Review of State Estimation and Remaining Useful Life Prediction Methods for Lithium–Ion Batteries
Abstract
:1. Introduction
2. SOC Estimation Method
2.1. Classification of SOC Estimation Methods
2.2. The Experimental SOC Estimation Method
2.2.1. Ampere Integration
2.2.2. The Open-Circuit Voltage-Based Method
2.2.3. Other Methods
2.3. The Model Method
2.3.1. SOC Estimation Method Based on the Electrical Model
2.3.2. SOC Estimation Method Based on the Electrochemical Model
2.4. Data-Driven Methods
2.4.1. Neural Networks and their Improvement Method
2.4.2. The Regression Analysis Method and Its Improvement
2.5. The Joint Method
3. RUL Prediction Method
3.1. Classification of RUL Prediction Methods
3.2. The Model-Based Method
3.2.1. Empirical Model
3.2.2. The Semi-Empirical Model
3.2.3. The Electrical Model
3.2.4. The Electrochemical Model
3.3. The Data-Driven Method
3.3.1. The Time Series Prediction Method
- (1)
- Low accuracy for local capacity variation;
- (2)
- Not applicable to predict the RUL in the battery’s entire life cycle due to different degradation characteristics in each aging period;
- (3)
- High dependence on the training set and data quality;
- (4)
- Problem of overfitting.
3.3.2. The Feature-Based Method
3.4. The Joint Method
4. Conclusions
- (1)
- The balance between calculation time and accuracy is difficult to reach. Due to the limitations in the number of computations required, most algorithms are not suitable for online application. In addition, since batteries are usually integrated as modules, calculations multiply in the real world which makes real-time application a challenge;
- (2)
- There are only relevant studies on single batteries at present; however, factors such as the inconsistency of batteries in battery strings in large-scale lithium battery systems such as in an energy storage power station, will cause the original method to no longer be applicable. At present, there is little research on the state estimation and RUL prediction methods of battery packs.
- (1)
- Develop the pack-level, cluster-level, system-level battery equivalent model to reduce the amount of calculation required for state evaluation and RUL prediction;
- (2)
- Enhance state estimation and RUL prediction methods with joint algorithms to compensate for weaknesses and improve the algorithm’s capacity for generalization;
- (3)
- Establish a multi-physical field coupling model for lithium–ion batteries to improve the accuracy of battery status assessment and RUL prediction.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Classification | Configuration | Description | Advantage | Disadvantage | |
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Electrical model | Rint model | An ideal voltage source is connected to a resistor in series. | The model is simple, and the parameter measurement is easy. | It cannot reflect the dynamic characteristics of the battery and has low accuracy with a small range of applications. | |
Thevenin model | The n-order Thevenin equivalent circuit model is based on the Rint model; n RC circuits are connected in series to represent the polarization phenomenon of the cell. | The RC loop is used to simulate the dynamic characteristics of batteries, and the higher the n, the higher the precision | The change in open-circuit voltage and the self-discharge caused by load current accumulated over time are not considered. The bigger the n, the more computation. | ||
PNGV model | Based on the 1st order Thevenin equivalent circuit model, capacitor Cp is added to describe the change in open-circuit voltage caused by load current over time. | The calculation burden is low; Compared with the 1st order Thevenin equivalent circuit model, the accuracy is higher. | It does not solve the battery self-discharge problem. | ||
GNL model | The two RC loops represent concentration polarization and electrochemical polarization, respectively, and the structure is closer to the internal characteristics of the cell. | Compared with PNGV, the open circuit voltage change caused by load current over time is considered, and the battery self-discharge is considered; higher precision and wider applicability. | Compared with the PNGV model, the calculation is more complex with larger calculation burden. | ||
Open-circuit voltage SOC model | The battery terminal voltage is calculated with the relationship between OCV and SOC. | Simple calculation | Some parameters of the model do not have actual physical significance, and the accuracy is low. | ||
Electrochemical model | P2D model | The lithium–ion battery is equivalent to the structure of electrode (positive and negative electrode); diaphragm and electrolyte are composed of numerous spherical solid particles. | High accuracy and wide applicability | It is too complex and computationally intensive, and the analytical solution can be is difficult to obtain. | |
