Early prediction of lithium-ion battery cycle life based on voltage-capacity discharge curves

Wei, Xiong; Xu, Gang; Li, Yumei; Zhang, Feng; Ye, Peng; Li, Ben

doi:10.1016/j.est.2023.106790

Cited by 27 publications

(7 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, in order to address the problem of long SVR training time, Hu et al [100] used a two-step search to improve the training efficiency of SVR, avoiding the behavior of blindly searching for parameters over a wide range and improving the accuracy and robustness of battery SOC estimation when using SVR under more complex working conditions. Recently, considering the problem of data anomalies in the training of SVM models, Xiong et al [101] proposed a weighted least squares SVM-based method for early prediction method for the life of lithium-ion batteries, which improved the prediction results through the error square term and weight coefficient, and verified the effectiveness of the method through experiments. It provides a theoretical basis for the battery system faults hierarchical management strategy.…”

Section: New Algorithms Based On Machine Learningmentioning

confidence: 99%

Status and Prospects of Research on Lithium-Ion Battery Parameter Identification

Li,

Peng,

Wang

et al. 2024

Batteries

View full text Add to dashboard Cite

Lithium-ion batteries are widely used in electric vehicles and renewable energy storage systems due to their superior performance in most aspects. Battery parameter identification, as one of the core technologies to achieve an efficient battery management system (BMS), is the key to predicting and managing the performance of Li-ion batteries. However, due to the complex chemical reactions and thermodynamic processes inside lithium-ion batteries, coupled with the influence of the external environment, accurate identification of lithium-ion battery parameters has become an urgent problem to be solved. In addition, data-driven parameter identification can enable battery models to better understand battery behavior, which is one of the focuses of future research. For this reason, this paper comprehensively reviews the application of data-driven parameter identification methods in different scenarios. Firstly, the research briefly explains the working principle of lithium-ion batteries and the key parameters affecting their performance. Secondly, this paper deeply discusses data-driven methods for parameter identification, which are widely used nowadays, and provides improvement ideas to address the shortcomings of traditional methods. Finally, the paper discusses the challenges faced by parameter identification technology for lithium-ion batteries and envisages future prospects.

show abstract

Section: New Algorithms Based On Machine Learningmentioning

confidence: 99%

Status and Prospects of Research on Lithium-Ion Battery Parameter Identification

Li,

Peng,

Wang

et al. 2024

Batteries

View full text Add to dashboard Cite

show abstract

“…Ref. [15] proposed a weighted least squares support vector machine (WLS-SVM)-based early prediction method for lithium-ion battery cycle life using health indicators as input.…”

Section: Introductionmentioning

confidence: 99%

Health State Assessment of Lithium-Ion Batteries Based on Multi-Health Feature Fusion and Improved Informer Modeling

He,

Liu,

Huang

et al. 2024

Energies

View full text Add to dashboard Cite

Accurately assessing the state of health (SOH) of lithium batteries is of great significance for improving battery safety performance. However, the current assessment for SOH suffers from the difficulty of selecting health features and the lack of uncertainty using data-driven methods. To this end, this paper proposes a health state assessment method for lithium-ion batteries based on health feature extraction and an improved Informer model. First, multiple features that can reflect the SOH of lithium-ion batteries were extracted from the charging and discharging time, the peak value of incremental capacity curve (ICC), and the inflection point value of differential voltage curve, etc., and the correlation between multiple health features and the health state was evaluated by gray correlation analysis. Then, an improved Informer model is proposed to establish a health state estimation method for lithium-ion batteries. Finally, the proposed algorithm is tested and validated using publicly available battery charge/discharge datasets and compared with other algorithms. The results show that the method in this paper can realize high-precision SOH prediction with a root-mean-square error (RMSE) of 0.011, and the model fit reaches more than 98%.

show abstract

“…Machine learning models have utilised different data-driven methods to forecast the battery SOH (Barré et al, 2014). These methods include GPR (Richardson et al, 2017), weighted least squares support vector machine (WLS-SVM) (Xiong et al, 2023), LSTM (Wang et al, 2023), support vector machine (SVM), random forest (RF), and multiple linear regression (MLR). Hybrid approaches integrate optimization algorithms with data-driven methods or combine filtering methods with other models.…”

Section: Introductionmentioning

confidence: 99%

A study on the application of discrete curvature feature extraction and optimization algorithms to battery health estimation

Goh,

An,

Zhang

et al. 2024

Front. Energy Res.

View full text Add to dashboard Cite

Lithium-ion batteries are extensively utilised in various industries and everyday life. Typically, these batteries are considered retired when their state of health (SOH) drops below 80%. These retired batteries, known as secondary batteries, can be repurposed for applications that demand lower battery performance. Precise forecasting of the lifespan of secondary batteries is crucial for determining suitable operational management approaches. Initially, we use the CACLE dataset for thorough investigation. Therefore, to account for the unpredictable and random character of the application circumstances, we employ the U-chord long curvature feature extraction approach to minimise errors resulting from rotation and noise. Additionally, we utilise the discharged power as a feature. This study employs two optimization algorithms, namely, particle swarm optimization (PSO) and sparrow optimization algorithm (SSA), in conjunction with least squares support vector machine (LSSVM) to compare the model against three conventional models, namely, Gaussian process regression (GPR), convolutional neural networks (CNN), and long short-term memory (LSTM). This work comprises two experiments: Experiment 1 utilises the battery’s charging and discharging history data to train the model for estimating the SOH of the remaining cycles of the same battery. Experiment 2, on the other hand, employs the complete discharging data of the battery to train the model for predicting the SOH of the remaining cycles of other batteries. The error evaluation metrics used are mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The results indicate that the average MAE for SSA-LSSVM, LSTM, CNN, PSO-LSSVM, and GPR in Experiment 1 and Experiment 2 are 1.11%, 1.82%, 2.02%, 2.04%, and 12.18% respectively. The best prediction results are obtained by SSA-LSSVM.

show abstract

Early prediction of lithium-ion battery cycle life based on voltage-capacity discharge curves

Cited by 27 publications

References 28 publications

Status and Prospects of Research on Lithium-Ion Battery Parameter Identification

Status and Prospects of Research on Lithium-Ion Battery Parameter Identification

Health State Assessment of Lithium-Ion Batteries Based on Multi-Health Feature Fusion and Improved Informer Modeling

A study on the application of discrete curvature feature extraction and optimization algorithms to battery health estimation

Contact Info

Product

Resources

About