BP neural network approach for heat generation rate estimation of power battery for electric vehicles

doi:10.16085/j.issn.1000-6613.2023-0229

Abstract

Abstract:

The heat generation of battery is one of the critical indicators of battery thermal management. Accurate estimation of the heat generation rate of the battery is crucial to building an efficient battery thermal management system and thereby facilitating the safe driving of electric vehicles. However, most researchers currently rely on model-based simulations to estimate the heat generation rate of electric vehicle batteries. There are some disadvantages in this method, such as time-consuming and only application to some specific battery condition, which impede its wide application in addressing real-time heat generation rate of battery for electric vehicles. This paper proposed a precise battery heat production rate estimation strategy based on BO-Adam-BP neural network approach, that was, an electric vehicle power battery heat production estimation model based on BP neural network. The model used the Bayesian optimization algorithm (Bayesian optimization, BO) to select hyperparameters of BP (back propagation, BP) neural network, and used Adam (adaptive momentum estimation) optimization algorithm to speed up the convergence speed and improve the accuracy and stability of the model. Comparing to the battery heat generation power of constant current discharge experiments under different discharge rates and various ambient temperatures, the results showed that the estimated average error of the model was 5.01%, and the maximum error was only 5.53W. The R² fitting index could reach up to 99.98%, proving that the proposed battery heat production estimation model had achieved high accuracy and strong robustness, providing a paradigm structure for real-time heat production estimation of electric vehicle batteries.

Key words: electric vehicle, power battery, BP neural network, heat generation rate estimation, optimization algorithm

摘要：

电池的产热情况是电池热管理的重要指标之一，准确估计电池产热功率对构建高效运行的电池热管理系统以确保电动汽车安全行驶至关重要。然而，目前大多采用基于模型的方法进行电池产热估计，但此方法存在耗费时间长和仅应用于某种特定电池状况产热估计等缺点，无法解决电动汽车电池实时产热估计的问题。对此，本文提出了一种基于人工智能算法的精准电池产热功率估计方法，即基于BP（back propagation，BP）神经网络的电动汽车动力电池产热估计模型。该模型利用贝叶斯优化算法（Bayesian optimization，BO）对BP神经网络进行超参数选取，采用Adam（adaptive momentum estimation，Adam）优化算法加快收敛速度，提高了模型的准确度和稳定性。研究对比了不同放电倍率和不同环境温度下恒流放电实验的电池产热功率，结果表明模型的估计平均误差为5.01%，最大误差仅为5.53W，R²拟合指标最高可达99.98%，证明了所提出的电池产热估计模型取得了较高的估计精度和较强的鲁棒性，为电动汽车电池实时产热估计提供了一个范式结构。

关键词: 电动汽车, 动力电池, BP神经网络, 产热估计, 优化算法

CLC Number:

TK11⁺3

WANG Jinghan, LYU Jie, ZHAO Ding, LIN Wenye, SONG Wenji, FENG Ziping. BP neural network approach for heat generation rate estimation of power battery for electric vehicles[J]. Chemical Industry and Engineering Progress, 2024, 43(1): 400-406.

王敬翰, 吕杰, 赵丁, 林文野, 宋文吉, 冯自平. 基于BP神经网络的电动汽车动力电池产热估计[J]. 化工进展, 2024, 43(1): 400-406.

Figures/Tables 13

方法	优点	缺点	准确度	稳定性
基于实验的方法	实验系统简单，人为操作不复杂	需要耗费大量的时间，仅限于实验室条件	★★★★	★★★
基于模型的方法	产热机理解释清晰，能了解内在逻辑关系	仿真结果只表示某种特定条件，不适用复杂行驶条件	★★★	★★★★
基于人工智能算法的方法	模型具有通用性和可迁移性^[5]，适用复杂行驶条件	需要大量数据支撑，系统原理复杂	★★★★★	★★★★

序号	流程
1	For t=1, 2, …, do
2	最小化采集函数，得到下一个评估点：x_t =argmin _x_∈_Uz(x\|D_1:_t_-1)
3	评估目标函数值：y_t =f(x_t )+ε_t
4	整理数据：D_1:_t =D_1:_t_-1∪(x_t, y_t )
5	更新目标函数f(x)
6	End for

参数	数值范围
批量处理样本数	[1000,1500]
L2正则化系数	[10^-8, 10^-3]
初始学习率	[10^-3, 10^-1]

序号	流程
1	While θ_t 不收敛 do
2	更新步长：t=t+1
2	计算损失函数梯度：g_t =∇ _wf(θ_t_-1)
3	计算一阶矩和二阶矩：m_t =β₁m_t_-1+(1-β₁)g_t，v_t =β₂m_t_-1+(1-β₂)g_t²
4	偏差纠正： $z m t$ = $z m t$ /(1-β₁^t )， $z v t$ = $z v t$ /(1-β₂^t )
5	更新权值w_t 和阈值b_t ： $θ t = θ t - 1 - α z m t z v t + ε$
6	End while

序号	流程
1	While θ_t 不收敛 do
2	更新步长：t=t+1
2	计算损失函数梯度：g_t =∇ _wf(θ_t_-1)
3	计算一阶矩和二阶矩：m_t =β₁m_t_-1+(1-β₁)g_t，v_t =β₂m_t_-1+(1-β₂)g_t²
4	偏差纠正： $z m t$ = $z m t$ /(1-β₁^t )， $z v t$ = $z v t$ /(1-β₂^t )
5	更新权值w_t 和阈值b_t ： $θ t = θ t - 1 - α z m t z v t + ε$
6	End while

项目	参数
额定容量/A·h	40
额定电压/V	3.7
放电能量/W·h	143
充电截止电压/V	4.2
放电截止电压/V	2.75
长×宽×高/mm	148×27×92
电池质量/g	840
比热容/J·kg^-1·K^-1	1192.16
热导率/W·m^-1·K^-1	沿长度方向：55.5
	沿宽度方向：0.8531
	沿高度方向：55.5

数据编码	U_oc/V	U_act/V	T_bat/℃	Q/W
1	4.1164	4.0008	25.687	4.6244
2	4.1161	4.0004	25.71	4.6281
3	4.1158	4.0001	25.702	4.6278
4	4.1155	3.9998	25.694	4.6275
5	4.1152	3.9995	25.71	4.6273
6	4.1149	3.9992	25.699	4.6270
7	4.1146	3.9989	25.71	4.6268
8	4.1143	3.9986	25.72	4.6267
9	4.1140	3.9983	25.728	4.6265
10	4.1137	3.9977	25.715	4.6384

主成分	指标	累计贡献率
x₁	U_act/V	0.9580
x₂	I/A	0.9862
x₃	SOC	1
x₄	电池表面温度/℃	1

倍率	MADE	RMSE	R²
0.5C	0.0852	0.1191	0.9945
1.0C	0.0511	0.0637	0.9998
1.5C	0.1963	0.3813	0.9948
2.5C	0.3900	0.5831	0.9961

温度/℃	MADE	RMSE	R²
0	0.0723	0.1246	0.9983
25	0.0852	0.1191	0.9998
35	0.1235	0.1849	0.9862

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