电气工程学报, 2024, 19(1): 3-22 doi: 10.11985/2024.01.002

特邀专栏:储能关键装备数字化智能安全管理技术

从电池管理到电化学数字电源*

朱建功,, 戴海峰,, 王学远,, 姜波,, 魏学哲,

同济大学汽车学院 上海 201804

Battery Management towards the Digitalized Electrochemical Power Source

ZHU Jiangong,, DAI Haifeng,, WANG Xueyuan,, JIANG Bo,, WEI Xuezhe,

School of Automotive Studies, Tongji University, Shanghai 201804

收稿日期: 2023-08-1   修回日期: 2023-09-18  

基金资助: 国家自然科学基金资助项目(52107230)
国家自然科学基金资助项目(U20A20310)
国家自然科学基金资助项目(52176199)

Received: 2023-08-1   Revised: 2023-09-18  

作者简介 About authors

朱建功，男，1990年生，博士，副教授。主要研究方向为新能源汽车动力电池技术、电池寿命衰减解析、测量评估及预测优化。E-mail：zhujiangong@tongji.edu.cn;

戴海峰，男，1981年生，博士，教授。主要研究方向为复合电源管理技术、状态估计方法。E-mail：tongjidai@tongji.edu.cn;

王学远，男，1990年生，博士，助理教授。主要研究方向为电池电化学阻抗谱及先进BMS设计。E-mail：7wangxueyuan@tongji.edu.cn;

姜波，男，1992年生，博士。主要研究方向为车用动力电池状态估计方法及健康管理技术。E-mail：jiangbo15@tongji.edu.cn;

魏学哲，男，1970年生，博士，教授。主要研究方向为复合电源技术、电力电子技术。E-mail：weixzh@tongji.edu.cn

摘要

电源技术的发展对绿色交通、规模化储能、智慧电网等领域具有重要作用。以电化学器件为代表的电化学电源具有反应机理复杂、多物理场、多尺度特性，同时工作过程中表现出强非线性、时变性及空间分布等特点，为电源的管理带来了挑战。本文以动力电池为对象，分别从模型、测量和管理三个方面阐述当前电池管理技术的研究现状及发展趋势，认为局限于本地的管理技术将逐渐走向全局的数字化管控，提出电化学数字电源的概念并诠释其内涵，旨在助力新能源汽车、氢电储能、新型电网、充电基础设施等电化学电源应用场景的发展。

关键词： 电池; 模型; 测量; 管理; 电化学数字电源

Abstract

The development of power sources plays a vital role in green transportation, large-scale energy storage, smart grid, etc. Electrochemical power sources, usually, have complex reaction mechanisms, and multi-physic and multi-scale characteristics. Also, they exhibit strong nonlinearity, time-varying, and spatial distribution characteristics during operation, which brings challenges to battery management. The power battery is taken as the object, the current research status and technology development trend are reviewed from aspects of battery modeling, measurement, and management, and it is proposed that the local management will move towards global digital management. The concept of digitalized electrochemical power source aims to facilitate the development of electrochemical power source application scenarios, e.g., new energy vehicles, hydrogen energy storage, new power grids, and charging infrastructure.

Keywords： Battery; model; measurement; management; digitalized electrochemical power source

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本文引用格式

朱建功, 戴海峰, 王学远, 姜波, 魏学哲. 从电池管理到电化学数字电源*[J]. 电气工程学报, 2024, 19(1): 3-22 doi:10.11985/2024.01.002

ZHU Jiangong, DAI Haifeng, WANG Xueyuan, JIANG Bo, WEI Xuezhe. Battery Management towards the Digitalized Electrochemical Power Source[J]. Chinese Journal of Electrical Engineering, 2024, 19(1): 3-22 doi:10.11985/2024.01.002

1 引言

电源技术的第一阶段是基于电磁学原理，其典型技术是电机和变压器技术，电源技术的第二阶段是基于半导体原理，其典型技术是基于电力电子开关器件的电能变换技术。目前，为实现可持续发展，工业、交通、供热等各领域的电气化水平快速提高，同时光伏、风电等可再生能源电力系统装机量迅速增加。与石油等传统化石能源不同，电能无法直接储存，因此，包含储能和能量变换的电化学电源技术成为了推动能源绿色低碳转型的重要装备基础，对绿色交通、规模化储能、智慧电网等领域具有重要作用。可再生能源资源的能量变换如图1所示[1]，由于以风能和太阳能等为代表的可再生能源发电具有间歇性和不稳定性，储能系统和可再生能源制氢(绿氢)在新能源高效利用过程中具有重要的地位。在储能系统中，以电池为代表的电化学储能在现代电网“发、输、配、变、用”各个环节的作用已经逐步体现。燃料电池是氢能转换利用的重要装备，发展高效率、低成本的燃料电池是实现其产业应用的关键。国内外各大主要经济体均制定了相关产业快速稳定发展的长期规划[2⇓⇓-5]。由以上趋势可以看出，电源技术正在走向第三阶段，其典型特征是基于电化学原理，其核心技术是电池和燃料电池技术。

图1

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图1   可再生能源资源的能量变换途径[1]


电化学原理及电池已有两百余年的发展历史，早期的电池如铅酸电池、镍氢电池等，其功率小、容量小、产业规模小，不需要进行电池管理。自锂离子电池发明以来，随着电子设备的普及应用，电池管理的概念和技术得到了快速发展。当前面向新能源汽车及储能行业[6]，动力电池和燃料电池的规模实现了数量级的增长，其成组形成具有特定功能的电源，呈现出复杂的多物理场耦合特性。主要表现为以下几个特点：电化学及材料学方面，覆盖复杂的电化学、质量传递、热量及力传导等基本理论；电学方面，具有多串多并、高压、大电流等成组特性；热学方面，需要考虑散热和加热的热管理及热安全防护；机械方面，需要考虑尺寸、重量、受力等多设计因素；电力电子方面，需要电能变换及多种充电/放电控制策略的设计优化。

电化学电源在能量密度、功率密度、寿命特性、安全特性、宽温域特性等方面的电化学属性，导致其内部规律和外部特点不同于传统机电系统，具有非线性、时变性及空间分布等特点，同时其衰减和安全演变机理复杂，衰减机制存在强耦合及工况依赖性。因此，在应用过程中，需要进行有效管理。目前电池和燃料电池的管理需求包含状态估计、能量/功率预测、安全管理、均衡管理、水热管理等。另外，电池和燃料电池的全生命周期包含设计、生产制造、运行管理、回收/梯次利用及再制造等多个环节，各个环节往往独立设计，缺少面向全局的设计思路和技术体系[7]。

面对电化学电源多域、全生命周期设计和管理的需求，目前尚存在如下理论和技术困难：在理论模型上，缺乏可实用化的电化学机理模型及其内部参数的获取及映射方法，导致数据和模型在各个环节难以统一，无法实现电池模型的全生命周期贯通；在数据测量上，用于各个环节设计开发及管理的可测量数据存在维度较低、数据精度不足以及采样周期过大的问题，导致数据质量难以满足精准管理的需求；在应用管理上，当前依托数据实时收集与短期储存的本地管理方法难以服务电池的整个生命周期，缺少全局高效的调节手段及主动管理方法。

因此，本文以动力电池为切入点，探讨电化学电源的多物理场和多尺度等共性问题，提出了电化学数字电源概念并诠释其内涵，分别在模型、测量及管理三个层面探索基于数字化技术贯穿电化学电源全生命周期的管理方法。电化学数字电源与电化学材料、先进测量、电力电子、大数据、云计算、通信等技术相融合，助力绿色能源转型及能源高效利用。

2 电池的复杂特性

2.1 多物理场特性

最基本的锂离子电池反应单元由薄片状的负极、隔膜、正极以及电解液构成，如图2所示。其系统包含电子导体、离子导体及电子/离子混合导体。电池包含了丰富的电化学热力学和反应动力学理论，热力学性能如理论能量密度、电压平台及副反应等通过吉布斯自由能计算获取，处于热力学平衡状态的电极电势由于电极过程的发生被打破产生电流。电极过程主要包含电子/离子的传导、电极反应电荷转移过程、非法拉第过程和扩散过程，扩散过程包含离子在液相中的扩散以及固相活性材料晶格内的扩散。电子/离子传导、电化学反应过程和离子固液相扩散满足电荷守恒和物质守恒。充放电过程中，固相电极中电子传输发生在集流体和固相电极中，固相电压电流之间遵守欧姆定律。液相离子传输过程，表现为液相电解液中带电离子的传递，同样符合欧姆定律，但会受离子淌度和液相导电性的影响。电极反应中的电荷转移过程指在活性颗粒与电解液接触界面处，锂离子嵌入/脱出过程产生的电荷迁移，该处产生的电极反应电流，一般由Butler-Volmer方程[8⇓-10]进行描述。除电荷转移过程外，电极过程还包含了电解液与固相之间的电双层充电过程。电双层电容是由于固液两相界面处带电粒子非均匀分布而形成的类似电容的电双层结构。电双层充电过程为非法拉第过程，其通过的电流为非法拉第电流。引起非法拉第电流的因素是固液两相反应界面两侧的相对电位变化。电极反应的产物会在电极材料中发生固相扩散，一般用菲克第二定律进行描述[11-12]。除了电极过程，电池还具有质量传递、热量传导及结构变形等多种物理效应。在充放电过程中，电池的产热特性满足能量守恒定律，即电化学反应中产生的热量减去热传导散热之后用于电池升温。产热来源包括反应热(不可逆热)、熵热(可逆热)和焦耳热(欧姆热)[13]，热源综合作用下，形成电池的温度场。除了电化学特性和热特性，在电池工作过程中正极和负极活性颗粒内部会产生锂浓度差，锂离子在浓度梯度驱动下发生固相扩散，导致颗粒内部产生应变，在材料自身以及其他部件的约束下，应变会转化为相应的应力[14]，形成电池的应力场。电池的电化学场、温度场及应力场三者相互耦合关联，进行多物理场解析是了解电池电化学工作机制的重要手段。

