基于PNGV模型储能锂电池参数辨识及SOC估算研究

锂电池因具有比能量高、循环寿命长、对环境无污染等优点,在储能系统中已逐渐得到应用.准确估算锂电池的荷电状态(SOC)可防止电池过充、过放,保障电池安全、充分地使用.为了精确估算储能锂电池SOC,基于PNGV(partnership for a new generation of vehicles)电池等效模型,利用递推最小二乘法(RLS)对模型参数进行在线辨识和实时修正,增强了系统的适应性.结合安时法、开路电压法和PNGV模型,提出了一种实时在线修正SOC算法.根据实验数据,建立了仿真模型,以验算模型和SOC估算算法的精度.仿真结果表明,PNGV模型能真实地模拟电池特性,且能有效地提高SOC估算精度,适合长时间在线估算储能锂电池的SOC.     

Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries

The coulomb counting method is expedient for state-of-charge (SOC) estimation of lithium-ion batteries with high charging and discharging efficiencies. The charging and discharging characteristics are investigated and reveal that the coulomb counting method is convenient and accurate for estimating the SOC of lithium-ion batteries. A smart estimation method based on coulomb counting is proposed to improve the estimation accuracy. The corrections are made by considering the charging and operating efficiencies. Furthermore, the state-of-health (SOH) is evaluated by the maximum releasable capacity. Through the experiments that emulate practical operations, the SOC estimation method is verified to demonstrate the effectiveness and accuracy.     

The progress in fault diagnosis techniques for lithium-ion batteries

实施精准、可靠的故障诊断,是确保电池系统安全、稳定、可靠运行的关键,并为电池系统的精准运营维护提供理论、方法与技术支撑。本文首先在总结电池系统故障类型与表现形式的基础上,重点分析了在电池制造、成组筛选和使用各个阶段的本体故障引发机制。其次,对现有研究发展的电池系统故障诊断方法进行了分类,大致分为基于电池模型的故障诊断方法和无模型的故障诊断方法。之后,针对不一致故障诊断、短路故障诊断、热故障诊断、传感器故障诊断、连接组件故障诊断和多故障联合诊断,对现有文献提出的解决策略及应用案例进行了全面综述。最后,以全面文献调研为基础,提出了电池系统故障诊断研究面临的主要挑战和未来的研究方向。     

基于加速寿命试验的锂离子电池可靠性分析方法

为了有效地提高锂离子电池寿命评估的准确性,延长储能系统在配电网中运行年限,文章提出了基于加速寿命试验的锂离子电池可靠性分析方法。综合考虑不同放电深度对锂离子电池寿命影响,建立了锂离子电池的寿命衰退模型;确定了荷电状态(SOC)与健康度(SOH)的关联特性关系;提出了基于逆幂率方程的储能系统加速寿命试验方法;基于情景分析法对锂离子电池的可靠性进行了分析。研究结果表明,文章所提出的试验方法能够准确地对不同运行状态下的锂离子电池储能系统进行可靠性评估,保证储能系统并网运行过程中的调控准确性。     

Recent progress on thermal runaway propagation of lithium-ion battery

简述了电动汽车锂离子动力电池热失控蔓延机理、建模与抑制技术的最新研究进展。为了满足汽车高能量的要求,需要动力电池进行串并联成组来提供动力。电池组成组安全问题成为电动汽车大规模应用的重要技术问题。电池组中的某一个电池单体发生热失控后产生大量热,导致周围电池单体受热产生热失控。因而,电池组成组安全问题的重要关注点是电池组内的热失控蔓延问题。本文对锂离子电池热失控蔓延问题的国内外研究进展进行了综述,分析了对于不同种类锂离子动力电池影响其热失控蔓延特性的主要因素。总结了文献中的热失控蔓延建模方法,并指出了已有方法的不足。从电池系统热安全管理的角度,阐述并分析了热失控蔓延防控技术的研究成果与方向。最后对锂离子电池热失控蔓延研究进行了展望。     

Thermal explosion hazards of lithium-ion batteries in hermetic space

使用扩展容积加速度量热仪（extend volume accelerating rate calorimeter,EV-ARC）及耐压罐,开展了密闭空间中不同荷电状态（SOC）下18650型锂离子电池的热爆炸实验。实验发现,SOC=0%时电池不会发生热爆炸,而在其它工况下均发生了热爆炸;电池发生热爆炸时,电池表面最高温度、耐压罐内部最大压力都随着SOC的增加而增大。利用实验中电池发生热爆炸时的初始温度和最高温度,通过计算得到了不同SOC下电池发生热爆炸时的爆炸当量,当SOC=100%时,爆炸当量值最大,为5.45 gTNT,约是SOC=25%时的2.5倍,并在耐压罐中产生40.69 bar的峰值压力。锂离子电池在密闭中的热爆炸危险性随着电池SOC的增加而增大。     

