurrent Neural Networks (RNNs, LSTM, GRU): RNNs model sequential patterns but often underperform on small datasets where gradual degradation dominates and “medium-range” dependencies are less informative (Hilal et al., 2023).
Hybrid Deep Architectures: Integrating CNNs (local shape extraction), A-LSTM (temporal attention), and ODE-LSTM (continuous hidden-state evolution) enables state-of-the-art RMSE $\sim$ 100 on large benchmarks (Tran et al., 22 May 2025).
Semiparametric Deep Gaussian Processes: SDG-L merges exponential trend fitting with LSTM-encoded residual modeling, yielding sub- $1\%$ test MSE and well-calibrated uncertainty on NASA cycles (Liu et al., 12 Oct 2025).
Survival Analysis and Path-Signature ML: Path signatures of voltage–time curves are fed into CoxPH, CoxTime, and DeepHit, permitting probabilistic estimations of RUL and robust C-index/AUC scoring on censored life data (Xue et al., 17 Mar 2025).
GAN-based Data Augmentation: Conditional RCGANs generate synthetic cycle profiles matched to capacity states, augmenting small datasets, and reducing regression RMSE/MAE for RNN-based downstream predictors by $50\%$ – $88\%$ (Chowdhury et al., 15 Mar 2025).

4. Comparative Benchmarking and Large-Scale Evaluation

Extensive benchmarking is reported across single-lab and large-scale multi-source datasets. Key benchmarks for lithium-ion operational time-series include:

Approach	Key Metric(s)	Dataset / Scale	Notable Results	Source
Random Forest	MAPE $\sim$ 9.8%	Severson (124 cells)	Low error on RUL (handcrafted)	(Hilal et al., 2023)
Hybrid CNN+A-LSTM+ODE-LSTM	MAPE=6.95%, RMSE=101.59	Severson, Ma (2019/2022)	Outperforms XGBoost, ElasticNet	(Tran et al., 22 May 2025)
Stack Ensemble (Ridge, LSTM, XGBoost)	$R^2=0.984$ , RMSE=0.0092	Multi-source (NASA, MIT, CALCE, NCA)	83.2% RMSE reduction v. DNN	(Shanxuan et al., 25 Apr 2025)
SDG-L (LSTM+DGPR)	Test MSE=0.0012	NASA (4 cells/168 cyc.)	Outperforms tree, GPR, MLP, CNN	(Liu et al., 12 Oct 2025)
CyclePatch MLP/GRU	MAPE=0.18, 15%-acc=0.62	BatteryLife (>800 cells, 59 chem.)	Outperforms vanilla/transformer/etc	(Tan et al., 26 Feb 2025)
Gaussian Process (GP–ICE)	RMSE $\approx$ 2–3%	Oxford, NASA (8/20 cells)	Accurate $Q$ estimation from short windows	(Richardson et al., 2017)

Models tailored for non-battery time-series domains (e.g., DLinear, Autoformer) show inferior transfer performance, primarily due to a lack of fine-grained feature extraction and inability to model cycle-wise voltage–current–capacity couplings (Tan et al., 26 Feb 2025, Kim et al., 20 Jan 2025).

5. Fault Detection, Health Monitoring, and Uncertainty Quantification

Recent work demonstrates that recursive spatio-temporal Gaussian processes can handle massive field datasets (%%%%16 $50\%$ 17%%%% points per cell). By explicitly separating resistance evolution into operating-point and time-dependent components, GP-based models enable on-line health monitoring and early anomaly (“knee-point”) detection (Schaeffer et al., 2024). Probabilistic thresholds of fault ( $p_\text{fault}>0.5$ ) at the cell level anticipate weakest-link failure in series packs, and transitions in posterior variance signal incipient degradation.

Semiparametric approaches (SDG-L) and survival frameworks (MTLR, CoxTime, DeepHit) provide calibrated uncertainty measures crucial for preventive maintenance scheduling, quantifying RUL as an event probability over time (Liu et al., 12 Oct 2025, Xue et al., 17 Mar 2025).

6. Best Practices, Model Selection, and Practical Considerations

Selection of modeling strategy should reflect dataset scale, signal channel diversity, cycle alignment, and interpretability requirements:

For datasets with $<200$ cells or $<10^4$ time-points, tree ensembles on handcrafted features remain the optimal choice; always benchmark against Random Forest (Hilal et al., 2023, Kim et al., 20 Jan 2025).
For large, heterogeneous datasets, stacked ensembles and attention-based deep pipelines consistently outperform single-model or off-the-shelf architectures, especially when CyclePatch intra/inter-cycle encoding is used (Tan et al., 26 Feb 2025, Shanxuan et al., 25 Apr 2025).
Feature engineering based on domain signal (slopes, moments, variance) and SHAP-based selection enhances both accuracy and interpretability (Kim et al., 20 Jan 2025, Shanxuan et al., 25 Apr 2025).
Synchronization (DTW) and uniform cycle tokenization are required to preserve information and prevent accuracy degradation from truncation/padding (Zhou et al., 2021, Tan et al., 26 Feb 2025).
Data augmentation using generative models (RCGAN) mitigates the small-sample limitation, particularly beneficial for deep learning regressors (Chowdhury et al., 15 Mar 2025).
Online and field-scale deployments benefit from recursive GP filtering and minimal-latency probabilistic diagnostics, supporting rapid anomaly localization (Schaeffer et al., 2024).

7. Outlook and Research Directions

Future work includes transfer learning and block-freezing strategies for cross-dataset generalization (Tran et al., 22 May 2025); domain-aware layers for chemistry/protocol adaptation (Tan et al., 26 Feb 2025); the fusion of sequence- and physics-informed modeling (e.g., ODE-LSTM, physics-embedded neural nets (Tran et al., 22 May 2025)); and universal benchmarks across both industry-scale and lab-scale datasets to provide reproducible, standardized metrics of forecasting real-world Li-ion battery degradation.

A plausible implication is that accurate lithium-ion battery operational time-series modeling will increasingly rely on (a) large, well-curated, and cycle-synchronized datasets, (b) hybrid deep–ensemble architectures with explainable feature selection, and (c) probabilistic outputs for risk-aware BMS and predictive maintenance (Shanxuan et al., 25 Apr 2025, Liu et al., 12 Oct 2025, Schaeffer et al., 2024).