图2

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图2   锂离子电池的原理示意图


2.2 多尺度特性

多尺度是电化学电源器件的另一个特性。电池的多尺度可以分为多时间尺度和多空间尺度。多时间尺度常用来描述电池内部不同电极过程的快慢，也用于描述电池短时间和长时间表现出来的性能变化。电池系统的多空间尺度描述如图3所示，可以分为材料、电极、单体及系统4个层面：① 电池内部包含活性材料、电解质及隔膜等关键材料，材料设计是电池设计的核心，通过合理的材料优化设计可以提升电化学电源器件的性能；② 电极是材料电化学特性的体现，其关键参数包含活性材料配比、颗粒半径、孔隙率及极片几何结构等；③ 单体层级上，电池具备空间分布特性，随着部件数量和体积增加，电化学、温度及力等多物理场耦合特性更加显著，表现为“机-电-热-流”耦合的特点；④ 系统层面上，单体成组后与电池管理系统(Battery management system, BMS)、热管理系统有机结合，应用于新能源汽车及储能系统等特定场景中。目前电池在4个层级上多采用独立研究的方式，进行各个部件的优化及性能提升。在层级关联上，由于计算的复杂性，一般采用均质化方法进行统一模型的建立。随着计算能力的提升，把连续介质的多物理场变量作为空间和时间的连续函数，进行从材料到系统的多尺度建模是当前的研究趋势，其中不同尺度下的参量获取及传递是实现多尺度建模及不同尺度管理的关键。其他电化学器件，如燃料电池、储能电池及电解池等，均具有高度相似的理论特性，其反应过程、共性参量及管控形式也具有相同的方式和方法。因此，可以认为电化学电源中问题的挖掘和求解方法具有统一性，电源设计和管理技术也具有统一性。

图3

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图3   电池系统的多空间尺度特性


3 电化学数字电源

电化学数字电源是综合电化学技术、电子技术及控制技术的数字化应用，旨在突破原有电化学电源设计及管控技术的范畴，形成全功能的电化学数字化的电源系统。从电池管理到电化学数字电源的升级可以总结为四个方面。

(1) 时间上，电池管理关注的是电池在应用过程中的行为，而电化学数字电源关注的是从设计到退役全生命周期中电池行为的数字化表达。

(2) 空间上，电池管理关注的是从电池单体到电池系统，电化学数字电源技术关注的是从电池材料、极片、单体、系统及电池的数字孪生，并适用于包括动力电池之外的其他电化学器件。

(3) 方法上，电池管理主要手段是以嵌入式技术为核心的电子技术，是基于电池外特性的管理，而电化学数字电源的手段包括电极内的电化学反应模型、电池系统的电、热、力等多维度模型，是基于全面电化学机理信息的管理。

(4) 功能上，电池管理关注的是电池本身的性能和功能，电化学数字电源关注的是以电源器件为核心，包括强弱电组成的充放电系统和水热力组成的机械系统在内的电源系统的整体性能和功能。

电化学电源数字化中的关键问题总结为以下三点：① 电化学电源的多物理场和多尺度形式化表达；② 电化学电源的多维和高精度测量；③ 电化学电源全生命周期寿命与安全优化控制。

本文提出的电化学数字电源包含模型、测量及管理三个方面的内涵，其相互作用关系如图4所示。在电化学数字电源中，器件的多物理场、多尺度特性通过模型进行形式化表达。对于动力电池来说，模型一方面用于电池设计，实现产品性能的快速优化提升，包含先进材料、智能电池及新型模组/包的设计和开发；另一方面，模型为管理方法的设计开发提供基础理论，如借助云侧高算力和存储能力，进行电池数字孪生模型的云侧部署应用。电化学数字电源需要具备高精度和多维度的参量测量，依托传感器的设计及应用，实现电化学电源器件的参量快速准确提取。对动力电池来说，测量一方面为车载BMS提供数据，进行本地的电池管理；另一方面，为边缘侧和云侧BMS提供大量数据并存储，实现基于全局信息的一体化管理。相关信息将传输至控制器，如整车控制、电机控制、电池管理控制、仪表等。电化学数字电源是在模型理论和多维参量测量的基础上，进行状态深度感知及反馈调控，在器件的全生命周期中实现状态估计、寿命与安全优化控制的方法论。下面本文按照模型、测量及管理三个层面的现状和趋势进行详细阐述。

图4

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图4   电化学数字电源的模型、测量及管理


4 模型

模型是物理世界的数字孪生，电化学电源器件是复杂的电化学系统，涵盖质量传递、电荷传递、热量传递以及多种电化学反应等物理化学过程，具有多维度、多层次及多形式的特点。从管理的角度上，按照观察视角的不同，电池目前分为三种典型的模型，即等效模型、机理模型和数据驱动模型。

基于端口及外部的测量建立的等效模型，也称为归纳性模型。电池的等效模型采用理想电压源与阻抗串联表示时，称为等效电路模型。电池端电压的上升与下降反映出电流流经电阻时电压响应的特点。典型的电池等效电路模型有Rint模型、PNGV模型、Thevenin模型与二阶RC模型等[15-16]。基于等效模型可以对电池的参数进行辨识，进而进行状态估计，实现在线应用[17]。CHARBONNEAU等[18]测量得到了锂离子电池在充放电循环过程中的电化学阻抗谱，并使用等效电路模型辨识得到了电双层电容、传荷阻抗和固相扩散系数。NASERI等[19]提出了一种等效电路模型，能够对电池的荷电状态(State of charge, SoC)进行估计，SoC估计的均方根误差低于0.95%。ZHANG等[20]建立了半电池的等效电路模型，使用参比电极将电池的正负极解耦，并采用扩展卡尔曼滤波算法实现了电池负极电位的实时估计，用于电池析锂行为的监测和预警。但是，使用等效模型对电池的参数和状态进行估计对模型结构形式和激励工况依赖度高。而且，不同的电池具有不同的电路参数，对电池进行状态估计时需要采用在线算法对电路参数进行实时更新，对算法的有效性和计算效率也提出了要求。此外，等效模型虽然具有结构简单、计算量小、反应迅速的特点，但因为其仅由电路元件组成，与真实电化学反应过程之间不存在显著的一一对应关系，导致对于同一个反应机理，电池可以呈现出不同的等效电路电压响应，同一种电压响应也可以由不同形式的等效电路来描述。因此，对于复杂的电化学体系，单纯地通过等效方法来对电池进行解释是十分困难的。要克服等效模型的缺陷，需要根据电极系统与电化学反应过程的特点，优化等效模型的结构，建立具有明确物理意义的数学模型。

机理模型是基于电化学、热力学、电极反应动力学等多种机理，通过输入电池电化学参数，实现多尺度和多维度的建模，也称为原理性模型。多孔电极理论[8,21⇓⇓ -24]从电荷平衡、物质平衡及电极动力学角度描述电极内部过程，基于多孔电极理论的模型及假设条件，研究人员建立了一系列具有代表性的电池机理模型[25⇓⇓-28]，用于电池设计及管理。例如，LI等[29]基于扩展单粒子模型开发了一种自适应无迹卡尔曼滤波器，可以实时对电池的荷电状态进行估计，同时估计锂离子的浓度和电势，从而描述电池的内部行为。WANG等[30]建立了包含电双层模型在内的电化学-热耦合模型，通过在电池不同循环中得到与健康状态相关的参数来进行电池的健康状态(State of health, SoH)估计。LEE等[31]对大尺寸磷酸铁锂电池建立了电化学-热耦合模型，以最小化电池内部温差为目标，对电池长宽比和正负极极耳的位置进行了优化，结果表明最优结构相比于初始结构，电池内部的温差减小了77.2%。多孔电极理论已经发展成为研究电极过程的重要基础理论[32⇓⇓⇓⇓-37]，但存在模型的输入参数多，且参数与温度、SoC、SoH等其他状态量具有强耦合的问题，电化学参数的准确获取具有很大挑战。对于难以直接测量的参数，通常基于测试数据，采用参数辨识的方法得到。这使得建立的机理模型只具有解释价值，而不具有预测价值。除此之外，目前基于多孔电极理论的电池模型采用均质化假设条件，但是电极具有空间异质性的微观特征，如材料内部的孔隙率和迂曲度等，显著影响电池内部离子的传输特性从而影响电池性能，所以采用均匀化假设的颗粒/界面离子传输特性会呈现出表达能力不足问题，不能仿真真实微结构对电池性能的影响。随着观测手段和数值仿真技术的发展，众多学者提出电池介观模型的研究路线[38⇓-40]，介观模型与电极的微观结构直接关联，能够表达电极的微观形貌，为机理模型的细化和拓展提供了新思路。