Online management of lithium-ion battery based on time-triggered controller area network for fuel-cell hybrid vehicle applications

This paper introduces a state of charge (SOC) estimation algorithm that was implemented for an automotive lithium-ion battery system used in fuel-cell hybrid vehicles (FCHVs). The proposed online control strategy for the lithium-ion battery, based on the Ah current integration method and time-triggered controller area network (TTCAN), incorporates a signal filter and adaptive modifying concepts to estimate the Li₂MnO₄ battery SOC in a timely manner. To verify the effectiveness of the proposed control algorithm, road test experimentation was conducted with an FCHV using the proposed SOC estimation algorithm. It was confirmed that the control technique can be used to effectively manage the lithium-ion battery and conveniently estimate the SOC.     

Patent analysis of lithium ion power battery recycling technology

锂离子动力电池的回收是当前储能产业关注的焦点之一,为了解锂离子动力电池的回收现状,以CNABS和DWPI专利数据库中的检索结果为分析样本,对锂离子动力电池回收技术的专利申请量趋势、全球分布区域、国内外主要申请人、全球重点技术分布以及国内重点技术进行全面分析,结果表明,虽然锂离子动力电池回收技术是目前全球尤其是中国争相布局的产业对象,但目前各个企业的专利申请量均较小且技术布局零散,总体来说锂离子动力电池回收技术仍处于摸索阶段,产业前景不明,本文以期给锂离子动力电池回收技术未来的布局和发展提供一定的借鉴。     

Recent progress on evolution of safety performance of lithium-ion battery during aging process

安全性是制约锂离子电池规模应用的重要技术问题。锂离子电池的安全性能不仅仅与材料体系、电芯设计相关,还会随着使用过程而发生变化。锂离子电池安全性能在全生命周期内的演变规律需要重点展开研究,以保障电池在使用过程中的安全性。本文对锂离子电池全生命周期安全性演变问题的国内外研究进展进行了综述,分析了国内外关于电池安全性能在循环老化和储存老化两种工况下的演变规律的研究,总结了电池老化衰减机理与安全性能变化之间的关系,指出负极析锂是影响电池全生命周期安全性能的重要因素,最后对锂离子电池全生命周期安全性演变研究进行了展望。     

电动汽车电池的充电状态和健康状况评估：关键问题和挑战

使用电动汽车（EV）进行运输被视为实现可持续发展和解决环境问题的必要组成部分。当前对环境的关注,例如化石燃料的快速消耗,空气污染的增加,能源需求的加速增长,全球变暖和气候变化,为交通运输部门的电气化铺平了道路。电动汽车可以解决上述问题。电源已成为电动汽车发展的关键,尤其是锂离子（Li-ion）电池。由于其能量密度、功率...     

A novel practical state of charge estimation method: an adaptive improved ampere-hour method based on composite correction factor

The research of the real-time state of charge (SOC) estimation method for lithium-ion battery is developing towards the trend of model diversification and algorithm complexity. However, due to the limitation of computing ability in the actual battery management system, the traditional ampere-hour (Ah) method is still widely used. First, temperature, charge-discharge current, and battery aging are considered as the main factors, which affect the estimation accuracy of the Ah method under the condition that detection accuracy of the current sensor is determined. Second, the relationship between the SOC and battery open-circuit voltage at different temperatures is analyzed, which is used to modify the initial SOC. Third, the influence mechanism of main factors on the effect of the Ah method is analyzed, and proposes a capacity composite correction factor to reflect the influence of charge-discharge efficiency, coulomb efficiency, and battery aging comprehensively, and then update its value in real-time. Lastly, the adaptive improved Ah formula and the complete SOC estimation model is designed, and the estimation effect of this model is verified by comparing with other SOC estimation methods in the experiment of dynamic cycle test. The results show that the estimation error of the adaptive improved method is less than 2% under two comprehensive working conditions, while the error of the traditional method is 5% to 10%, and compared with an extended kalman filter algorithm, it also gets a better SOC estimation performance, which proves that this method is scientific and effective.     

