With the global transition toward cleaner energy and the rapid advancement of electrification technologies, lithium-ion batteries have emerged as essential energy storage components in electric vehicles, renewable energy storage systems, and portable electronic devices (Wang et al., 2023). Owing to their high energy density, long cycle life, and low self-discharge rate, lithium-ion batteries have become increasingly important. However, their performance inevitably degrades over time due to repeated charge-discharge cycles, leading to capacity fade and a shortened remaining useful life (RUL) (Wang et al., 2021). Accurate prediction of battery state of health (SOH) and RUL is crucial for optimizing battery management, extending service life, reducing maintenance costs, and ensuring system safety (Elmahallawy et al., 2022; Yao et al., 2021). In particular, battery failure in electric vehicles or large-scale energy storage systems can result in significant safety hazards and economic losses, making the development of high-accuracy RUL prediction methods a pressing research focus.

Precise RUL prediction plays a vital role in optimizing battery management systems. First, it provides a scientific basis for battery replacement and maintenance decisions, thereby lowering operational costs (Tong et al., 2021). Second, it enables early identification of potential failures, thus enhancing system safety (Yang et al., 2023). Moreover, accurate RUL estimates support battery recycling and second-life applications, contributing to sustainable resource utilization. Nevertheless, several challenges hinder effective RUL prediction. The degradation process is influenced by multiple factors such as temperature, charge/discharge rates, and usage scenarios, exhibiting high nonlinearity and complexity (Sharma and Bora, 2022). Real-world operational data often contain noise and missing values, increasing the difficulty of modeling (Li et al., 2023). Furthermore, long-term prediction requires models that can simultaneously capture short-term fluctuations and long-term trends, which is difficult for single-model architectures to achieve.

Traditional RUL prediction approaches can be categorized into physics-based and data-driven methods. Physics-based models rely on the electrochemical mechanisms of batteries (Liu et al., 2022), using complex mathematical formulations to describe the degradation process. However, these methods require detailed knowledge of material properties and operating conditions, involve high computational complexity, and often lack generalizability across different battery types. In contrast, data-driven approaches have gained popularity by learning patterns directly from operational data (Ji et al., 2024). With advances in sensor technology and data acquisition capabilities, these methods can effectively model battery behavior using features such as voltage, current, and temperature, showing improved adaptability and predictive accuracy.

Data-driven RUL prediction techniques generally fall into three categories: statistical models (Crawford et al., 2021), machine learning methods (Zhang L. et al., 2022), and deep learning models (Zhang D. et al., 2022). Statistical approaches, such as Kalman filtering and particle filtering, model battery degradation probabilistically. For example, Nunes et al. (2023) proposed an online RUL estimation method for second-life lithium-ion batteries based on an unscented Kalman filter and degradation curve modeling, validated on six different second-life battery datasets. Despite some success—achieving a worst-case mean absolute percentage error (MAPE) of 5.279% and an R² score of 0.726—statistical models often struggle with nonlinear or complex degradation behaviors. Machine learning approaches such as support vector machines, random forests, and XGBoost have demonstrated promising results in RUL prediction through hand-crafted features. Jafari and Byun (2022) introduced a hybrid RUL prediction method based on particle filtering and Kalman filtering, where XGBoost was used as the observation model due to its strong nonlinear fitting capabilities. Despite high predictive accuracy based on full-cycle test data, such methods are heavily dependent on the quality of feature engineering and may suffer from overfitting or inefficiency when applied to high-dimensional time-series data.

The emergence of deep learning has opened new avenues for RUL prediction. Long Short-Term Memory (LSTM) networks, known for their capability in modeling temporal dependencies, have been widely adopted for battery degradation modeling. LSTM networks utilize gating mechanisms to effectively capture long-term dependencies, making them well-suited for modeling the nonlinear degradation process of batteries. Reza et al. (2024) proposed an improved method combining LSTM with the Gravitational Search Algorithm (GSA), using data cleaning to remove noise, replacing anomalies with highly correlated data, and applying normalization. GSA was employed to optimize the LSTM hyperparameters to address key challenges in battery life prediction. However, LSTM models may still face limitations such as vanishing gradients and computational inefficiencies when dealing with long sequences. Recently, Transformer models have demonstrated excellent performance in natural language processing and time-series analysis tasks due to their strong capability in extracting global features (Chen et al., 2022; Han et al., 2023). The Transformer architecture processes sequences in parallel through multi-head attention mechanisms, effectively capturing long-range dependencies. Nevertheless, its application in battery RUL prediction remains relatively unexplored.