数据驱动模型是对数据进行分析，通过挖掘输入激励与输出响应之间的内在关联，建立逼近真实对象的统计模型，又称黑箱模型[41⇓-43]。在具备丰富可靠数据的基础上，数据驱动模型依靠大数据分析及先进计算可以为电池管理方法的创新设计提供新的可能。借助数据的方法一般有基于滤波技术[44⇓⇓-47]和基于机器学习[48⇓-50]的方法，通过建立特征参量与电化学状态之间的对应关系进行研究。基于滤波技术的方法是将滤波技术引入模型中形成闭环，目标是利用系统的动态模型和实时测量数据，来估计系统的未知状态，滤波方法的选择包括粒子滤波、卡尔曼滤波及其扩展和变种[44⇓⇓-47]。这种方法通常用于具有噪声和不确定性的系统，在电池状态估计等实际工作条件下应用较多。其中，YANG等[51]提出了一种基于粒子重采样策略的锂离子电池剩余可用寿命预测模型，将最优组合策略用于重新采样过程，以改善粒子滤波器的颗粒分布并保持其多样性。基于机器学习的方法直接从数据中寻找与电化学系统相关的特征及其演化规律来对未知状态进行预测，具有无需构建显性数学模型的特点[52]。常用的机器学习方法包括决策树模型[49]、支持向量机[53]和神经网络[42]等。机器学习方法不要求理解内部机理，只需要从宏观特征参数中提取有效信息，具有简化分析的优势，近年来在电池寿命评估和预测等方面获得应用。例如，CHEN等[54]提出了一种基于深度学习的锂离子电池寿命预测方法，寿命模型由二维和一维并行混合神经网络建立，该方法在多循环工况下具有较强的泛化能力。但是数据驱动方法存在的对数据质量要求高、对数据量要求大、缺乏解释性的问题，是制约其发展应用的因素。

除了上述三类典型模型，电池的建模还涉及电极材料的微观尺度。电极的微观尺度包括原子排列和晶格结构，它们控制着电极材料的物理和化学性质，对电池性能会产生直接影响。目前在微观尺度上采用的建模方法包括第一性原理计算[55⇓⇓-58]、分子动力学模拟[59⇓⇓-62]和蒙特卡洛方法[63⇓⇓-66]。第一性原理计算基于量子力学基本原理，通过薛定谔方程来描述原子核和电子之间的相互作用；分子动力学模拟是基于经典力学，通过跟踪原子和分子在特定温度下的轨迹来模拟材料的动力学；蒙特卡洛方法是一种统计模拟技术，通过利用随机抽样和概率分布有效地捕捉系统的演化。这类方法提供了对电极材料中原子行为的详细描述，能够阐明包括离子传输、力学性能和热力学等的关键材料特性。在电极材料的微观尺度上建模可以揭示电池中的离子和电子运动规律，有助于了解电池的工作原理和性能限制，帮助研究和设计新型电极材料、电解液和隔膜等，进而提升电池的能量密度、功率密度和稳定性。然而，在微观尺度上进行建模仍存在诸多困难，例如微观尺度建模需要更详细和精确的材料参数和初始条件，这些数据的获取存在很大的困难和不确定性；另外，电极的微观尺度涉及复杂的数学和物理方程，模型的运行需要大量的计算资源和时间成本。

5 测量

精准测量工作过程中的参量是实现电化学电源稳定工作和可靠管理的关键手段。由于电池具有多片、平板及堆叠等分布式特征，仅依靠单一参量的测量不能反映其内部电流密度、压力、温度、气体分布等多种物理量的变化。进行多维信号的测量感知是电源设计与管理的发展趋势[67]，也是提升动力电池稳定性和可靠性的关键技术路线之一[68]。因此，电化学数字电源的发展一方面需要进行已有参量的测量精度和采样速度的提升；另一方面需要依托电力电子技术，进行新参量的获取，增加信号的维度；二者共同实现电化学电源对测量的需求。目前，交流阻抗[69]、电池内部温度[70]、力[71]及气体[72]等新参量逐步得到关注。结合电子技术的发展，信号测量维度的增加主要借助电池传感技术实现，目标是实现具有自我测量能力的智能电池[73]。本文从电化学电源器件的电、热、力及气参量分别进行现状及发展趋势的阐述。

5.1 电压/电流

目前用于电池管理的常规电信号主要有电压和电流。电压是电化学电源器件的基本参量，随着器件规模化的发展，对电压采样尤为重要。表1是目前电源管理芯片在电压信号采样方面的性能对比。NXP的MC33771的电压测量具备较高的精度，但无法兼顾采样速度。LTC6811在27 kHz模式的最大噪声为4.7 mV，为了保证测量精度需要对采集到的信号进行校准。提升电信号测量的精度和速度是模拟前端采样的目标之一。电池管理中的电流测量主要针对每串电池或者整个电池系统的充放电电流进行测量。使用较多的传感器是霍尔式电流传感器，一般满足-2 000~2 000 A量程。上述传感器的设计和开发均较为成熟，发展趋势是实现信号更高频率和更高精度的采样。对于电压信号，三电极电池可以分别检测到正、负极相对于参比电极的电位，进而将正极对负极的电压信号进行解耦，是目前的研究热点。准确可靠的正/负极对参比电极电位信息在指导电池材料设计、诊断电池析锂、正负极失效模式分析、快充设计等方面具有重要意义[74]。参比电极的稳定性是三电极电池的关键。锂(Li)金属具有快速的反应动力学，最常被用作锂离子电池中的参比电极[75]，但其容易发生溶解，使用寿命较短。Li合金，如Li-Al、Li-Au[76]等，化学性质更加稳定，使用寿命相对更长。LiFePO4[77-78]和Li4Ti5O12[79]被认为是更具潜力的参比电极，其具有稳定的电压平台，良好的空气耐受性便于制造，同时具有更长久的使用寿命。但是随着参比电极电池长时间搁置或工作，插层氧化物中的锂离子析出，在实际使用过程中需要定期对LiFePO4和Li4Ti5O12进行镀锂，以校准其电位。大规模商用的参比电极需要在电池全生命周期内稳定运行，需要具备高度稳定性和长寿命的特点。能满足电池的循环寿命和能量密度的三电极电池设计是目前电压信号测量的方向之一。

表1   常用电池管理采样芯片

芯片型号AD位数最大
通道数		通信方式AD采样精度转换开始至结果可读
所有通道总时间/μs		所有通道数据
传输时间/μs		最大采样
频率/Hz
MAX14920
(Maxim)		16161 Mbps SPI1.6 mV, f≤285 Hz
1.6 V, f≥1 kHz		8 000
(含校准时间)		256121
LTC6811
(ADI)		16121 Mbps SPI1.2 mV, f≤7 kHz
4.7 mV, f≥14 kHz		1 564
(含校准时间)		192569
MC33771(NXP)16144 Mbps SPI0.8 mV520561 736

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5.2 交流阻抗测量

交流阻抗被广泛接受是表征电化学界面反应及离子传输的重要参量，目前在电池管理技术的开发中被广泛研究[80]。主要思想是研究交流阻抗与SoH、温度和SoC之间的对应关系，利用该关系实现对状态的有效估计[81]。实验室中一般采用电化学工作站进行交流阻抗测试，该方法受到成本和设备体积限制，难以进行车载应用。目前研究中采用高精度和同步性电压、电流采样构建电池的扫频激励，也有研究采用时频变换方法进行阻抗的计算。按照激励方式，交流阻抗的测量分为集中式激励和分布式激励，二者的测量原理如图5所示。阿拉巴马大学[82]、南洋理工大学和慕尼黑工业大学[83]、康涅狄格大学[84]以及同济大学[85]提出的阻抗测量系统的集中式激励方案如图5a所示。其特点均是利用与电池相连接的开关电源实现集中激励信号的产生。分布式激励[82,86]是利用了电池单体的主动均衡电路来实现，通过不同单体电池之间在电能转移瞬间所产生的激励电流实现阻抗的测量。分布式激励需要针对每一个电池单体设计激励电路和电流测量电路，如图5b所示，考虑到分布激励方式的分离器件多带来的体积、成本和可靠性问题，具有单芯片解决方案的阻抗测量系统是当前阻抗用于电池管理的研究趋势[87]。

图5

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图5   电池的电化学阻抗测量


5.3 表面/内部温度测量

温度是电池管理研究中的重要参数[88⇓⇓-91]。外部温度测量多采用热电耦和电阻温度探测器(Resistance temperature detector, RTD)。PANCHAL等[92]将多个热电偶附着在方形电池表面，研究了不同操作条件下的电池表面温度分布。LI等[93]设计了一个多传感测试平台，研究振动过程中电池性能退化，其中采用RTD传感器检测电池表面温度。近年来，光纤布拉格光栅(Fiber bragg grating，FBG)因其独有的小尺寸、绝缘、耐腐蚀、多路复用等特点，在温度测量领域得到广泛应用。NASCIMENTO等[94]同时采用FBG传感器和K型热电偶研究了正常和滥用操作条件下电池表面温度演变规律。研究发现FBG传感器比K型热电偶具有更好的分辨率和响应速度。虽然电池表面温度较容易进行检测，但是电化学电源系统的热量由内而外进行传递，表面温度与内部温度具有一定差距，单纯依靠表面温度测量难以保证系统的使用安全性。对电池内部温度的获取可以总结为间接估计法和直接测量法。对于间接估计法，一种方式是利用内外温差构建传递函数和温度估计模型，利用产热率构建内外温差之间函数关系[95-96]。也有基于电池阻抗特性进行温度估计的方法被提出[97⇓⇓⇓-101]，通过建立阻抗特征参数与内部温度之间的关系，使用特征参数如阻抗幅值和阻抗相角进行温度估计。基于阻抗测量进行温度估计的方法需要依靠阻抗在线测量装置[102⇓⇓-105]进行车载应用。对于直接测量法，RICHARDSON等[96,104,106 -107]在动力电池内部埋设热电偶，从而直接测量动力电池内部真实温度。该方法虽然能直接准确地测得动力电池内部温度，但易破坏动力电池内部结构，对动力电池造成永久性损伤。YANG等[51]通过将松散排列的直径为150 μm的并联单模FBG传感器和具有微结构的FBG插入到18650电池的卷芯中间，将压力和温度解耦获取电池内部温度，如图6a所示。ZHU等[108]开发了一种将多点RTD传感器与电极集成的新方法，通过刮除电极表面部分活性材料并安装薄膜RTD，如图6b所示，从而在不破坏电池内部主体结构的同时实现对电池内部多点温度的准确测量。

