Performance Optimization of Negative Electrode Material for Vanadium-based Nickel Metal Hydride Battery Based on Neural Network Algorithm
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摘要: 采用6×36×12×1四层拓扑结构神经网络模型,以钛含量、镍含量、铝含量、铬含量、保温温度和保温时间为输入层参数,以充放电循环稳定性为输出层参数,构建了钒基镍氢电池负极材料性能优化神经网络模型。结果表明,模型具有较强预测能力和较高预测精度,平均相对训练误差4.8%、平均相对预测误差4.9%。与现有V3Ti Ni0.56材料相比,神经网络模型优化获得的V3Ti Ni0.56Al0.3Cr0.4材料在充放电循环30次后的放电容量衰减率从61%减小到26%。
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关键词:
- 钒基镍氢电池 /
- 负极材料 /
- V3TiNi0.56Al0.3Cr0.4 /
- 神经网络 /
- 充放电循环稳定性
Abstract: The neural network model with 6×36×12×1 four-layer topological structure was used to optimize the performances of the negative electrode material of vanadium based hydrogen storage battery.The input layer parameters were titanium content,nickel content,aluminum content,chromium content,holding temperature and holding time.The output layer parameters were charge and discharge cycle stability.The results show that the model has strong prediction ability and high prediction accuracy,with an average relative training error of 4.8%and an average relative prediction error of 4.9%.Compared with the existing V3Ti Ni0.56material,the capacity decay rate of V3Ti Ni0.56Al0.3Cr0.4material optimized by neural network model after 30 charge-discharge cycles is reduced from 61%to 26%. -
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