Study of Lithium-Ion Battery Charge State Estimation Based on BP Neural Network Fusion Optimized Sparrow Algorithm
Abstract
1. Introduction
2. SOC Prediction Model of Lithium Battery Based on BP Neural Network
3. BP Neural Network Based on ISSN Optimization
3.1. Sparrow’s Algorithm to Optimize BP Neural Networks
- (1)
- Generation of the Sparrow Population
- (2)
- Calculation of Fitness for Each Sparrow in the Population
- (3)
- In the population, producers with higher fitness values are prioritized in obtaining energy during the search process. At the same time, producers are responsible for leading the entire population, guiding the population toward the food source. The position update for producers follows the formula below:
3.2. Optimizing the Sparrow Algorithm
- (1)
- Tent Chaos Mapping
- (2)
- Sine–cosine algorithm
- (3)
- Firefly Perturbation Strategy
3.3. Comparison of Optimization Algorithms
3.4. Model Training and Convergence
3.5. ISSNBP-Based Model Realization Process
- (1)
- Normalize the input and output data, and set the basic parameters of the BP neural network, such as the maximum number of iterations, learning rate, etc.
- (2)
- Set the basic parameters of the Sparrow Search Algorithm, including the maximum number of iterations, population size, safety threshold, upper and lower bounds of initial values, etc.
- (3)
- Initialize the population using Tent chaotic mapping, and calculate the fitness of each sparrow individual in every generation.
- (4)
- Update each individual in the sparrow population, selecting individuals with high fitness values as producers.
- (5)
- Introduce the sine–cosine algorithm into the update of producers, update the positions of producers, and select individuals with high fitness as producers.
- (6)
- Update the positions of followers and alerters in the sparrow population, and calculate their fitness.
- (7)
- Add firefly disturbance to the updated population, calculate the fitness of the updated sparrow population, and update the positions of the sparrows.
- (8)
- Calculate the fitness, determine the optimal position of the population, and input the optimal structural parameters into the BP neural network for training.
- (9)
- Judge whether the training requirements are met based on the training error. If not, continue training the network; if met, stop the calculation and output the battery’s SOC value.
4. Simulation Experiment and Analysis
4.1. Sampling Data Selection
4.2. Results and Discussion
5. Conclusions
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
BMS | Battery management system |
SOC | State of charge |
SSA | Sparrow Search Algorithm |
BP | Backpropagation |
DST | Dynamic Stress Test |
UDDS | Urban Dynamometer Driving Schedule |
FUDS | Federal Urban Driving Schedule |
MAE | Mean absolute error |
MSE | Mean squared error |
RMSE | Root Mean Squared Error |
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Algorithm | Advantages | Disadvantages |
---|---|---|
PSO | Fast convergence Simple implementation | Prone to local optima |
GA | Strong global search ability Adaptive crossover mechanisms | Slow convergence Sensitive to noise |
GWO | Good balance between exploration and exploitation | Performance degrades in high dimensions |
SSA | Good global exploration Fewer control parameters Accurate | Sensitive to initial population quality |
ISSA | High accuracy Improved robustness Better convergence | Increased complexity due to hybridization |
BP | SSA-BP | ISSA-BP | |
---|---|---|---|
MAE | 0.0122 | 0.0109 | 0.0073 |
MSE | 2.8494 × 10−4 | 2.2003 × 10−4 | 1.1927 × 10−4 |
RMSE | 0.0169 | 0.0148 | 0.0109 |
BP | SSA-BP | ISSA-BP | |
---|---|---|---|
MAE | 0.0111 | 0.0102 | 0.0079 |
MSE | 2.0756 × 10−4 | 1.9204 × 10−4 | 1.1193 × 10−4 |
RMSE | 0.0144 | 0.0139 | 0.0106 |
BP | SSA-BP | ISSA-BP | |
---|---|---|---|
MAE | 0.0108 | 0.0107 | 0.0081 |
MSE | 2.3585 × 10−4 | 2.3547 × 10−4 | 1.5821 × 10−4 |
RMSE | 0.0154 | 0.0153 | 0.0126 |
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Han, S.; Wei, T.; Wang, L.; Li, X.; Chen, D.; Jia, Z.; Zhang, R. Study of Lithium-Ion Battery Charge State Estimation Based on BP Neural Network Fusion Optimized Sparrow Algorithm. Coatings 2025, 15, 697. https://doi.org/10.3390/coatings15060697
Han S, Wei T, Wang L, Li X, Chen D, Jia Z, Zhang R. Study of Lithium-Ion Battery Charge State Estimation Based on BP Neural Network Fusion Optimized Sparrow Algorithm. Coatings. 2025; 15(6):697. https://doi.org/10.3390/coatings15060697
Chicago/Turabian StyleHan, Shaojian, Tianhao Wei, Liyong Wang, Xiaojie Li, Dongdong Chen, Zhenhua Jia, and Rui Zhang. 2025. "Study of Lithium-Ion Battery Charge State Estimation Based on BP Neural Network Fusion Optimized Sparrow Algorithm" Coatings 15, no. 6: 697. https://doi.org/10.3390/coatings15060697
APA StyleHan, S., Wei, T., Wang, L., Li, X., Chen, D., Jia, Z., & Zhang, R. (2025). Study of Lithium-Ion Battery Charge State Estimation Based on BP Neural Network Fusion Optimized Sparrow Algorithm. Coatings, 15(6), 697. https://doi.org/10.3390/coatings15060697