人工心脏泵用锂电池关键健康因子估计  

Estimation of key health indicators of lithium battery for artificial heart pumps

作　　者：沈喆 陈海丰 王媛惠 屈增 喻航 Shen Zhe;Chen Haifeng;Wang Yuanhui;Qu Zeng;Yu Hang(Shanxi Jinzhong Institute of Technology,Jinzhong 030600,China;Changzhi Jiu An Artificial Heart Science and Technology Development Co.,Ltd.,Changzhi 046000,China;School of Electrical and Control Engineering,North University of China,Taiyuan 030051,China;Key Laboratory of Dynamic Measurement,North University of China,Taiyuan 030051,China)

机构地区：[1]山西晋中理工学院,晋中030600 [2]长治市久安人工心脏科技开发有限公司,长治046000 [3]中北大学电气与控制工程学院,太原030051 [4]中北大学动态测试技术国家重点实验室,太原030051

出　　处：《电子测量技术》2025年第4期139-148,共10页Electronic Measurement Technology

基　　金：山西省重点研发计划(202102130501020)项目资助。

摘　　要：当前锂电池的健康状态估计技术主要聚焦于新能源汽车动力电池等非生命支持设备,推广于人工心脏泵用锂电池时,显著的工况差异和难以表征复杂电化学反应特性的简单模型限制了SOH估计的准确性与可靠性。为此,针对人工心脏泵用锂电池高阶模型计算复杂度与SOH准确性评估的固有矛盾,提出一种自优化的关键健康因子估计方法建立锂电池模型。首先,针对锂电池电化学系统电流电势非线性使得阻抗测不准而难以建立模型的问题,设计暂稳态的电化学阻抗谱法并利用自研EIS测试装置获取多温度多SOC多频率下的多维阻抗信息。然后,分析阻抗信息与电池健康状态之间的线性关系,建立最小化阻抗目标函数并利用改进的粒子群优化算法求解优化问题。最后,硬件在环仿真实验模拟人工心脏泵用锂电池脉动模式下的不同工况并验证了所提方法的可行性和有效性。实验结果表明,所提算法在不同荷电状态和不同温度下关键健康因子估计误差小于2%;与标准PSO算法相比,所提算法的估计精度提升了1.88%,满足人工心脏泵用锂电池高精度的模型建立要求和SOH估计。The current lithium battery state of health(SOH)estimation technology primarily focuses on non-life-supporting devices such as electric vehicle power batteries.When applied to artificial heart pump lithium batteries,significant operational differences and the inability of simple models to characterize complex electrochemical reactions limit the accuracy and reliability of SOH estimation.To address the inherent contradiction between the computational complexity of higher-order models and the accuracy of SOH assessment for artificial heart pump lithium batteries,a self-optimizing key health factor estimation algorithm is proposed to establish a lithium battery model.Firstly,to tackle the issue of inaccurate impedance measurement due to the nonlinear current-voltage characteristics of lithium battery electrochemical systems,a quasi-steady-state electrochemical impedance spectroscopy method is designed,and a self-developed EIS testing device is used to obtain multidimensional impedance information under various temperatures,state of charge,and frequencies.Then,the linear relationship between impedance information and battery health status is analyzed,a minimized impedance objective function is established,and an improved particle swarm optimization algorithm is utilized to solve the optimization problem.Finally,hardware-in-the-loop simulation experiments simulate different conditions under the pulsating mode of artificial heart pump lithium batteries and validate the feasibility and effectiveness of the proposed method.Experimental results show that the proposed algorithm has an estimation error of less than 2%for key health factors under different SOC and temperatures;compared to the standard PSO algorithm,the estimation accuracy of the proposed algorithm is increased by 1.88%,meeting the high-precision model establishment requirements and SOH estimation for artificial heart pump lithium batteries.

关 键 词：人工心脏泵用锂电池 健康状态 二阶RC等效电路模型 电化学阻抗谱法 粒子群优化算法 关键健康因子 

分 类 号：TN710.2[电子电信—电路与系统] R197.39[医药卫生—卫生事业管理]