Review on the Battery Model and SOC Estimation Method
Abstract
:1. Introduction
2. Battery Model
2.1. Electrochemical Mechanism Model
2.2. Equivalent Circuit Model
2.3. Data-Driven Models
3. Research on SOC Estimation Algorithm
3.1. Direct Measurement Method Not Based on Battery Model
3.2. SOC Estimation Method Based on the Black Box Battery Model
3.3. SOC Estimation Method Based on the State Space Battery Model
3.3.1. Research on the Identification Method of Battery Model Parameters
3.3.2. Research Status of SOC Estimation Observer
4. Summary
5. Future Development
Author Contributions
Funding
Conflicts of Interest
References
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Review Article | Merits | Demerits |
---|---|---|
[4] | In-depth overview of battery SOC estimation methods, focusing on estimation errors and their advantages and disadvantages | Mathematical expressions, flowcharts and structural diagrams of related algorithms are not provided |
[5] | Focus on summarizing commonly used lithium-ion battery SOC estimation methods, and analyzing the advantages and disadvantages of various methods | The analysis of SOC estimation algorithm and research progress is not comprehensive |
[6] | The SOC estimation methods of batteries are reviewed, and three battery models and model-based estimation methods are mainly introduced | The data-driven SOC estimation method was not specifically introduced |
[7] | The SOC estimation method based on the equivalent circuit model is systematically sorted out and compared with advantages and disadvantages. It also introduces in detail the factors affecting the estimation error and its countermeasures | Only the model-based SOC estimation methods are reviewed |
[8] | It focuses on analyzing the main characteristics of five types of estimation algorithms and comprehensively comparing and discussing the advantages and disadvantages of models and algorithms | The introduction to the battery model is relatively brief |
[9] | Analyze the improvement of the battery model and the refinement of the algorithm while considering the temperature | The analysis of the research status is not comprehensive enough |
Type of model | Electrochemical mechanism model | Equivalent circuit model | Data-driven models |
Accuracy | Very high | Medium | Medium |
Computational Complexity | Very high | Medium to low | Medium |
Configuration Effort | high | Medium | Medium to high |
Time | Solving control equations consumes a lot of time | Simple and easily understood, so medium time consuming | Less time consuming as prior battery knowledge is not required |
Interpret Ability | Low | High | Low |
Merits | The mathematical model established by the knowledge of electrochemical theory can better reflect the characteristics of the battery and have Very high accuracy | Simple structure. Easy access to model parameters | Do not rely on the battery model, eliminating the tedious process of physical modeling. Can quickly evaluate and analyze the internal state of the battery |
Demerits | Poor adaptability to some working conditions, leading to poor estimation results | Can not reflect the internal characteristics of the battery well | The estimation accuracy depends heavily on the number of samples, and the convergence speed is slow. When the sample size is small and the numerical error rate is high, the model will be over-fitted and under-fitted |
Estimation Method | Merit | Demerit | |
---|---|---|---|
Direct measurement method not based on battery model | Ampere-hour integral method | Simple and reliable, fast estimation speed, low requirements for controller hardware and storage | The sensor has high requirements for accuracy, which is heavily dependent on the accuracy of the initial SOC value, and there is a cumulative error |
Open circuit voltage method | Simple structure, convenient operation and high estimation accuracy | Long standing time and hysteresis effect | |
Internal resistance method | The principle is simple, and the estimation accuracy is high | The resistance test device is expensive, the internal resistance value is small, the range of change is small, and the resistance is easily affected by the temperature and the number of cycles | |
Discharge test method | High estimation accuracy and strong reliability | It takes a long time and requires high test conditions, and it is impossible to estimate the battery SOC value in real time | |
Electrochemical impedance spectroscopy | High estimation accuracy, which can better reflect the dynamic characteristics of the battery | High battery impedance cost, susceptible to battery temperature and life | |
Load voltage method | Good estimation accuracy under constant current conditions | Affected by current changes, it is not suitable for practical applications | |
Estimation method based on black box model | Neural Networks | No battery model is required, with strong variable processing ability and self-learning ability, real-time detection of SOC status | Severely depends on the number of samples, and samples have a greater impact on the training results, long learning time, and heavy sampling workload |
Support Vector Machines | It has strong generalization ability, does not rely on the battery model, and has high estimation accuracy and fast convergence speed in the case of small samples | The estimation accuracy depends heavily on a large number of sample data and weight parameters | |
Deep learning | It has strong generalization ability and parallel processing ability, and the estimation result has high accuracy and stability | Model training is complex, requires high computing resources and configuration, and has over-fitting problems | |
Genetic algorithm | Highly parallel operation, self-organization, self-adaptation, self-learning and group evolution capabilities, high robustness | The algorithm is complex and the global search speed is slow, and it is easy to fall into the local optimum | |
State space-based estimation method | Kalman filter | The estimation accuracy is high in the case of considering the error, does not depend on the initial SOC value, and has a strong anti-interference ability | The estimation accuracy depends on the accuracy of the model, is easily affected by temperature, and is limited to linear systems |
Extended Kalman filter | Suitable for non-linear systems, suitable for working conditions with severe current fluctuations | Ignoring high-order terms in the linearization process produces a large error value and poor robustness | |
Double Kalman filter | High estimation accuracy, which can effectively eliminate noise in the system and model | The amount of calculation is large, and the calculation takes a long time | |
Unscented Kalman Filtering Method | Suitable for nonlinear systems, reducing errors caused by linear systems | Factors such as abnormal disturbance and initial value uncertainty cause the system to diverge, and its robustness is poor | |
Adaptive Kalman filter | Able to continuously estimate the system status in real time and correct the influence of noise | Need noise zero mean hypothesis and noise variance is known, and the measured value may diverge | |
Particle filter | It is not restricted by the linear and Gaussian conditions of the system model, and has few constraints on the probability distribution of state variables | The estimation accuracy is not stable, and the phenomenon of particle depletion is prone to occur |
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Zhou, W.; Zheng, Y.; Pan, Z.; Lu, Q. Review on the Battery Model and SOC Estimation Method. Processes 2021, 9, 1685. https://doi.org/10.3390/pr9091685
Zhou W, Zheng Y, Pan Z, Lu Q. Review on the Battery Model and SOC Estimation Method. Processes. 2021; 9(9):1685. https://doi.org/10.3390/pr9091685
Chicago/Turabian StyleZhou, Wenlu, Yanping Zheng, Zhengjun Pan, and Qiang Lu. 2021. "Review on the Battery Model and SOC Estimation Method" Processes 9, no. 9: 1685. https://doi.org/10.3390/pr9091685
APA StyleZhou, W., Zheng, Y., Pan, Z., & Lu, Q. (2021). Review on the Battery Model and SOC Estimation Method. Processes, 9(9), 1685. https://doi.org/10.3390/pr9091685