1. Introduction
Lithium-ion batteries, due to their high energy density, long service life, and low self-discharge rate, have gained widespread application in electric vehicles, energy storage systems, and other fields [
1]. The optimal operating temperature range for lithium-ion batteries is 20 °C to 40 °C [
2]. If the battery materials continuously remain within abnormal temperature ranges, this can lead to accelerated degradation of the positive and negative electrode materials, electrolyte decomposition, and increased internal resistance, resulting in increased heat generation [
3]. If cooling is inadequate and temperature rise is uncontrolled, it may trigger a thermal runaway process [
4]. With the expansion of the new energy industry, incidents of fires are increasing, and over 90% of these incidents are attributed to thermal runaway of batteries [
5]. As shown in
Figure 1, constructing an accurate lithium-ion battery temperature prediction model can reveal future surface temperature trends. If the residuals between the predicted and actual values exceed a threshold, it indicates potential anomalies in the battery [
6], and the battery’s operating state can be adjusted in advance based on the warning information to effectively prevent thermal runaway [
7].
Currently, battery temperature prediction models can be categorized into two types: physical models [
8] and data-driven models [
9]. Physical models are constructed based on the electrochemical mechanisms or external electrical behavior of batteries, such as electrochemical models and equivalent circuit models (ECMs) [
10]. To ensure the accuracy of these models, it is necessary to consider the coupled relationships among the battery’s structure, size, materials, temperature, and usage scenarios [
11]. Sun et al. [
12] combined a first-order ECM with an Extended Kalman Filter to establish a real-time temperature-estimation model; however, they overlooked the impact of battery temperature on model parameters. The equivalent resistance and capacitance values change with temperature variations, which in turn affect the accuracy of the model. Liu et al. [
13] established a temperature prediction model by combining a two-state thermal model with a second-order ECM; however, the model exhibits instability during current and voltage-step changes, which may lead to divergence in the identification results. In contrast, machine learning algorithms can identify complex nonlinear relationships and latent variables in the data. Data-driven models, which rely entirely on external data and do not require consideration of intricate internal battery structures or parameters, often achieve high precision [
14]. The model has advantages in terms of generality and portability. Álvarez Antón et al. [
15] proposed a support vector machine (SVM) model that can be implemented in microcontroller-based battery management systems (BMSs). Guo et al. [
16] developed an improved back-propagation (BP) neural network using battery current, voltage, and ambient temperature as inputs. This model has a fast computation speed and significantly reduces the state of charge (SOC) prediction error in practical applications. Therefore, data-driven models are very suitable for online prediction in the context of BMS [
17]. Wang et al. [
18] established a temperature prediction model for lithium-ion batteries using a local regression neural network, demonstrating shorter training times and better adaptability and generalization capabilities. Jiang et al. [
19] employed long short-term memory (LSTM) recurrent neural networks and gated recurrent unit (GRU) recurrent neural networks to predict the surface temperature of 18,650 lithium-ion batteries under different environmental temperatures, proposing the use of temperature differences along the time axis as the output of the neural network, which aligns well with electrochemical and thermodynamic principles. Jiang et al. [
20] combined an elitist preservation genetic algorithm with bidirectional LSTM neural networks, using an improved loss function to achieve precise prediction of upper and lower temperature limits.
In previous studies, the training data for models primarily originated from experimental results under periodic operating conditions or single charge–discharge cycles, which made it difficult for the models to accurately capture the nonlinear temperature dynamics exhibited by batteries under complex operating conditions, potentially reducing prediction accuracy. Moreover, most of these models have focused on improving single-step-ahead prediction accuracy, and thus lack a broad prediction range.
This paper utilizes the RW10 dataset from NASA’s Prognostics Center of Excellence [
21], which contains 9000 sets of random usage data for 18,650 lithium-ion batteries. Each set includes three features: temperature, voltage, and current. In practical applications, these data can be obtained through corresponding sensors. On this basis, EMD was introduced as a method for processing nonlinear and nonstationary signals. EMD decomposes the temperature data into a series of IMFs. Pearson correlation coefficients (PCCs) were then used to measure the strength of correlation between continuous variables. IMFs with low correlation represent noise or high-frequency disturbances in the temperature data; IMFs with medium correlation characterize the mid-frequency features of the temperature data, and IMFs with high correlation represent the long-term trends or dominant periodicities in the temperature data. These components were combined with voltage and current data and input into sub-models. Ultimately, the model captures the long-term dependencies among temperature, voltage, and current, achieving accurate temperature prediction under complex operating conditions. Additionally, this study employed the Informer framework, which implements a probabilistic sparse self-attention mechanism by focusing on the most informative query vectors. This approach reduces computational complexity while achieving high-precision predictions. Based on this framework, single-step and multi-step predictions of the surface temperature of lithium-ion batteries were conducted, thereby enhancing the range of temperature predictions under complex operating conditions.