SP model | Two spherical particles are used to represent the positive and negative terminals of lithium ion batteries, respectively. | Simple structure and small amount of calculation | Under the condition of high rate charge and discharge, the assumption of the model is not valid, the calculation error is large and the application range is small. | ||
Simplify the P2D model | The PDE of the P2D model is simplified. | The calculation burden of the P2D model is greatly reduced. It is more accurate and applicable than the SP model. | Unable to solve the inherent problems of the P2D, it is difficult to apply online. |
Classification | Method | Advantage | Disadvantage | |
---|---|---|---|---|
Experimental method | Ampere integration | Non-relative internal mechanism of the battery; simple | Easy to generate cumulative error, which requires high initial value and sensor precision | |
OCV–SOC method | Non-relative to battery types; simple | The SOC cannot be calculated online in real time [5] | ||
Model-based method | Basic model | Electrical model | Simple and practical | Poor accuracy |
Electrochemical model | It reflects the internal characteristics of the battery | Complex and computationally intensive [5] | ||
State estimation method | KF method | The convergence speed is fast, and the noise suppression ability is strong. Low sensitivity to initial value | The system noise is uncertain, which requires high accuracy of the model | |
PF method | Strong robustness and low requirement for model accuracy | Prone to particle degradation Computationally intensive | ||
Data-driven method | Neural network | No reliance on high-precision battery models | Easy to disappear gradient and fall into local optimization Computationally intensive Easy to overfit, poor generalization capability | |
Regression analysis | It can achieve good results in high-dimensional pattern recognition, nonlinear regression and other problems | Only applicable to small data samples | ||
Joint method | Model-based method and data-driven method | The accuracy and reliability of the estimation results are high | High complexity and computationally intensive [2] | |
Data-driven method and data-driven method |
Classification | Health Factors |
---|---|
Voltage curve relavant | Constant voltage charging time [93], voltage increment in the same time interval [94], time consumed for certain voltage increments [95], dQ/dV [97], dV/dQ [97], maximum slope of voltage curve [95] |
Current curve relavant | Constant current charging time [93], current increment in the same time interval [94], time consumed for certain current increments [96], area under current curve [95] |
Temperature | Temperature increment in the same time interval [94], time consumed for certain temperature increments [95] |
Other | Wavelet packet energy entropy [98] |
Classification | Method | Advantage | Disadvantage |
---|---|---|---|
Model-based method | Empirical model | Low calculation burden [3] | Poor accuracy; not be able to consider influence of working conditions and the environment [2] |
Semi-empirical model | Able to consider influence of working conditions and environment | Low accuracy; low robustness under dramatic working condition changes [1] | |
Electrochemical model | Able to reveal the aging mechanism | Large calculation amounts due to too many parameters [3] | |
Data-driven method | Time series prediction method | Does not require complex mathematical models | Large calculation amounts; overfitting; unable to reveal local RUL variation and highly dependent on training set and data quality |
Feature-based method | Related to battery characteristics [4] Good accuracy for local RUL variation | Heavy calculation burden [2] | |
Joint method | Model-based method and data-driven method | Relatively high reliability and accuracy [4] | Heavy calculation burden; high complexity [2] |
Data-driven method and data-driven method |
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Zhao, J.; Zhu, Y.; Zhang, B.; Liu, M.; Wang, J.; Liu, C.; Hao, X. Review of State Estimation and Remaining Useful Life Prediction Methods for Lithium–Ion Batteries. Sustainability 2023, 15, 5014. https://doi.org/10.3390/su15065014
Zhao J, Zhu Y, Zhang B, Liu M, Wang J, Liu C, Hao X. Review of State Estimation and Remaining Useful Life Prediction Methods for Lithium–Ion Batteries. Sustainability. 2023; 15(6):5014. https://doi.org/10.3390/su15065014
Chicago/Turabian StyleZhao, Jiahui, Yong Zhu, Bin Zhang, Mingyi Liu, Jianxing Wang, Chenghao Liu, and Xiaowei Hao. 2023. "Review of State Estimation and Remaining Useful Life Prediction Methods for Lithium–Ion Batteries" Sustainability 15, no. 6: 5014. https://doi.org/10.3390/su15065014