图6

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图6   电池内部温度及力参量测量


5.4 力参量测量

电池工作过程中极片的呼吸效应会导致电池产生可逆/不可逆的体积变化，进而引起极片间压力增加和极片间应力分布不均问题，表现为运行过程中的应变/应力变化。极片间的压力变化会通过相邻极片逐步传递至相邻单体，因此力信号可被视为表征电池状态的重要指标。研究学者提出多种应变和应力的检测设备和方法[109-110]，RAGHAVAN等[111-112]将FBG嵌入石墨负极的表面，通过温度和应变的解耦，得到了石墨负极表面应变随SoC和循环老化的演变，如图6c所示。并且通过机器学习方法，实现了基于应变的SoC和SoH估计。HU等[113]利用压电/热电聚偏氟乙烯-三氟乙烯(PVDF-TrFE)、薄膜晶体管阵列及原位极化工艺制备了一种新型锂离子电池健康监测传感器阵列，将该传感器贴合在电芯表面并用铝塑膜封装，如图6d所示，用于监测电池动态机械损伤和准静态损伤。牛少军等[114]研究了锂离子电池硅基负极循环过程中的膨胀应力，发现电池循环中电池表面压力的增长和容量衰减之间为线性相关。MIAO等[115]通过光纤传感器原位监测了三种不同转换机理的软包锂硫(Li-S)电池中正极应力的演变过程，系统地监测了硫基正极在三种不同机制下内部的化学-力学应力演化。在气体压力测量方面，MATASSO等[116]开发了一个原位圆柱电池的内部气体压力监测系统，将电池密封在测试系统后，用针从系统顶部拧入对电池进行开口，用压力传感器监测内部气体压力演变情况，如图6e所示，并用两种类型的电池证明了该压力监测系统对电池循环性能影响很小。该团队还使用此系统检测到电池在循环过程中压力上升和容量衰减呈现很强的相关性[117-118]。LEE等[31]将气体压力传感器集成于电池上盖，设计了嵌入气体压力传感的方形电池，实现了电池内部气体压力的原位测量，如图6f所示，分析了气体压力随SoC和循环次数的非线性关系。上述传感及测量技术的发展为力信号的采集及其在电池管理技术上的应用提供了基础。

5.5 气体测量

电池在失效过程中会释放气体，气体成分及含量能有效表征过充/过放、衰减及热失控等安全失效[119-120]，定量表征及分析电池产气成分和含量是研究电池气体信号的关键步骤。如表2所示，气体成分的综合性定量表征多借助材料学表征手段，可以分为非原位和原位的表征分析方法。非原位方法主要有气相色谱质谱法(Gas chromatography-mass spectrometry, GC-MS)、傅里叶变换红外光谱法(Fourier transform-infrared spectroscopy, FT-IR)和核磁共振光谱(Nuclear magnetic resonance, NMR)。原位表征方法采用在线/微分电化学质谱技术(Online or differential electrochemical mass spectrometry, OEMS or DEMS)、原位拉曼光谱(In-situ Raman spectra, IRS)和非色散红外气体传感器(Nondispersive infrared gas sensors, NDIR)监测气体成分随电位和时间演变情况。KONG等[121]将LiCoO2、LiMn2O4和LiFePO4三种正极材料的18650锂离子电池正常充电和过充至4.5 V和5.0 V，然后用注射器收集电池内部的气体，并采用GC-MS测量了气体成分，如7a所示。研究发现，在正常充电条件下，气体发生行为与正极材料类型无关。在过充条件下，正极材料的氧化能力对气体种类和数量有显著影响。O/DEMS可以将电化学反应池(图7b)与质谱仪联用，实时检测电化学反应界面消耗或产生的气体和挥发性中间产物及最终产物，并进行定性和定量分析。GALUSHKIN等[122]使用OEMS监测了NMC111电池在不同上截止电压和温度条件下循环过程中正负极产气的情况。结果表明电解质分解会产生CO和H2气体，而CO2只在正极生成，是正极原子晶格释放的O2与正极附近CO(电解液分解)反应的产物。GERELT-OD等[123]开发了用于电池分析的IRS分析系统，如图7c所示，其在商业电池上安装玻璃窗进行激光散射，因此可以无干扰地跟踪电池内部电化学反应。研究结果表明，满电状态的18650电池在25~45 ℃环境下存储，会逐渐产生H2、CH4、CO2和CO四种主要气体，过量的H2使电池存在安全隐患。LYU等[72]开发了基于NDIR的气体成分监测装置，将CO2、CH4和C2H4的NDIR气体传感器和开口的商业电池共同放在密封罐中，如图7d所示，监测电池运行时三种气体的演化情况。结果表明，高电压会导致CO2生成量增加，而CH4和C2H4的生成量对温度更敏感。上述装置可以监测商业电池气体的发生，但是都需要连接大型的气体解析仪器，多用于机理解析。

表2   监测电池气体成分方法对比

名称是否原位探测是否监测商用电池电压相关精度/
(×10-6)		分辨率/
(×10-6)		响应时间/s监测气体类型
气相色谱质谱法×××100.15H2，O2，CO，CO2，CH4，C2H4，挥发性有机(VOCs)；非挥发性气体除外
傅里叶变换红外光谱法×××——<10-6CO，CO2，CH4，C2H4，VOCs;
单原子和同核分子除外，如He，H2
核磁共振光谱×××——>10-4H2，O2，CO，CO2，CH4，C2H4等
在线/微分电化学质谱技术√×√100.15H2，O2，CO，CO2，CH4，C2H4，VOCs等；惰性气体除外
原位拉曼光谱√√×——<10-6CH4，CO2，CO；
H2、惰性气体除外
非色散红外气体传感器√√√50120H2，CO，CO2，CH4，C2H4；
O2、惰性气体除外

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图7

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图7   气体成分测量


为直接采集电池内部气体，有研究通过对电池壳体进行设计，在电池壳体增加取气口，在不影响电池的动态工作过程的前提下，实现多次取气及检测分析，进而实现在长时间尺度上监测气体成分的演变。WANG等[124]设计了一个原位气体监测装置，其中电池内部通过导管连接四通阀，四通阀与压力传感器、气体采样口、真空阀连接，可随时记录电池内部压力，也可以通过气密针随时采集气体样品，分析气体组分，如图7e所示。基于此方法该学者研究了钛酸锂电池在55 ℃循环过程和55 ℃搁置工况下内部压力、胀气体积以及各组分气体含量的变化规律，并推导了可能的产气反应。SCHMIEGEL等[125]设计了带气体取样口的软包锂离子电池，其中单向气体取样口由鲁尔接口、GC进气垫和GC采样瓶盖组成并通过PP导管连接电池内部，如图7f所示，通过单向取样口多次取气研究了单次充放电循环间气体组分的演变。上述试验装置能获取电池工作过程中的气体，借助材料表征技术离线监测商业电池内部气体成分的演化。在保证原位测量装置稳定性和可靠性的前提下，如接口的耐腐蚀性、密闭性等，配合微型气体传感器的设计开发，为电池内部特定气体成分的在线监测提供可能。

5.6 其他参量

除上述信号之外，有研究将声学信号与电极材料的特性产生关联，用于电池内部信息检测，如故障诊断、状态估计、电解液浸润状态等[68]。声学信号可进一步分为声发射技术和超声表征技术。CHOE等[126]采用声发射技术，对LiCoO2/C扣式电池开展了声发射探测。DAVIES等[127]对LiCoO2/C和LiFePO4/C两种材料的软包电池的充放电过程进行了超声表征，采用机器学习方法实现了SoC和SoH的估计。为了实现声发射技术以及超声表征技术的有效传导声波，目前需要借助液体耦合剂(例如硅油)来完成。未来，随着干耦合剂技术的不断发展，声学传感技术有机会在车载领域发挥作用[128]。

5.7 电池处理单元

当前电池管理中主要通过模拟前端完成数据采集并发送至主控制器完成测量，其测量参量主要包括电压、电流、外部温度。为了实现上述电化学数字电源对多维测量参量的采集速度和精度的需求，提高数据处理能力，需要电池处理单元(Battery process unit，BPU)为电化学电源的数字化提供相应的硬件基础，BPU的功能是将多参量的测量、数据处理与计算、数据存储等功能进行集成，并承担本地管理等任务，成为电源系统的域控制器。

6 管理

电池管理系统用于对电池应用过程中的参数和状态进行实时监控，包括电池数据采集、数据处理和预测功能，保证系统安全可靠运行。初代电池管理系统采用阈值保护，实现电池不过压、不过流、不过热的基础功能。第二代电池管理以SoC估计为核心，采取模块化的集合，对电池物理参数(如电流、电压和温度)进行实时监测，实现电池状态估计、在线诊断与预警、充/放电与预充控制、均衡管理和热管理等且具有很强的扩展性。第三代管理系统是在第二代电池管理技术的软硬件基础上，响应当前电池的长寿命和高安全目标，进行电池系统全局的设计优化，比如采用集中式架构实现电池管理，相关技术目前仍在发展中。下面从本地管理和云边端一体化管理展开论述。