Given the above challenges, accurate RUL prediction remains difficult due to the following key factors,

1. The degradation process is highly nonlinear and influenced by external factors such as temperature, cycling rate, and usage scenarios;

To address these challenges, this study develops a hybrid deep learning model that captures both local temporal dependencies and global contextual features. As illustrated in Figure 1, we propose a novel LSTM-Transformer hybrid model based on the MIT battery dataset to enhance prediction accuracy and robustness. The LSTM module extracts local temporal dynamics from time-series inputs, while the Transformer module, with its attention mechanism, captures global feature dependencies. The integration of both allows the model to effectively represent complex degradation patterns.

The main contributions of this work are as follows,

1. A novel hybrid deep learning architecture combining local temporal modeling and global attention mechanisms is proposed for lithium-ion battery RUL prediction.

The remainder of this paper is organized as follows: Section 2 introduces the data preprocessing and feature engineering methods; Section 3 details the architecture and training process of the LSTM-Transformer hybrid model; Section 4 presents experimental results and performance evaluation; Section 5 concludes the paper with a summary of findings and future work directions. Through this research, we aim to provide an efficient and accurate solution for lithium-ion battery RUL prediction, offering theoretical and technical support for optimized battery management systems.

Accurate prediction of the remaining useful life of lithium-ion batteries critically depends on high-quality data preprocessing and feature engineering. To construct a time-series modeling–ready prediction dataset, this study systematically performs data cleaning, temperature feature extraction, normalization, target variable transformation, and sample construction based on a sliding window. By incrementally sliding a fixed-length window over the time-series data to extract local segments, representative features or sequential samples are generated. This approach facilitates the capture of local temporal dependencies and dynamic variations (Lin et al., 2024), thereby ensuring that the input data fed into the model possesses strong representativeness and consistency.

The original dataset used in this work is the MIT Battery Dataset, which includes operational data from multiple batteries under various charging and discharging conditions. Key variables include voltage, current, and temperature. Let the raw feature matrix be denoted as x ∈ R n × d , where n represents the number of samples and d indicates the number of feature dimensions. The target variable is denoted as y ∈ R n .

To explore the linear relationships between different features, we construct a Pearson correlation-based heatmap, as illustrated in Figure 2a. In this heatmap, red indicates strong positive correlations, while blue denotes strong negative correlations. The heatmap reveals significant correlations among several voltage-related, capacity-related, and temperature-related features. Based on this analysis, redundant features are removed to improve the generalization ability of the model and to mitigate issues related to multicollinearity.

To comprehensively evaluate the structure of the feature space, Principal Component Analysis was applied to the high-dimensional feature data after redundancy removal, as shown in Figure 2b. The first two principal components were visualized, with a color gradient representing the corresponding battery RUL values. This visualization enables the observation of degradation trends within the feature space. The results indicate that samples with different RUL levels exhibit clear clustering patterns in the two-dimensional PCA space, demonstrating the discriminative capability of the extracted features in characterizing battery degradation states.

Temperature, as a critical factor influencing lithium-ion battery aging and performance degradation, plays a key role in modeling degradation behavior. Analysis reveals that the raw temperature features (T1-0 to T3-3) exhibit significant dynamic fluctuations during battery operation and are highly correlated with changes in the RUL curve.

To this end, three types of temperature-derived features are designed: temperature mean, temperature range, and temperature fluctuation. The temperature mean reflects the overall thermal load level during battery operation. Elevated operating temperatures accelerate electrolyte decomposition, solid electrolyte interphase (SEI) layer growth, and structural degradation of electrode materials, which are key contributors to capacity fade and internal resistance increase. The temperature range measures the amplitude of temperature variation within each time window, indicating the degree of thermal stress fluctuation. Frequent and intense thermal stress cycles may induce mechanical fatigue or even cracking of electrode particles, exacerbating material degradation and performance decline. Temperature fluctuation, quantified by the standard deviation, characterizes the local instability of temperature over time, typically associated with abnormal conditions such as high-current charge/discharge events and cooling system failures. These factors are prone to cause localized hotspots and accelerate undesirable electrochemical side reactions, ultimately shortening battery life.

