1. Introduction
Due to their wide operating temperature range, convenient charging capabilities, low self-discharge rate, and environmental friendliness, Lithium-ion batteries (LIBs) have been widely adopted in electric vehicles (EVs) and distributed energy storage systems. This transition effectively reduces carbon emissions and fossil energy consumption. The internal structure of a battery changes as a result of an increasing number of usage cycles and improper charging and discharging practices. The presence of internal resistance, capacity degradation, voltage fluctuations, and other effects can negatively impact the device’s performance and safety. The state of health (SOH) of a battery represents the performance of the active materials and electrolytes within the battery. Variations in this parameter can indicate changes in the internal health state of a battery [
1]. Battery performance declines rapidly if the capacity drops below 80% of its initial capacity [
2], resulting in power devices being unable to operate or becoming permanently damaged. In certain applications, the battery may be deemed unusable when its SOH reaches 70–80% of its rated capacity. Therefore, an accurate assessment of battery health status is required to ensure that battery management systems (BMSs) operate normally, preventing accidents. Moreover, evaluating the health status of retired batteries can facilitate their reuse in secondary applications, extend their life cycle, and minimize resource waste [
3].
Various methods have been suggested for assessing the degree of battery degradation and aging based on the SOH of a battery. Research on predicting lithium-ion batteries’ health status has incorporated a number of innovative methods and techniques. Advances in technology have improved the accuracy of predicting LIB health status, which is beneficial to the long-term maintenance and safe operation of these batteries. SOH estimation methods are currently divided primarily into two categories: model-based estimation and data-driven estimation [
4]. The model-based estimation method constructs a model that describes the aging behavior of LIBs through a series of algebraic and differential equations. Then, it is used to identify the battery aging parameters using filtering algorithms and their derived algorithms to achieve SOH estimation. According to different model mechanisms, the model-based methods can be divided into electrochemical models and equivalent circuit models (ECMs) [
5]. The electrochemical model derives mathematical formulas, such as differential or partial differential equations, that characterize the battery performance decay mechanism by analyzing the lithium-ion battery’s internal composition and working principle. The electrochemical model has high accuracy, but complexity needs to be lowered in actual SOH estimation [
6]. The ECM ignores the above physical and chemical reactions and approximates the aging behavior of the battery by using existing circuit components such as resistors and capacitors. Analyzing experimental data achieves accurate model-related parameter identification. However, due to the fixed structure of the ECM, it is challenging to achieve high-precision SOH estimation for the entire life cycle of the battery [
7].
One major limitation of model-based methods is that if the model selected does not accurately reflect the physical and chemical processes of the battery, the SOH estimation results will be inaccurate. Furthermore, capturing all potential battery degradation mechanisms and reflecting them in model parameters is challenging. Unlike model-based SOH estimation methods, data-driven estimation methods do not require a deep understanding of the complex degradation mechanism inside the battery. Data-driven methods can automatically establish the mapping relationship between the battery-aging features contained in a large amount of battery charging and discharging data and SOH. Data-driven methods are easy to operate and have high estimation accuracy and generalization ability. Support vector regression (SVR) [
8], Gaussian process regression (GPR) [
9], neural networks (NNs) [
10], and Ensemble learning [
11] are very powerful and have been widely applied. Li et al. [
12] directly used the original voltage data of the constant current stage charging curve as input to the LSTM-RNN network. The network shows excellent anti-interference and estimates SOH ability when facing input noise. Tagade et al. [
13] used local charge and discharge time series data (voltage, current, and temperature) based on deep GPR for capacity estimation without extracting additional input features to achieve high prediction accuracy. However, the features automatically extracted by these methods often need a more precise physical meaning.
Nowadays, feature-processing methods are widely used because they simplify and reduce computation time by extracting health features (HFs). Feature processing refers to the extraction of trends from collected data, such as voltage, current, and temperature, from batteries. The purpose of this analysis is to characterize the aging performance of LIBs. An explanation-boosting machine was used by Lin et al. [
5] to estimate the SOH, which improved accuracy and robustness when the battery was near its end of life due to the battery’s internal resistance. In the work by Wang et al. [
14], 12 HFs were extracted from the incremental capacity curve and input to a structurally weighted twin SVR model in order to assess SOH accurately. Feng et al. [
15] utilized an improved GPR method to estimate SOH and remaining life based on 5 HFs calculated from the charging current curve of the battery. Zhang et al. [
16] extracted 25 statistical characteristics of feature histograms from the original data collected under different operating conditions and used them as features to learn battery aging. Roman et al. [
17] proposed a SOH estimation method based on machine learning to extract 30 features from the battery charging curve. Thermal runaway presents a significant risk to lithium battery operation; thus, temperature is strongly correlated with the SOH. Based on the accelerated durability test with idle and load-changing cycles and the degradation characteristics test, Tang et al. [
18] established a mathematical model that reveals the relationship among load current, SOH, and optimal temperature reference. With the assistance of domain knowledge, enhanced features can be constructed using original data to extract more practical information, which can be used to describe battery aging more effectively. Then, statistical analysis, correlation analysis, and other methods can be utilized to identify the most representative and predictive features, which will enhance the model’s accuracy and generalization capability.