6.1 本地管理

当前电池管理重点围绕本地对电池单体和电池组进行检测和控制。电池管理系统通常分为两个功能模块：电池组监测模块和电池组控制模块，通过一体式、主从式或分布式架构实现。当前电池管理的测量方面具备电压、电流及表面温度的测量，信号通信及数据的实时分析，实现对被测对象的预警/报警及故障定位。数据采集方式由最初的分立器件发展为模拟前端的集成芯片。管理系统根据测得的信号和给定的算法模型实现电池的状态监控、热管理以及电池均衡、故障诊断与处理、信号通信等功能。管理系统要求一定测量精度以满足功能需求，上海交通大学[129]研发的用于某混合动力大客车上的电池管理系统可以对电池性能参数实现精确测量，包括模块电压、总电压、电流、温度等，可以实现基于安时法的SoC估计及故障诊断。湖南大学[130]研发了用于燃料电池混合大客车的电池管理系统，电流检测精度为相对误差0.2%，温度检测精度可以达到±0.5 ℃，电压试验误差±(3~5) mV，短时试验工况下的静态SoC偏差基本在5%之内。通信方式由CAN通信、菊花链通信，发展到无线通信，美国Analog Devices公司推出无线电池管理系统，在不影响功能的基础上，节约90%线束和约15%的电池组体积，提高了设计灵活性和可制造性。通过无线数据构建数据库系统，实现动力电池全生命周期数据监控。本地管理的可靠性方面，结合现代大规模集成电路技术，利用电力电子变流技术实现调控。在数据库管理方面，配备数据库管理系统，对性能进行评价，为优化设计提供数据支持。

常规电池组由串联的电池模块组成，电池模块由多个串联和并联的电池组成，即电池、模组、包(Cell, Module, Pack, CMP)模式。同济大学[131]研发的用于“领驭”系列燃料电池轿车的锂离子动力电池管理系统采用分层结构，根据各层功能的不同分为中央电池管理单元(Central electronic control unit，CECU)和本地电池管理单元(Local electronic control unit，LECU)两层。为了提升电池能量密度，直接将单体组装成电池组的无模组Cell To Pack (CTP)技术应运而生，例如宁德时代、斯沃特以及比亚迪的刀片电池技术。该技术能够更好地利用电池组中电池的安装空间，原本在模组分布实现的功能可以在系统集中实现。目前，特斯拉、比亚迪、零跑等提出了将单体直接集成到底盘的Cell To Chassis (CTC)技术，这将使得汽车底盘的集成化和模块化程度更高，使得本地管理架构能够从分布式向由域控制器及中央控制器构成的集中式架构演变。

6.2 云边端一体化管理

本地管理方式受限于本地存储能力和计算能力，随着信息技术和电力电子技术的进步，新能源汽车及储能系统电气化和智能化程度增加，在运行过程中将会产生大量结构化和半结构化数据[132]，如整车状态数据、电池系统数据、电机数据等，在国家大数据战略的背景下，新能源汽车与大数据的融合发展成为汽车工业新的发展契机。“云管理”“云边端一体化管理”等方法和思想应运而生。云管理是在云中进行云计算和服务。云边端一体化是在资源一体化的基础上，对云侧、边缘侧和终端侧三部分进行合理分配和调度，形成一体化协同的效果，该概念已经广泛用于各个领域的发展。我国工业和信息化部建立了新能源汽车国家监测与管理平台和新能源汽车国家检测与动力蓄电池回收利用溯源管理平台，平台用于构建动力电池关键数据集，为进行信息监管并实现车用动力电池的全寿命周期管理提供支撑。随着通信技术和云数据平台的发展，依赖大量数据与存储空间的基于数据和模型进行电池云管理的算法实现具备可能性。如洪吉超等[133-134]基于实车运行监控大数据，提取新能源汽车每次行驶与充电过程的累计里程、总电压、电流、温度、SoC、车速等数据对动力电池系统进行状态评估，如图8a所示。YANG等[135]提出构建基于数字孪生技术的云管理技术平台——赛博链(Cyber hierarchy and interactional network, CHAIN)，进行电池全生命周期管理。如图8b所示，赛博链的核心思路是构建与物理电池系统映射的虚拟电池系统，从云侧实现微观演化与安全评价，反馈至物理实体更新管理策略，电池的物理实体和数字虚拟模型协同联动。WANG等[136]也提出了类似的面向未来电池管理系统的数字孪生技术和云边端协同的构想。图8c为ZHANG等[137]提出的元能源系统(Meta-Energy)，在数字网络空间中，基于分布式数据处理和存储技术实现各个节点共享，通过复杂可控的网络相互连接数字孪生系统，实现能源供需预测、能源调度和需求响应等功能。

图8

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图8   几种基于云数据平台的管控思路


近年来，云服务器的发展为电化学电源的云边端一体化管理落地提供了可能性。亚马逊、谷歌和微软等都提供了公共计算资源的访问，电动汽车和储能行业也正着力于运用云计算，博世、松下、华为等都推出了基于云的软件。博世公司提出将无线车端电池管理系统收集的数据上传至云，通过云大数据算法，实现数据在线处理和各模块联通，进而提升电池性能并降低成本。松下提出的通用电池管理云服务致力于识别电池状态并优化电池运行。华为构建云端智能预警系统，提出AI BMS，从机理出发，融合大数据和AI能力，基于云侧海量数据，构建电池热失控多物理场数字孪生模型，进行电池全生命周期的故障预警和动态管理。

云边端一体化管理需要基于终端多参量采集、大数据、边缘计算、人工智能及云计算等技术，通过积累长期大规模数据捕获全局变化，同时需要具备模型和参数的更新能力，实现数字孪生。云侧通过数据的标定可以构建新的算法、新的模型，并定期下载到终端或者边缘侧，使得本地管理系统具备了全生命周期的精度、适应性和算法快速迭代能力。当前车辆电池系统实际使用过程产生的大数据具有数据规模大但密度低的特点，因此，电池数据的多维采集和预处理是数字化方法和云边端一体化管理的难点。

本文认为云边端一体化管理适用于电化学数字电源的管理。端侧管理，即本地管理，通过多维测量，实现数据的实时收集与短期储存，并进行实时管理；边缘侧用于中长期数据的存储和管理；云侧专注于整个生命周期中器件的主要参数的提取、长期存储及计算。云边端一体化协同，保证短期的数据精度，也确保了长期的数据广度，形成优势互补。通过端侧多维度的测量获取机、电、热、流多域信息，边缘侧和云侧协同并结合高性能计算，实现对象的智能管理、预警、故障告警、数据挖掘、健康评估、安全预警等管理需求。

7 电化学数字电源的拓展

7.1 在电化学储能的应用

随着电气化和电力低碳化的发展趋势，储能系统成为新型电力系统转型的基石之一，其中电化学储能是储能系统的关键路线，如图9a所示，为增强电力系统的调节能力，电化学储能系统布局在用电侧、输配电侧及发电侧。一般来说，电化学储能系统主要由电池组、电池管理系统、能量管理系统、储能变流器以及其他电气设备构成。电池组是储能系统最主要的构成部分，与动力电池相比，其规模及存储量更大。其电池管理与动力电池BMS的很多功能保持一致，主要负责电池的监测、评估、保护以及均衡等。在储能系统工作工程中，同样需要实时监测电池的状态，进行电池容量精准估计、电池的充放电管理及安全管控等。由于电化学储能系统与动力电池的高度相似性，本文提出的电化学数字电源方法论适用于储能电池组及其管理系统，基于电池模型及深度测量，采用本地和云/边结合的电池管控方法来监控电池状态，保证储能电池的安全稳定运行。

图9

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图9   电化学数字电源的拓展应用


7.2 在新能源汽车复合电源的应用

燃料电池与动力电池具有相似的电化学特性，也存在非线性、时变性及空间分布的问题。燃料电池和动力电池互补后的特性接近于理想电源，形成新能源汽车的复合电源系统，如图9b所示。电化学数字电源方法论同样适用于复合电源，由于复合电源存在两种电源的深度耦合，在数字化管理上还需要考虑两个方面：一是基于数字控制的电流变换技术，其本质是利用高频开关将电流离散化，新一代电力电子技术的快速发展为电流变换技术开辟了全新的思路，为模块化的电源设计和柔性化的动态组合提供了设计基础；二是面向车用复杂工况的复合电源控制需要结合车辆智能驾驶技术，充分挖掘周围环境等云/边侧信息支撑车辆工况的可预测性，实现复合电源系统的可预测控制。

8 结论与展望

随着新能源汽车及储能场景使用规模的迅速扩大，局限于本地的管理难以满足电化学电源器件的多场景管控需求。随着电化学材料、先进测量、电力电子、大数据、云计算及通信等技术的进步，本文以动力电池为切入点，提出电化学数字电源的概念，分别从模型、测量及管理三个层面对其内涵进行阐述。在电化学数字电源中，电源器件的多物理场、多尺度特性通过模型进行形式化表达，模型一方面用于电源器件的设计，实现产品性能的快速优化；另一方面，模型为管理方法的设计开发提供基础理论，借助云/边侧高算力和存储能力的优势，进行数字孪生模型的云/边侧部署应用。电化学数字电源具备高精度和多维度的参量测量，依托传感器发展应用，实现参量的快速准确提取。测量结果一方面用于本地管理，另一方面，为云/边管控提供大量数据，实现基于全局信息的一体化管理。本地管理通过多维测量，实现数据的实时收集与短期储存，云/边侧专注于中长期电源器件的主要参数，本地和云/边协同保证短期的数据精度，也确保长期的数据广度，两者形成优势互补。电化学数字电源的管理将通过多维度的测量获取机、电、热、流多域信息，本地和云/边协同并结合高性能计算，实现电化学电源的全生命周期寿命优化与安全控制。电化学数字电源将是响应电动化、智能化、网联化的重要方法论，是推进新能源汽车、氢电储能、新型电网、充电基础设施等电化学电源应用发展的重要支撑。

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[J]. Nature Communications, 2020, 11(1):2079.