For each temperature sensor group, the temperature mean is defined as shown in Equation 1, T i m e a n = 1 m ∑ j = 0 m − 1 T i , j (1)

In this context, T i , j denotes the j t h temperature channel within the i t h group, where m = 4 represents the number of channels in each group. The corresponding temperature difference range is defined as shown in Equation 2, T i r a n g e = max 0 ≤ j < m T i , j − min 0 ≤ j < m T i , j (2)

The above two features respectively characterize the central tendency and extreme dispersion of each temperature group, which can reflect phenomena such as localized overheating or abnormal heat dissipation. In addition, to capture the overall fluctuation level of the thermal behavior, the standard deviation across all temperature channels is calculated as shown in Equation 3 and used as an indicator of temperature volatility. σ t e m p = 1 k ∑ k = 1 K T k − T ¯ 2 (3)

Let K = 12 denote the total number of temperature channels, T k represent the value of the k t h temperature channel, and T ¯ be the mean temperature across all channels. These three temperature-derived features not only enhance the semantic expressiveness of the data but also provide physically consistent inputs aligned with the underlying battery aging mechanisms. To eliminate dimensional discrepancies among features and to improve training stability, as shown in Equation 4, Z-score normalization is applied to the feature matrix, ensuring that each feature has zero mean and unit variance before being fed into the model. x s c a l e d = X − μ σ (4)

Let μ and σ denote the mean and standard deviation of each feature column, respectively, and x s c a l e d represent the normalized feature matrix. This normalization ensures that all features follow an approximately zero-mean and unit-variance distribution, which facilitates faster convergence of gradient descent during model training and enhances generalization performance. Meanwhile, the Yeo-Johnson transformation (Bao-Hua et al., 2024) is applied to the target variable for nonlinear processing. This parameterized transformation adjusts the data distribution to approximate a normal distribution, thereby enhancing the stability and accuracy of subsequent model training.

The transformation is defined as shown in Equation 5, y t r a n s = y + 1 λ − 1 λ , y ≥ 0 , λ ≠ 0 ln y + 1 , y ≥ 0 , λ = 0 − − y + 1 2 − λ − 1 2 − λ , y < 0 , λ ≠ 2 − ln − y + 1 , y < 0 , λ = 2 (5)

Here, λ denotes the transformation parameter, which is automatically estimated using the maximum likelihood method. The transformed variable y t r a n s exhibits a more symmetric distribution, which is beneficial for subsequent model convergence and stable error control.

To accommodate the requirements of deep learning-based time series modeling, the dataset is reconstructed into a sliding window format. As illustrated in Figure 3, let the input window length be w i n = 30 and the prediction horizon be w o u t = 7 . For the normalized feature matrix x s c a l e d ∈ R n × d and the transformed target variable y t r a n s ∈ R n , as shown in Equation 6, each training sample is constructed from the following subsequences: x t = X s c a l e d t : t + w i n ∈ R w i n × d y t = y t r a n s t + w i n : t + w i n + w o u t ∈ R w o u t (6)

The total number of samples that can be constructed from the dataset is given by Equation 7, N = n − w i n − w o u t + 1 (7)

The final dataset was split into training and testing sets at a ratio of 8:2, with the training set randomly shuffled to enhance sample diversity and training robustness. The data preprocessing pipeline significantly improved the semantic representation and structural compatibility of the input data. The design of temperature-derived features was closely aligned with the underlying battery physical mechanisms. Standardization and nonlinear transformation ensured numerical stability during model training, while the sliding window data construction effectively captured the dynamic evolution of battery degradation. Together, these steps laid a solid foundation for subsequent battery life prediction modeling based on the LSTM-Transformer architecture.

