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pybamm电池模型基础语法（Python编程）

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前一篇文章介绍了Python编程软件以及电池模拟代码库的安装过程，点击标题 pybamm锂离子电池模拟环境搭建步骤和过程 可以查看。本文主要分享自己从零基础开始学习pybamm代码语言的笔记和经验。

1. Doyle-Fuller-Newman (DFN) model电化学模型简单调用

import pybamm #在python中引入电池电化学模拟代码库

model = pybamm.lithium_ion.DFN()

#调用Doyle-Fuller-Newman (DFN) 锂离子电池模型

sim = pybamm.Simulation(model) # 仿真

sim.solve([0, 3600]) #求解

sim.plot() #绘制主要的参数曲线

以上简单语句是调用Doyle-Fuller-Newman (DFN) 锂离子电池模型，所有参数全部采用模型默认值，以1C电流放电过程的模拟，并且绘制主要的参数曲线。

2. 模型变量以及参数值的查找

# 1. Doyle-Fuller-Newman (DFN) model电化学模型简单调用

import pybamm #在python中导入电池电化学模拟代码库

model = pybamm.lithium_ion.DFN() #调用Doyle-Fuller-Newman (DFN) 锂离子电池模型

# 模型变量列表查看

model.variable_names() #模型变量列表

model.variables.search("electrolyte") #查找模型中包含指定单词electrolyte的变量

#参数值可用search查找

parameter_values.search("electrolyte")#查看模型中包含指定单词electrolyte的参数值

前面采用模型默认参数运行了放电模拟，第二部分代码介绍模型中变量的查找，以及参数值查看，以上代码运行结果如下：

模型变量查找结果：

Electrolyte concentration

Electrolyte concentration [Molar]

Electrolyte concentration [mol.m-3]

Electrolyte current density

Electrolyte current density [A.m-2]

Electrolyte flux

Electrolyte flux [mol.m-2.s-1]

Electrolyte potential

Electrolyte potential [V]

Electrolyte tortuosity

Gradient of electrolyte potential

Gradient of negative electrolyte potential

Gradient of positive electrolyte potential

Gradient of separator electrolyte potential

Negative electrolyte concentration

Negative electrolyte concentration [Molar]

Negative electrolyte concentration [mol.m-3]

Negative electrolyte potential

Negative electrolyte potential [V]

Negative electrolyte tortuosity

Positive electrolyte concentration

Positive electrolyte concentration [Molar]

Positive electrolyte concentration [mol.m-3]

Positive electrolyte potential

Positive electrolyte potential [V]

Positive electrolyte tortuosity

Separator electrolyte concentration

Separator electrolyte concentration [Molar]

Separator electrolyte concentration [mol.m-3]

Separator electrolyte potential

Separator electrolyte potential [V]

Sum of electrolyte reaction source terms

Sum of negative electrode electrolyte reaction source terms

Sum of positive electrode electrolyte reaction source terms

Sum of x-averaged negative electrode electrolyte reaction source terms

Sum of x-averaged positive electrode electrolyte reaction source terms

X-averaged battery electrolyte ohmic losses [V]

X-averaged electrolyte concentration

X-averaged electrolyte concentration [Molar]

X-averaged electrolyte concentration [mol.m-3]

X-averaged electrolyte overpotential

X-averaged electrolyte overpotential [V]

X-averaged electrolyte potential

X-averaged electrolyte potential [V]

X-averaged negative electrolyte concentration

X-averaged negative electrolyte concentration [mol.m-3]

X-averaged negative electrolyte potential

X-averaged negative electrolyte potential [V]

X-averaged negative electrolyte tortuosity

X-averaged positive electrolyte concentration

X-averaged positive electrolyte concentration [mol.m-3]

X-averaged positive electrolyte potential

X-averaged positive electrolyte potential [V]

X-averaged positive electrolyte tortuosity

X-averaged separator electrolyte concentration

X-averaged separator electrolyte concentration [mol.m-3]

X-averaged separator electrolyte potential

X-averaged separator electrolyte potential [V]

参数值查看结果：

EC initial concentration in electrolyte [mol.m-3] 4541.0

Electrolyte conductivity [S.m-1] <function electrolyte_conductivity_Capiglia1999>

Electrolyte diffusivity [m2.s-1] <function electrolyte_diffusivity_Capiglia1999>

Initial concentration in electrolyte [mol.m-3] 1000.0

Negative electrode Bruggeman coefficient (electrolyte) 1.5

Positive electrode Bruggeman coefficient (electrolyte) 1.5

Separator Bruggeman coefficient (electrolyte) 1.5

Typical electrolyte concentration [mol.m-3] 1000.0

3. 可以对模型默认参数进行简单修改

# 1. Doyle-Fuller-Newman (DFN) model电化学模型简单调用

import pybamm #在python中导入电池电化学模拟代码库

model = pybamm.lithium_ion.DFN() #调用Doyle-Fuller-Newman (DFN) 锂离子电池模型

#查看默认参数值

parameter_values = model.default_parameter_values

parameter_values.search("Current function [A]") #用search查看默认参数值

#可以对默认参数进行简单修改：

parameter_values["Current function [A]"] = 3 #修改参数值

parameter_values.search("Current function [A]") #查看修改后参数值

以上代码运行结果如下：

Current function [A] 0.680616 默认参数值，放电电流

Current function [A] 3 修改后参数值

来源：锂想生活

化学 python

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首次发布时间：2023-09-18