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Driving range and fast charge capability of electric vehicles are heavily dependent on the 3D microstructure of lithium-ion batteries (LiBs) and substantial fundamental research is required to optimise electrode design for specific operating conditions. Here we have developed a full microstructure-resolved 3D model using a novel X-ray nano-computed tomography (CT) dual-scan superimposition technique that captures features of the carbon-binder domain. This elucidates how LiB performance is markedly affected by microstructural heterogeneities, particularly under high rate conditions. The elongated shape and wide size distribution of the active particles not only affect the lithium-ion transport but also lead to a heterogeneous current distribution and non-uniform lithiation between particles and along the through-thickness direction. Building on these insights, we propose and compare potential graded-microstructure designs for next-generation battery electrodes. To guide manufacturing of electrode architectures, in-situ X-ray CT is shown to reliably reveal the porosity and tortuosity changes with incremental calendering steps.

KASHKOOLI A G, FARHAD S, LEE D U, et al.

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程昀, 李劼, 贾明, 等.

锂离子电池多尺度数值模型的应用现状及发展前景

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CHENG Yun, LI Jie, JIA Ming, et al.

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ZHANG Y, TANG Q, ZHANG Y, et al.

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[J]. Nature Communications, 2020, 11(1):1706.

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Forecasting the state of health and remaining useful life of Li-ion batteries is an unsolved challenge that limits technologies such as consumer electronics and electric vehicles. Here, we build an accurate battery forecasting system by combining electrochemical impedance spectroscopy (EIS)-a real-time, non-invasive and information-rich measurement that is hitherto underused in battery diagnosis-with Gaussian process machine learning. Over 20,000 EIS spectra of commercial Li-ion batteries are collected at different states of health, states of charge and temperatures-the largest dataset to our knowledge of its kind. Our Gaussian process model takes the entire spectrum as input, without further feature engineering, and automatically determines which spectral features predict degradation. Our model accurately predicts the remaining useful life, even without complete knowledge of past operating conditions of the battery. Our results demonstrate the value of EIS signals in battery management systems.

WU Y, LI W, WANG Y, et al.

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[J]. IEEE Access, 2019,7:54843-54854.

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HU C, YE H, JAIN G, et al.

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[本文引用: 2]

张凝, 徐皑冬, 王锴, 等.

基于粒子滤波算法的锂离子电池剩余寿命预测方法研究

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ZHANG Ning, XU Aidong, WANG Kai, et al.

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ZHANG H, MIAO Q, ZHANG X, et al.

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HE W, WILLIARD N, OSTERMAN M, et al.

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SEVERSON K A, ATTIA P M, JIN N, et al.

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[J]. Nature Energy, 2019, 4(5):383-391.

DOI:10.1038/s41560-019-0356-8      [本文引用: 1]

Accurately predicting the lifetime of complex, nonlinear systems such as lithium-ion batteries is critical for accelerating technology development. However, diverse aging mechanisms, significant device variability and dynamic operating conditions have remained major challenges. We generate a comprehensive dataset consisting of 124 commercial lithium iron phosphate/graphite cells cycled under fast-charging conditions, with widely varying cycle lives ranging from 150 to 2,300 cycles. Using discharge voltage curves from early cycles yet to exhibit capacity degradation, we apply machine-learning tools to both predict and classify cells by cycle life. Our best models achieve 9.1% test error for quantitatively predicting cycle life using the first 100 cycles (exhibiting a median increase of 0.2% from initial capacity) and 4.9% test error using the first 5 cycles for classifying cycle life into two groups. This work highlights the promise of combining deliberate data generation with data-driven modelling to predict the behaviour of complex dynamical systems.

HUANG Y. Data-driven capacity estimation and cycle life prediction of lithium-ion batteries[D]. Karlsruhe: Karlsruhe Institute of Technology, 2020.

[本文引用: 2]

庞晓青, 李佳承, 刘文学, 等.

模型与数据融合的大尺寸快充锂电池分布式温度估计

[J]. 机械工程学报, 2023, 59(22):20-32.

DOI:10.3901/JME.2023.22.020      [本文引用: 1]

充电时间长、续航里程短、热安全性差等问题正成为阻碍电动汽车大规模应用的主要因素。高安全无损快充和高比能电池技术正成为发展趋势，具体表现为充电功率快速和电池单体大型化提高。然而，大尺寸锂离子电池的温度不一致性问题明显，高功率充电容易导致电池温度快速升高甚至引发热失控。因此，针对快充场景，开发准确高效的温度估计方法尤为重要。针对软包型锂离子电池的分布式温度估计问题，提出一种基于长短期记忆神经网络(Long short-term memory， LSTM)与产热模型融合的方法，并在 5～40 ℃的宽温度区间以及多种快充场景下验证了方法的适用性。该方法仅基于一个温度测点的温度信息即可准确估计电池平面其余多个关键测点的温度，可近似获取大尺寸电池二维的温度分布情况，包括最高温度和最大温差，还能有效降低电池成组时的传感器布置成本。融合模型将产热模型结果作为 LSTM 模型输入，讨论不同温度测点以及不同产热模型作为输入对模型精度的影响。结果表明，温度测点的选择对模型精度影响明显，相比于正极极耳温度，将正极极耳与电池本体连接处温度作为输入，其他测点的估计方均根误差可降低 50%，最大误差仅为 0.239 ℃。相较于无产热输入，选择接近电池分布式产热情况的产热模型，估计值方均根误差可减小约 11%。

PANG Xiaoqing, LI Jiacheng, LIU Wenxue, et al.

Estimation of distributed temperature of large-format fast-charging lithium-ion batteries based on a model-data fusion method

[J]. Journal of Mechanical Engineering, 2023, 59(22):20-32.

DOI:10.3901/JME.2023.22.020      [本文引用: 1]

Problems such as long charging time, short mileage, and poor thermal safety are becoming the major factors hindering the large-scale penetration of electric vehicles. High-specific-energy battery technologies and high-safety and damage-free fast charging are becoming popular, reflecting in the larger-sized battery cell and rapidly increased charging power. However, the temperature unevenness within the large-format lithium-ion battery is evident and high-power charging is prone to cause a rapidly elevated temperature and even thermal runaways. Therefore, it is of great importance to propose an accurate and efficient temperature estimation method for fast-charging batteries. A fusion method integrating a long short-term memory(LSTM) network and a heat generation model is proposed for estimating the distributed temperature of pouch-type lithium-ion batteries. The generalizability of the method is verified in a wide temperature ranging from 5 to 40 °C and a variety of fast charging scenarios. This method accurately estimates the temperatures of remaining key measurement locations on the battery plane based on the temperature information of only one measurement location. It can not only obtain the approximate two-dimensional temperature distribution of large-format batteries, including the highest temperature and maximum temperature difference, but greatly reduce the cost of temperature sensors when battery grouping. The fusion model takes the output of the heat generation model as the input of the LSTM model, and the effects of the measurement location and heat generation model on the fusion model’s accuracy are discussed. The results show that the selection of temperature measurement locations has a great impact on the prediction accuracy of the model. Compared to the positive tab temperature, taking the temperature measurement at the connection of the positive tab and main battery body as the input can reduce the root mean square error(RMSE) of the remaining measurement locations by 50%, and the maximum RMSE is only 0.239 °C. Compared to the model without heat generation as its input, the heat generation model optimally simulating distributed heat generation of the battery can reduce the RMSE of the model by about 11%.

YANG J, FANG W, CHEN J, et al.

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[J]. Journal of Energy Storage, 2022,55:105648.

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KANUNGO S, BHATTACHARJEE A, BAHADURSHA N, et al.

Comparative analysis of LiMPO4 (M= Fe,Co,Cr,Mn,V) as cathode materials for lithium-ion battery applications-A first-principle-based theoretical approach

[J]. Nanomaterials (Basel), 2022, 12(19):3266.

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The rapidly increasing demand for energy storage has been consistently driving the exploration of different materials for Li-ion batteries, where the olivine lithium-metal phosphates (LiMPO4) are considered one of the most potential candidates for cathode-electrode design. In this context, the work presents an extensive comparative theoretical study of the electrochemical and electrical properties of iron (Fe)-, cobalt (Co)-, manganese (Mn)-, chromium (Cr)-, and vanadium (V)-based LiMPO4 materials for cathode design in lithium (Li)-ion battery applications, using the density-functional-theory (DFT)-based first-principle-calculation approach. The work emphasized different material and performance aspects of the cathode design, including the cohesive energy of the material, Li-intercalation energy in olivine structure, and intrinsic diffusion coefficient across the Li channel, as well as equilibrium potential and open-circuit potential at different charge-states of Li-ion batteries. The results indicate the specification of the metal atom significantly influences the Li diffusion across the olivine structure and the overall energetics of different LiMPO4. In this context, a clear correlation between the structural and electrochemical properties has been demonstrated in different LiMPO4. The key findings offer significant theoretical and design-level insight for estimating the performance of studied LiMPO4-based Li-ion batteries while interfacing with different application areas.

