State of Charge Estimation of Power Battery Using Improved Back Propagation Neural Network
Abstract
:1. Introduction
2. Back Propagation Neural Network Principle
3. Improved Back Propagation Neural Network
3.1. Improved Back Propagation Neural Network Principle
3.2. Improved Back Propagation Neural Network Modeling
3.2.1. Input Layer Modeling
3.2.2. Hidden Layer Model
3.2.3. Output Layer Model
4. Improved Back Propagation Neural Network Algorithm
- (1)
- Initialize: first, the whole model is initialized to determine the particle size, setting particle velocity, location, global extremum, individual extreme value, and maximum number of iterations.
- (2)
- Neural network training: the initial state neural network training is conducted.
- (3)
- Determination of particle fitness: the feedback mean square error is fully utilized in neural network training, and then brought into the calculation as the fitness function value of the particle swarm.
- (4)
- Optimal value finding: the fitness value of each particle is compared with the optimal value of the individual in the current state, leaving the optimal result; in the same way, group optimal values are identified.
- (5)
- Update speed and location: the velocity and position of the particle swarm is updated according to Equations (5) and (6).
- (6)
- The particle algorithm ends: the end condition is the maximum number of iterations that has been set or the point at which N steps is reached, and the mean square error of all samples can meet the requirements. If the end condition is not met, the particle fitness determination step is repeated, and the sequence is carried forward step by step. When the requirements are met, the iteration is stopped and returned to the optimal individual. The optimal value of the individual particle is the improved neural network weight, and the global optimal value is used as the adjustment threshold.
- (7)
- Improved neural network model error analysis: this step determines whether the final mean square error and the SOC estimation error meet the requirements—the training will be finished if they are satisfied; if not, resetting and training is continued until the error requirements are met. The flow chart of the corresponding algorithm is shown in Figure 4.
5. Working Condition Test and Simulation Analysis
5.1. Construction of Experimental Platform and Training Sample Collection
5.2. Back Propagation Neural Network Simulation
5.3. Improved Back Propagation Neural Network Simulation
5.4. Working Condition Test
6. Conclusions
Author Contributions
Acknowledgments
Conflicts of Interest
Nomenclature
BMS | Battery management system |
SOC | State of charge |
BP | Back propagation |
DV | Differential voltage |
EKF | Extend Kalman filter |
PF | Particle filter |
OCV | Open circuit voltage |
PSO | Particle swarm optimization |
MSE | Mean square error |
LM | Levenberg Marquardt |
CCCV | Constant current constant voltage |
DST | Dynamic Stress Test |
USABC | United States Advanced Battery Consortium |
Pi | Output of the hidden layer |
Yk | Output of the output layer |
p | Transfer function of the hidden layer |
q | Transfer function of the output layer |
ωij | Connection weight between the input layer and the hidden layer |
ωjk | Connection weight between the hidden layer and the output layer |
M | Number of input layers |
L | Number of hidden layers |
N | Number of output layers |
X | Vector of input variables |
θ | Threshold value |
E | Mean square error |
tk | Expected output |
Yk | Actual output |
ωa | Current state weight |
η | Learning rate |
m | Number of input layer nodes |
a | Adjustment coefficient |
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Network Parameters | Value | Network Parameters | Value |
---|---|---|---|
Maximum number of iterations | 1000 | Learning rate | 0.05 |
Target mean square error | 4–10 | Optimal number of iterations | 312 |
Research Methods | BP Neural Network | Improved BP Neural Network |
---|---|---|
Maximum estimation error in simulation | 5% | 3% |
Maximum estimation error in working condition test | 6% | 4% |
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Share and Cite
Zhang, C.-W.; Chen, S.-R.; Gao, H.-B.; Xu, K.-J.; Yang, M.-Y. State of Charge Estimation of Power Battery Using Improved Back Propagation Neural Network. Batteries 2018, 4, 69. https://doi.org/10.3390/batteries4040069
Zhang C-W, Chen S-R, Gao H-B, Xu K-J, Yang M-Y. State of Charge Estimation of Power Battery Using Improved Back Propagation Neural Network. Batteries. 2018; 4(4):69. https://doi.org/10.3390/batteries4040069
Chicago/Turabian StyleZhang, Chuan-Wei, Shang-Rui Chen, Huai-Bin Gao, Ke-Jun Xu, and Meng-Yue Yang. 2018. "State of Charge Estimation of Power Battery Using Improved Back Propagation Neural Network" Batteries 4, no. 4: 69. https://doi.org/10.3390/batteries4040069