| 摘要: |
| 传统的电池荷电状态（State of Charge,SOC）估计方法是基于精确的数学模型，它依赖于大量的建模假设和经验参数，故模型预测SOC精度是有限的；为了提高动力电池SOC预测的精度，提出利用人工鱼群算法（Artificial Fish Swarm Algorithm, AFSA）优化径向基神经网络（RBF）对SOC进行预测，解决了RBF网络参数选择的不确定性；仿真实验结果表明：方法能方便、快速、准确地实现对SOC的预测，且具有实际使用价值。 |
| 关键词: 电动汽车电池 荷电状态SOC预测 径向基神经网络 人工鱼群算法 |
| DOI： |
| 分类号: |
| 基金项目: |
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| SOC Prediction for Electric Vehicle Battery Based on AFSA RBF Neural Network |
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FENG Zhi min, TIAN Li
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| Abstract: |
| According to the principle that traditional battery state of charge (State of Charge, SOC) estimation method is based on precise mathematical model which relies on a lot of modeling assumptions and empirical parameters, therefore, SOC accuracy by the model prediction is limited. In order to improve the prediction accuracy for the battery SOC, Artificial Fish Swarm Algorithm (AFSA) is applied to predicting the SOC by optimizing Radial Basis Function (RBF) neural network, which solve the uncertainty in RBF network parameter choice. The simulation test results show that this method can easily, quickly and accurately achieve SOC prediction and has practical value. |
| Key words: electric vehicle battery State of Charge SOC prediction Radial Basis Function neural network Artificial Fish Swarm Algorithm |