XIONG W, HU W, LI H.

First-principles calculations of the atomic structure and electronic structure of F-doped Li(Ni0.8Co0.1Mn0.1)O2 cathode material for lithium-ion batteries

[J]. Journal of Electronic Materials, 2022, 51(7):3944-3949.

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LU J, GUAN J, CHEN H, et al.

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As the cathode of lithium-ion batteries, carbon material has been the focus of research. At present, diverse investigations have been carried out on the lithium convergence behavior in the carbon material family. As a new carbon material, multilayer fullerenes have been shown in various experimental studies to have a high discharge rate as an electrode, indicating that onion-like carbon has the potential to release energy quickly. Materials and mechanical scientists are increasingly interested in lithium-ion batteries. In this paper, the molecular dynamics (MD) method was used to simulate the absorption of lithium ions by multilayer fullerenes. A model of five layers of fullerenes was established to compare the lithium-ion absorption rates of multiple layers of fullerenes at different lithium-ion concentrations. The effects of the lithium-ion diffusion rate on the results were considered. In addition, the effects of the number of lithium ions, the velocity, and the layer number of multilayer fullerenes on the structural behavior and stress were investigated thoroughly when the multilayer fullerenes adsorbed lithium ions.

MABUCHI T, NAKAJIMA K, TOKUMASU T.

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[J]. Micromachines (Basel), 2021, 12(9):1012.

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ZHU Y, HUANG Y, DU R, et al.

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LiNi1−x−yMnxCoyO2 materials are a typical class of layered cathode materials with excellent electrochemical performance in lithium-ion batteries. Molecular dynamics simulations are performed for LiNi1−x−yMnxCoyO2 materials with different transition metal ratios. The Li/Ni exchange ratio, ratio of anti-site Ni2+ to total Ni2+, and diffusion coefficient of Li ions in these materials are calculated. The results show that the Li-ion diffusion coefficient strongly depends on the ratio of anti-site Ni2+ to total Ni2+ because their variation tendencies are similar. In addition, the local coordination structure of the Li/Ni anti-site is analyzed.

DIETRICH F, CISTERNAS E, MARCELO PASINETTI P, et al.

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The study of chemo-mechanical stress taking place in the electrodes of a battery during cycling is of paramount importance to extend the lifetime of the device. This aspect is particularly relevant for all-solid-state batteries where the stress can be transmitted across the device due to the stiff nature of the solid electrolyte. However, stress monitoring generally relies on sensors located outside of the battery, therefore providing information only at device level and failing to detect local changes. Here, we report a method to investigate the chemo-mechanical stress occurring at both positive and negative electrodes and at the electrode/electrolyte interface during battery operation. To such effect, optical fiber Bragg grating sensors were embedded inside coin and Swagelok cells containing either liquid or solid-state electrolyte. The optical signal was monitored during battery cycling, further translated into stress and correlated with the voltage profile. This work proposes an operando technique for stress monitoring with potential use in cell diagnosis and battery design.© 2022. The Author(s).

LYU S, LI N, SUN L, et al.

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RICHARDSON R R, IRELAND P T, HOWEY D A.

Battery internal temperature estimation by combined impedance and surface temperature measurement

[J]. Journal of Power Sources, 2014,265:254-261.

[本文引用: 2]

HANDE A.

Internal battery temperature estimation using series battery resistance measurements during cold temperatures

[J]. Journal of Power Sources, 2006, 158(2):1039-1046.

DOI:10.1016/j.jpowsour.2005.11.027      URL     [本文引用: 1]

张广续, 魏学哲, 陈思琦, 等.

高温循环老化对锂离子电池安全性影响研究

[J]. 机械工程学报, 2023, 59(22):33-45.

DOI:10.3901/JME.2023.22.033      [本文引用: 1]

高温循环老化会造成电池降解，改变电池热稳定性，进而影响电池的安全使用。全面阐述锂离子电池在高温循环老化过程中热安全特性演变具有重要意义。通过对锂离子电池在高温循环老化过程中降解行为、放电过程中的产热特性、绝热热失控特性及过充热失控特性进行研究，全面揭示锂离子电池在高温循环老化过程中热安全特性的演变，为电池的安全使用提供参考。结果显示，电池在高温循环老化过程中的内部降解致使容量衰减、阻抗增加。在绝热放电过程中，随着老化程度的加深，温升先降后增，主导因素由容量变为温升速率；高温循环严重削减阳极和阴极的热稳定性，使得电池的自产热起始温度和热失控触发温度严重下降，热稳定性降低。活性物质的损失致使电池最高温度和最大温升速率降低，热危害性降低；由于高温循环老化致使电池阻抗增加，在过充过程中，老化电池的特征电压均出现升高。但是，老化电池的过充热失控时间缩短、热失控触发温度降低、副反应热对热失控触发的贡献率降低，电池的抗性下降。

ZHANG Guangxu, WEI Xuezhe, CHEN Siqi, et al.

Research on the impact of high-temperature cyclic aging on the safety of lithium-ion batteries

[J]. Journal of Mechanical Engineering, 2023, 59(22):33-45.

DOI:10.3901/JME.2023.22.033      [本文引用: 1]

High-temperature cyclic aging can cause battery degradation, influence battery thermal stability, and further affect the safety use of batteries. It is of great significance to clarify the evolution of thermal safety during high-temperature cyclic aging for lithium ion batteries. By investigating the degradation behavior, heat generation characteristics upon discharge, adiabatic thermal runaway characteristics, and overcharge characteristics during high-temperature cyclic aging, the thermal safety evolution is revealed, which can provide guidance for the safe utilization of lithium ion batteries. High-temperature cyclic aging causes battery degradation, such as capacity degradation and impedance increase. In the adiabatic discharge process, the entire temperature rise first decreases and then increases with aging. The dominant factor changes from capacity to temperature rise. High-temperature cyclic aging severely reduces the thermal stability of the anode and cathode, making the self-heating initial temperature and thermal runaway triggering temperature decrease severely. The battery thermal safety is reduced. The loss of active materials reduces the maximum temperature and the maximum temperature rise rate, which means that the thermal hazards decrease. Due to the impedance increase during high-temperature cyclic aging, the characteristic voltages increase upon overcharge. However, the overcharge thermal runaway time of aged cell is shortened, the thermal runaway triggering temperature is reduced, and the contribution rate of side reaction heat to thermal runaway triggering is reduced. The battery tolerance is reduced.

来鑫, 马云杰, 郑岳久, 等.

一种基于几何特征变换与迁移的锂离子电池电化学阻抗谱曲线重构方法

[J]. 机械工程学报, 2023, 59(22):140-149.

DOI:10.3901/JME.2023.22.140      [本文引用: 1]

利用电化学阻抗谱(Electrochemical impedance spectroscopy， EIS)技术对锂离子电池进行无损检测是电池健康度评价及分选的有效手段。然而， EIS 测试时需要将电池的荷电状态(State of charge， SOC)调整到一致，大大降低了测试效率。针对上述问题，提出一种基于几何特征变换与迁移的锂离子电池 EIS 曲线重构方法。首先，将 EIS 曲线图形化为三个圆弧和一段直线的组合几何体，并定义这些几何体的特征参数；其次，通过小批量电池 EIS 测试数据建立几何特征参数与 SOC 之间的线性关系；然后，将上述关系迁移到大批量电池的 EIS 曲线重构，实现将任意 SOC 下测试的 EIS 曲线重构到同一目标 SOC 下；最后，利用试验验证所提出方法的有效性。试验结果表明，重构 EIS 曲线与实测 EIS 曲线之间的方均根误差及平均绝对百分比误差分别保持在 1.2 mΩ 与 3.5%以内，测试时间相比传统方法缩短了至少 98%。所提出方法简单易实现，在保证 EIS 曲线重构精度下可大幅度减小测量时间。

LAI Xin, MA Yunjie, ZHENG Yuejiu, et al.

A reconstruction method of electrochemical impedance spectrum curve of lithium-ion batteries based on geometric feature transformation and migration

[J]. Journal of Mechanical Engineering, 2023, 59(22):140-149.

DOI:10.3901/JME.2023.22.140      [本文引用: 1]

Non-destructive testing of lithium-ion batteries(LIBs) by electrochemical impedance spectroscopy(EIS) is an effective method for battery health evaluation and selection. However, when EIS is tested, the charge state(SOC) of batteries needs to be adjusted to a uniform level, which greatly reduces the testing efficiency. Aiming at these problems, a method of EIS curve reconstruction for LIBs based on geometric feature transformation is presented in this study. Firstly, the EIS curve is graphed as a combination of three circular arcs and a straight line, and the characteristic parameters of these geometries are defined. Subsequently, the linear relationship between geometric feature parameters and SOC is established through small-batch EIS test data. Then, the above relationships are transferred to the EIS curve reconstruction of a large number of batteries, and the EIS curve measured under any SOC can be reconstructed to the target SOC. Finally, the proposed method is verified by experiments. The results show that the root-mean-square-error and the average-absolute-percentage-error between the reconstructed EIS curve and the measured EIS curve are controlledby 1.2 mΩ and 3.5%, respectively, and the test time is reduced by at least 98% compared with the traditional methods. The proposed method is simple and easy to implement, and can significantly reduce the measurement time while ensuring the EIS accuracy.

