电源学报

基于鲸鱼算法优化CNN对叉车电池SOH进行预测

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徐泽明中国电源学会学生会员 , 李凯昂 , 顾玉琦 , 赵超

电源学报 | 新能源发电与储能 2026,24(4): 196-204

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电源学报 |新能源发电与储能 2026 , 24 (4) : 196 -204

基于鲸鱼算法优化CNN对叉车电池SOH进行预测

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徐泽明（1997—　），男，中国电源学会学生会员，硕士研究生。研究方向：电池管理系统，电池健康度预测。E-mail：1287754487@qq.com。

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徐泽明（1997—　），男，中国电源学会学生会员，硕士研究生。研究方向：电池管理系统，电池健康度预测。E-mail：1287754487@qq.com。

李凯昂（2001—　），男，硕士研究生。研究方向：数字化设计与制造技术。E-mail：1561611733@qq.com。

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李凯昂（2001—　），男，硕士研究生。研究方向：数字化设计与制造技术。E-mail：1561611733@qq.com。

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赵超（1980—　），男，博士，教授。研究方向：电池管理系统，储能技术。E-mail：zhaochao@zafu.edu.cn。

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赵超（1980—　），男，博士，教授。研究方向：电池管理系统，储能技术。E-mail：zhaochao@zafu.edu.cn。

徐泽明中国电源学会学生会员, 李凯昂, 顾玉琦, 赵超

作者信息

浙江农林大学光机电工程学院，杭州　310000

通讯作者:

顾玉琦（1979—　），女，通信作者，博士，副教授。研究方向：电池管理系统，储能技术。E-mail：guyuqi@zafu.edu.cn。

作者简介:

徐泽明（1997—　），男，中国电源学会学生会员，硕士研究生。研究方向：电池管理系统，电池健康度预测。E-mail：1287754487@qq.com。

李凯昂（2001—　），男，硕士研究生。研究方向：数字化设计与制造技术。E-mail：1561611733@qq.com。

赵超（1980—　），男，博士，教授。研究方向：电池管理系统，储能技术。E-mail：zhaochao@zafu.edu.cn。

Predicting SOH of Forklift Batteries Based on CNN Optimized by Whale Optimization Algorithm

Zeming XU Student Member, CPSS, Kai’ang LI, Yuqi GU, Chao ZHAO

Affiliations

College of Optical, Mechanical and Electrical Engineering, Zhejiang Agriculture and Forestry University, Hangzhou 310000, China

出版时间: 2026-04-30 doi: 10.13234/j.issn.2095-2805.2026.4.196

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摘要

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铅酸电池健康状态SOH（state of health）是铅酸电池可靠运行的重要指标，针对叉车电池的健康状态预测精确度低的问题，提出了1种基于鲸鱼算法WOA（whale optimization algorithm）的卷积神经网络CNN（convolutional neural networks）预测模型WOA-CNN。该模型以CNN作为基础模型，为了进一步优化CNN模型的性能，引入鲸鱼算法作为1种全局优化方法，通过搜索空间中的最优解来优化CNN的权重和偏差。为了验证WOA-CNN模型的有效性，使用了叉车电池的实际数据集进行实验。实验结果表明，WOA-CNN在预测叉车电池SOH时，相比于未优化的CNN，均方误差、均方根误差、平均绝对误差、平均绝对百分比误差分别降低了29.4%、19.7%、41.5%、19.5%；相比于传统的反向传播BP（back propagation）神经网络，分别降低了42.2%、31.1%、64.7%、54.8%，具有更小的预测误差、更高的准确性和稳定性。WOA-CNN预测模型为电动叉车电池健康管理提供了有力的支持，从而延长电池寿命，并提高叉车的可靠性。

关键词

叉车电池 / 健康状态 / 鲸鱼算法 / 卷积神经网络

Abstract

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The state of health (SOH) of lead-acid batteries is an important indicator for their reliable operation. A prediction model based on convolutional neural networks (CNN) optimized by the whale optimization algorithm (WOA) was proposed to solve the problem of low prediction accuracy of SOH of forklift batteries. First, CNN was taken as the basic model for the WOA-CNN model. Then, to further optimize the performance of the CNN model, the WOA algorithm was introduced to optimize the weight and bias of CNN by searching the optimal solution in space. To verify the validity of the WOA-CNN method, experiments were conducted using a real data set of forklift batteries. Experimental results show that the WOA-CNN model reduced the mean square error, root mean square error, mean absolute error and mean absolute percentage error in predicting the SOH of forklift batteries by 29.4%, 19.7%, 41.5% and 19.5% respectively compared with the unoptimized CNN neural network, and it reduced these errors by 42.2%, 31.1%, 64.7% and 54.8% respectively compared with the traditional BP neural network, exhibiting smaller prediction errors, higher accuracy and higher stability. The WOA-CNN prediction model provides strong support for the battery health management of electric forklifts, thus extending the battery life and improving the forklift reliability.

Key words

Forklift battery / state of health (SOH) / whale optimization algorithm (WOA) / convolutional neural networks (CNN)

引用本文

徐泽明中国电源学会学生会员, 李凯昂, 顾玉琦, 赵超. 基于鲸鱼算法优化CNN对叉车电池SOH进行预测. 电源学报, 2026 , 24 (4) : 196 -204 . DOI: 10.13234/j.issn.2095-2805.2026.4.196

Zeming XU, Kai’ang LI, Yuqi GU, Chao ZHAO. Predicting SOH of Forklift Batteries Based on CNN Optimized by Whale Optimization Algorithm[J]. Journal of Power Supply, 2026 , 24 (4) : 196 -204 . DOI: 10.13234/j.issn.2095-2805.2026.4.196

基金

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浙江省重点研发计划项目子项目(2023C02053)

参考文献

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2026年第24卷第4期

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文章信息

doi: 10.13234/j.issn.2095-2805.2026.4.196

接收时间：2023-11-20
首发时间：2026-10-29
出版时间：2026-04-30

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出版历史

收稿日期：2023-11-20
修回日期：2024-01-05
录用日期：2024-01-22

基金

Sub-projects of Zhejiang Key R&D Program(2023C02053)

浙江省重点研发计划项目子项目(2023C02053)

作者信息

浙江农林大学光机电工程学院，杭州　310000

通讯作者:

顾玉琦（1979—　），女，通信作者，博士，副教授。研究方向：电池管理系统，储能技术。E-mail：guyuqi@zafu.edu.cn。

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https://castjournals.cast.org.cn/joweb/dyxb/CN/10.13234/j.issn.2095-2805.2026.4.196

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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