The degradation of LIBs typically manifests as a decline in battery capacity and partial recovery with increasing charge-discharge cycles, which may be referred to as a time series problem. SOH has been successfully diagnosed using recurrent neural networks (RNNs), which are particularly suited to processing sequential data [
19]. The problem of vanishing or exploding gradients makes it difficult for RNNs to capture long-term dependencies. LSTM is an improved variant of the standard RNN, which effectively captures the long-term trend of decreasing battery capacity over time [
20]. In the design and training of LSTM models, the number of neurons is a crucial factor that influences the model’s complexity and fit. The predictive power of LSTM cannot be fully exploited through empirical parameter selection. The model may be unable to capture complex patterns in the data if its neurons are too small, resulting in insufficient fitting ability. Alternatively, overfitting may occur when there are too many neurons, which will result in poorer predictive ability. Designing and training LSTM models requires balancing model complexity and predictive accuracy with the appropriate number of neurons. Qian et al. [
21] proposed a method to estimate battery capacity using a one-dimensional convolutional neural network (CNN). The neural network hyperparameters were optimized by using a linear decreasing weighted particle swarm optimization algorithm, resulting in accurate battery capacity estimation. Li et al. [
22] proposed a method for SOH estimation that uses the improved ant lion optimization (IALO) algorithm to optimize the kernel parameters of SVR. This method has been demonstrated to be highly accurate and robust in experimental studies. With random battery data length inputs, Zhang [
23] proposed a method for estimating SOH that optimizes back propagation (BP) of neural networks using genetic algorithms (GAs). Harris hawk optimization (HHO) is widely used due to its simple structure, fast convergence, and strong local search performance. Jafari [
24] proposed an innovative method combining HHO to optimize random forest and LightGBM hyperparameters, accurately estimating the battery’s remaining useful life (RUL). Gadekallu [
25] proposed a hybrid HHO-CNN model for gesture image classification. HHO is used to adjust CNN hyperparameters, and the proposed model achieved 100% accuracy.
Battery health prediction is challenging due to nonlinearity, wide operating conditions, and different aging processes. Furthermore, deep learning requires an extensive amount of training data, limiting the application space for LIB SOH estimation. Using knowledge derived from different but related source domains could improve the performance of data-driven methods in the target domain [
26]. Deng et al. [
27] used early aging data of batteries to achieve aging pattern recognition and transfer learning (TL), effectively improving SOH estimation accuracy. Yao et al. [
28] used the capacity increment features of partial charge/discharge data and adopted a deep transfer convolutional neural network to improve battery capacity estimation accuracy. Che et al. [
29] developed an innovative method for predicting RUL based on gated RNNs and TL. TL can leverage data from the source domain to reduce the model’s demand for data from the target domain.
Although deep learning and optimization algorithms have achieved certain results in battery health prediction, they still have some limitations. Deep learning requires a large amount of training data, which may be difficult to obtain in practical applications. Optimization algorithms can adjust the hyperparameters of the model to improve prediction accuracy, but these methods often require a large amount of computational resources and may lead to model overfitting. In order to address these challenges, this paper proposes a new method that utilizes features extracted from the voltage, current, and temperature profiles of LIBs during charging. These features not only contain the physical information of the battery but can also be obtained from actual charging data. They serve as inputs for the base model LSTM-FC and allow the model to be transferred to different application scenarios. During the transfer, a fine-tuning strategy is adopted without modifying the model structure. In order to ensure the precision and generalizability of both the base and transfer models, we employ the HHO algorithm for the global optimization of the hyperparameters in our proposed model. This method can not only reduce the demand for data in the target domain but also improve the accuracy and generalization ability of SOH estimation. According to the experimental results, the proposed method can enhance estimation accuracy while maintaining high stability.
While specific experiments on retired batteries have not been conducted yet, our method is highly suitable for their assessment. By utilizing transfer learning, our approach accurately estimates the SOH of batteries even with limited sample data. This capability has substantial potential for evaluating the health status of retired batteries, thereby enabling their reuse in secondary applications. Such practices contribute to a circular economy and sustainable development by optimizing resource utilization and minimizing environmental impact.
The remainder of this paper is organized as follows:
Section 2 presents the extraction of degradation features based on the charging curve.
Section 3 describes the LSTM-FC base model, the HHO optimization algorithm, and the proposed SOH prediction method.
Section 4 introduces the experimental results and analysis.
Section 5 provides the conclusion.