SCHMIDT J P, ARNOLD S, LOGES A, et al.

Measurement of the internal cell temperature via impedance:Evaluation and application of a new method

[J]. Journal of Power Sources, 2013,243:110-117.

[本文引用: 1]

SRINIVASAN R, CARKHUFF B G, BUTLER M H, et al.

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Impedance observer for a Li-ion battery using Kalman filter

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Broadband identification of battery electrical impedance for HEVs

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HOWEY D A, MITCHESON P D, YUFIT V, et al.

Online measurement of battery impedance using motor controller excitation

[J]. IEEE Transactions on Vehicular Technology, 2014, 63(6):2557-2566.

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HUANG W, QAHOUQ J A.

An online battery impedance measurement method using DC-DC power converter control

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RICHARDSON R R, HOWEY D A.

Sensorless battery internal temperature estimation using a kalman filter with impedance measurement

[J]. IEEE Transactions on Sustainable Energy, 2015, 6(4):1190-1199.

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BHIDE S, SHIM T.

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ZHU Shengxin, HAN Jindong, AN Hongyan, et al.

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LOULI A J, ELLIS L D, DAHN J R.

Operando pressure measurements reveal solid electrolyte interphase growth to rank Li-ion cell performance

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[本文引用: 1]

RAGHAVAN A, KIESEL P, SOMMER L W, et al.

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GANGULI A, SAHA B, RAGHAVAN A, et al.

Embedded fiber-optic sensing for accurate internal monitoring of cell state in advanced battery management systems part 2:Internal cell signals and utility for state estimation

[J]. Journal of Power Sources, 2017,341:474-482.

[本文引用: 1]

HU Xiaoran, JIANG Zhaolian, YAN Liqin, et al.

Real-time visualized battery health monitoring sensor with piezoelectric/pyroelectric poly (vinylidene fluoride-trifluoroethylene) and thin film transistor array by in-situ poling

[J]. Journal of Power Sources, 2020,467:228367.

[本文引用: 1]

牛少军, 吴凯, 朱国斌, 等.

锂离子电池硅基负极循环过程中的膨胀应力

[J]. 储能科学与技术, 2022, 11(9):2989.

DOI:10.19799/j.cnki.2095-4239.2022.0194      [本文引用: 1]

研究硅基负极在充放电及循环过程中的膨胀对开发下一代高比能锂离子动力电池具有重要意义。本工作采用商业化的SiO <sub>x</sub> /Graphite为负极匹配高比能镍钴锰酸锂［Li(Ni<sub>0.8</sub>Mn<sub>0.1</sub>Co<sub>0.1</sub>)O<sub>2</sub>，NCM811］正极，组装了60 Ah大软包电池，并对其进行循环膨胀应力、应力增长机理与膨胀应力的改善等方面的研究。结果表明SiO <sub>x</sub> 材料的构成为3～5 nm Si颗粒分散在无定形的SiO<sub>2</sub>内部，首次充放电比容量为1840.9/1380 mAh/g，库仑效率为75%。大软包电池单次充放电膨胀应力的变化为7320 N，约为石墨负极的4倍。工作温度越高容量衰减越快，衰减到70% SOH时，25、45和60 ℃对应的循环次数分别为980、850和500次，对应的最大膨胀应力分别为25107、25490、23667 N。此外，机理分析发现电池循环膨胀应力的增长和容量衰减之间为线性相关，CP(cross section polisher)-SEM分析发现膨胀应力的增加主要来自于SiO <sub>x</sub> 颗粒表面的破裂及副反应导致的SEI (solid electrolyte interphase)增厚。通过测定缓冲垫压缩曲线的方法筛选了合适的聚氨酯类缓冲垫，验证对循环无影响，但可以显著改善膨胀应力的增加，膨胀应力降低50%，这些结果将为更好地应用高比容量的硅基负极材料奠定基础。

NIU Shaojun, WU Kai, ZHU Guobin, et al.

Studies on the swelling force during cycling of Si-based anodes in lithium ion batteries

[J]. Energy Storage Science and Technology, 2022, 11(9):2989.

DOI:10.19799/j.cnki.2095-4239.2022.0194      [本文引用: 1]

Studying the swelling force of Si-based anodes for the next generation of high energy lithium-ion batteries is crucial. In this study, we assembled 60-Ah large pouch batteries with commercialized SiO x /Graphite and NCM811 cathode, tested their cycle life and swelling force increase, and studied the relevant mechanisms and strategies for reducing the swelling force. The structure of SiO x was a 3～5 nm Si core distributed in amorphous SiO2, and the specific capacity of SiO x in the first cycle was 1380 mAh/g and the first columbic efficiency was ca. 75%. The swelling force increase during the first cycle was 7320 N, which was 4 times higher than that of graphite-based batteries. The cycling tests under different ambient temperatures showed high temperature-dependent tendency. At 25 ℃, 45 ℃, and 60 ℃, the cycle numbers were 980, 850, and 500, corresponding to 70% SOH, with the maximum swelling force being 25107, 25490, and 23667 N, respectively. The root cause for the swelling force increase was the solid electrolyte interface growth and thickening with repeated electrochemical cycles. The compression curve was applied to sorting appropriate cushion that can accommodate the swelling force. The results showed that polyurethane cushion had the best compression properties, reducing the swelling force by 50%. This study provides a foundation for using SiO x in large-scale lithium-ion batteries.

MIAO Ziyun, LI Yanpeng, XIAO Xiangpeng, et al.

Direct optical fiber monitor on stress evolution of the sulfur-based cathodes for lithium-sulfur batteries

[J]. Energy & Environmental Science, 2022,15:2029-2038.

[本文引用: 1]

MATASSO A, WETZ D, LIU F.

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MATASSO A, WONG D, WETZ D, et al.

Correlation of bulk internal pressure rise and capacity degradation of commercial LiCoO2 cells

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MATASSO A, WONG D, WETZ D, et al.

Effects of high-rate cycling on the bulk internal pressure rise and capacity degradation of commercial LiCoO2 cells

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LIU X, REN D, HSU H, et al.

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ROWDEN B, GARCIA-ARAEZ N.

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KONG W, LI H, HUANG X, et al.

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GALUSHKIN N Е, YAZVINSKAYA N N, GALUSHKIN D N.

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GERELT-OD B, KIM J, SHIN E, et al.

In situ Raman investigation of resting thermal effects on gas emission in charged commercial 18650 lithium ion batteries

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WANG S, LIU J, RAFIZ K, et al.

An on-line transient study on gassing mechanism of lithium titanate batteries

[J]. Journal of the Electrochemical Society, 2019, 166(16):A4150-A4157.

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SCHMIEGEL J P, LEIßING M, WEDDELING F, et al.

Novel in situ gas formation analysis technique using a multilayer pouch bag lithium ion cell equipped with gas sampling port

[J]. Journal of the Electrochemical Society, 2020, 167(6):060516.

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Parasitic gas evolution in lithium ion battery (LIB) cells especially occurs within the first charge cycle, but can also take place during long-term cycling and storage, thereby, negatively affecting the cell performance. Gas formation is influenced by various factors, such as the cell chemistry and operating conditions, thus, demanding fundamental studies in terms of interphase and gas formation (gas volume and composition) and electrolyte consumption. Gas analyses in terms of mass spectrometry of gaseous products are regularly performed, however, usually using custom-made cell designs with a high excess of electrolyte. Here, a gas sampling port (GSP) is incorporated in a commercial small-scale multilayer pouch cell in a simple post-production process and systematically evaluated as proof-of-principle approach towards effective electrolyte additive research under practically relevant conditions, i.e., when applying a limited amount of electrolyte per cell capacity. The GSP-based LIB pouch cell design allows the voltage-dependent identification and separation of formed gases, while a clear correlation between electrolyte reduction peaks, observed in differential capacity profiles, and the onset of gas evolution is demonstrated. In summary, the novel GSP-based pouch cell setup benefits from the possibility of multiple time-, cell voltage- or state-of-charge-dependent gas measurements, without significantly influencing the original cell performance.

CHOE C Y, JUNG W S, BYEON J W.

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DAVIES G, KNEHR K W, VAN TASSELL B, et al.

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DENG Z, HUANG Z, SHEN Y, et al.

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张忠义, 羌嘉曦, 杨林, 等.

混合动力汽车电池管理系统

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ZHANG Zhongyi, QIANG Jiaxi, YANG Lin, et al.

Battery management system for HEVs

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杨朔, 何莉萍, 钟志华.

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[J]. 贵州工业大学学报, 2004, 33(2):90-94.

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YANG Shuo, HE Liping, ZHONG Zhihua.

A battery monitoring system based on CAN bus for electric vehicles

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魏学哲, 孙泽昌, 邹广楠.

模块化的 HEV 锂离子电池管理系统

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佘承其, 张照生, 刘鹏, 等.

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SHE Chengqi, ZHANG Zhaosheng, LIU Peng, et al.

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HONG Jichao. Research on safety control management of electric vehicle power battery based on operation big data[D]. Beijing: Beijing Institute of Technology, 2020.

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HONG J, WANG Z, YAO Y.

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YANG S, HE R, ZHANG Z, et al.

CHAIN:Cyber hierarchy and interactional network enabling digital solution for battery full-lifespan management

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WANG Y, XU R, ZHOU C, et al.

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ZHANG C, LIU S.

Meta-energy:When integrated energy internet meets metaverse

[J]. IEEE/CAA Journal of Automatica Sinica, 2023, 10(3):580-583.

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