摘要
为了减少噪声对锂离子电池荷电状态估计的影响,本文提出一种新颖的基于极限学习机和最大相关熵平方根容积卡尔曼滤波的SOC估计方法.首先,利用泛化性好、运行速度快的极限学习机作为卡尔曼滤波的测量方程;其次,基于灰狼优化算法,极限学习机的超参数被优化以提高电池荷电状态的估计精度;最后,基于最大相关熵平方根容积卡尔曼滤波,极限学习机的测量噪声被进一步减弱.所提方法可以简化极限学习机繁琐的调参过程,且为闭环的SOC估计方法.所提方法在多工况和宽温度范围内被测试以验证其泛化性能.测试结果显示,所提方法明显地提高了锂离子电池的荷电状态估计精度.同时,对比其他算法,所提方法的平均运行时间仅仅为长短时序列和循环门控单元网络的三分之一.当行驶工况复杂、温度变化区间较大时,所提方法的均方根误差小于1%,最大误差小于3%.当存在初始误差与环境噪声时,所提方法显示出了优越的鲁棒性.
Lithium-ion batteries are widely used in electric vehicles and energy storage systems. As a prerequisite for the safe and efficient application of lithium-ion batteries, battery management systems have received extensive attention worldwide. Among these prerequisites, the state of charge(SOC), as the basic parameter of battery management system online application, is crucial for the safe and efficient operation of battery management systems. However, measurement noise decreases the accuracy and robustness of the state of charge estimation. To reduce the impact of noise on the state of charge estimation of lithium-ion batteries, a novel SOC estimation method based on an extreme learning machine and a maximum correlation entropy square root volumetric Kalman filter is proposed in this paper. First, the extreme learning machine is used as the measurement equations of the Kalman filter because of its good generalization and fast running speed, and the voltage and current are selected as the model input;second, on the basis of the gray wolf optimization algorithm, the extreme learning machine hyperparameters are thoroughly optimized to improve the accuracy of the state of charge estimation for lithium-ion batteries;finally, on the basis of the framework of the maximum correlation entropy square root volumetric Kalman filter, a closed-loop estimation is realized to further reduce the state of charge estimation error caused by the measurement noise of voltage and current. The proposed method can simplify the time-consuming parameter adjustment of an extreme learning machine and show superior robustness under low-quality measurement. The proposed method is validated under multiple drive cycles and a wide temperature range to verify its generalization performance. The test results show that the proposed method substantially improves the accuracy of lithium-ion battery state of charge estimation.At the same time,the average running time of the proposed method is only one-third of that of long short memory neural networks and gate recurrent unit neural networks.Under complex driving conditions and a large temperature range,the root mean square error of the proposed method is less than 1%,and the maximum error is less than 3%.Furthermore,two case experiments are performed to evaluate the robustness of the proposed closed-loop estimation approach,and the results obtained when data have an initial state of charge error and measurement noise verify the superior robustness of the proposed approach compared with long short memory neural networks and gate recurrent unit neural networks.
作者
王桥
叶敏
魏孟
廉高棨
武晨光
WANG Qiao;YE Min;WEI Meng;LIAN Gao-qi;WU Chen-guang(National Engineering Research Center for Highway Maintenance Equipment,Chang'an University,Xi'an 710064,China;Department of Mechanical Engineering,National University of Singapore,Singapore 117576,Singapore)
出处
《工程科学学报》
EI
CSCD
北大核心
2023年第6期995-1002,共8页
Chinese Journal of Engineering
基金
陕西省科技创新团队(2020TD0012)
中央高校基本科研业务费专项资金-长安大学优秀博士学位论文培育资助项目(300102252710)
陕西省重点研发计划资助项目(2023-GHYB-05)。
关键词
锂离子电池
荷电状态估计
极限学习机
灰狼优化
卡尔曼滤波
鲁棒性估计
lithium-ion battery
state of charge
extreme learning machine
grey wolf optimizer
Kalman filter
closed-loop estimation