To achieve high-precision regression prediction of the remaining useful life of lithium-ion batteries, this study designs a deep neural network model that integrates Long Short-Term Memory networks with the Transformer architecture. The model leverages the strength of LSTM in capturing local temporal dynamics in time series data and the powerful capability of Transformer in modeling global dependencies, thereby enhancing the ability to characterize the evolving performance trends of batteries. Time series features are fed into two parallel subnetworks for separate encoding, followed by feature-level fusion to ultimately predict the battery life over multiple future time steps.

A comprehensive evaluation was conducted to assess the predictive performance of the proposed LSTM-Transformer hybrid model on the MIT battery dataset, demonstrating its effectiveness and superiority in the task of remaining useful life prediction for lithium-ion batteries. Model training and testing were performed on the publicly available MIT battery degradation dataset. After undergoing data preprocessing and temporal windowing, the dataset was restructured into time series samples with an input sequence length of 30 and an output prediction horizon of 7 steps. The objective was to forecast the battery capacity degradation trend over the next 7 cycles. During training, the Adam optimizer was employed with an initial learning rate set to 0.001, and an Early Stopping mechanism was integrated to prevent overfitting. Architecturally, the LSTM layer captures local temporal dynamics, while the Transformer module exploits its global attention mechanism to model long-term dependencies. The synergistic integration of both enhances the model’s capacity to capture the complex degradation behaviors of batteries.

With the widespread adoption of electrification and intelligent systems in transportation, energy storage, and industrial control, health management and remaining useful life prediction of lithium-ion batteries have become critical tasks to ensure system safety and operational efficiency. Addressing key challenges such as the difficulty of RUL prediction under nonlinear and complex degradation mechanisms during battery operation, this work constructs a hybrid deep learning model that integrates Long Short-Term Memory networks with Transformer architecture based on the publicly available MIT battery dataset. The model aims to enhance prediction accuracy, stability, and generalization capability.

This study centers on the theme of “high-dimensional sequence modeling and deep fusion prediction.” First, in data preprocessing and feature engineering, raw sensor data including battery temperature and multi-channel voltages were systematically processed. Feature dimensionality reduction, principal component analysis, and physics-informed temperature-derived feature design were conducted to construct interpretable input variables such as temperature mean, temperature difference range, and temperature fluctuation. Meanwhile, unified data normalization techniques, including Z-score standardization and Yeo-Johnson transformation, were applied to improve the model’s capability to handle multi-scale and heterogeneous distributions. Furthermore, to align with sequence prediction tasks, a sliding window method was employed to reconstruct time series samples, effectively embedding local temporal dynamics.

Second, at the model design level, this work proposes a fusion modeling approach combining LSTM and Transformer structures. The LSTM module leverages gating mechanisms to accurately capture short-term fluctuations and long-term dependencies, which is well-suited for modeling dynamic sequences exhibiting continuity and staged features during battery degradation. The Transformer module employs multi-head attention and positional encoding to model global dependencies across the entire input sequence, enhancing expressiveness under long-sequence conditions. By concatenating and fusing the feature vectors from both modules, a multimodal prediction model capable of simultaneously capturing local temporal dynamics and global structural variations was constructed.

In terms of training optimization and performance evaluation, a training framework based on mean squared error loss and the Adam optimizer was established, achieving stable convergence after 200 iterations. Evaluation on the test set demonstrated that the proposed LSTM-Transformer model attained an RMSE of 0.0085, MAPE of 0.0200, and R² of 0.9902, significantly outperforming conventional single deep learning models. Residual distribution analysis and visualization of prediction results further validated the model’s strong ability to capture battery degradation trends, robust performance, and lack of systematic bias, indicating substantial potential for engineering applications.

However, the proposed method still faces certain limitations in practical deployment, such as its reliance on high-quality sensor data and insufficient transferability across different usage scenarios. Future research will focus on enhancing the model’s adaptability to multi-source data and exploring strategies that integrate online learning with few-shot learning to improve its practicality and robustness.

In summary, the proposed LSTM-Transformer fusion prediction model exhibits high accuracy and stability in battery RUL forecasting. It provides effective technical support for the development of next-generation intelligent battery management systems, with promising prospects for practical engineering deployment and broader adoption.