Equalization of series connected lithium-ion batteries based on back propagation neural network and fuzzy logic control

In this article, a nondissipative equalization scheme is proposed to reduce the inconsistency of series connected lithium-ion batteries. An improved Buck-Boost equalization circuit is designed, in which the series connected batteries can form a circular energy loop, equalization speed is improved, and modularization is facilitated. This article use voltage and state of charge (SOC) together as equalization variables according to the characteristics of open-circuit voltage (OCV)-SOC curve of lithium-ion battery. The second-order RC equivalent circuit model and back propagation neural network are used to estimate the SOC of lithium-ion battery. Fuzzy logic control (FLC) is used to adjust the equalization current dynamically to reduce equalization time and improve efficiency. Simulation results show that the traditional Buck-Boost equalization circuit and the improved Buck-Boost equalization circuit are compared, and the equalization time of the latter is reduced by 34%. Compared with mean-difference algorithm, the equalization time of FLC is decreased by 49% and the energy efficiency is improved by 4.88% under static, charging and discharging conditions. In addition, the proposed equalization scheme reduces the maximum SOC deviation to 0.39%, effectively reducing the inconsistency of batteries.     

基于深度神经网络的梯次利用电池健康状态评估

随着大量退役电池梯次利用,对退役动力电池健康状态的准确估计是保障电池梯次利用安全高效运行的前提。针对上述问题,提出基于深度神经网络学习的梯次利用电池健康状态评估方法。根据不同循环次数下梯次利用电池充放电性能的差异性,从梯次利用电池物理特性角度挖掘影响梯次利用电池老化特征的主要参数,利用皮尔逊法计算电池老化特征与梯次利用电池健康状态的相关系数,选取较高相关度特征作为深度神经网络的输入,建立基于深度神经网络学习的梯次利用电池健康状态评估模型。通过美国国家航空航天局Ames卓越预测中心的锂离子电池测试数据仿真实例验证了该文方法的有效性。仿真结果表明,与传统神经网络相比,深度神经网络学习可明显提高梯次利用电池健康状态的预测精度,为退役动力电池健康状态评估提供理论依据。     

Research progress of high rate Li-ion battery materials

电动汽车行业迅速发展,高倍率的锂离子电池是其关键,因此需要不断开发适用高倍率充放电的电池材料。本文简要综述了高倍率锂离子电池正极材料、负极材料、隔膜和电解液方面的研究进展,并对高倍率锂离子电池材料发展进行了展望。     

Brief analysis the safety of solid-state lithium ion batteries

锂二次电池因其具有能量密度高、循环寿命长、无记忆效应、无污染等优点,使得其在便携式消费电子产品、电动汽车、能量储存等领域具有广泛的应用前景。目前,锂二次电池的能量密度和安全性是当今世界的研究热点。但对于传统液态电解质的锂离子电池而言,尽管从材料、模组、电源管理、热管理、系统设计等各个层面均采取了多种改进措施,然而高能量密度电芯的安全性问题依然突出,热失控问题难以彻底避免。因此,为了提高锂电池的安全性,发展理论上不易燃的固态锂电池是解决锂电池安全问题的必由途径。本工作比较了传统液态锂离子电池与固态锂电池结构特征,总结了其各自优缺点,进一步深入剖析了传统液态锂离子电池安全问题产生的根本原因,提出了解决锂离子电池安全性问题的最佳方案,并通过对自主研发的系列容量固态锂（离子）电池的安全性能进行测试,证实了固态锂电池的高安全特性。     

All-solid-state thin film lithium batteries

薄膜型全固态锂电池具有完美的电极/电解质固-固界面,可以有效解决当前商用锂离子电池的安全性问题,并具有超长的循环寿命、较宽的使用温度范围、较低的自放电率等优点,相比体型固态锂电池性能优越,受到了业界的广泛关注。然而制备成本高、单位面积能量密度低等缺点限制了其应用范围。本文介绍了薄膜型全固态锂电池的工作原理及特点、关键材料的研究现状,并针对薄膜固态锂电池的产业化现状和技术瓶颈进行了总结,对新一代薄膜型全固态锂电池的发展及产业化应用进行了展望。     

SOC estimation method of power battery based on BP artificial neural network

电池剩余电量（SOC）的估算是电池管理系统中的关键技术之一,在众多估算方法中,神经网络在估算的准确性及鲁棒性上具有明显优势。庞大的数据量是获得SOC精确值的重要因素。针对以上问题,研究提出了基于BP人工神经网络的动力电池SOC估算方法,以某型号整包电池作为实验对象,通过对电池电压、电流、内阻及温度的数据采集,获得海量数据。建立电池的等效电路模型,考虑电池极化、充放电倍率及温度的影响对初始数据进行修正。基于MATLAB平台建立BP人工神经网络模型,数据修正后用于网络模型的训练,并验证了模型的可行性。将模型用于实验数据的预测,通过函数拟合实现了SOC的估算。最后,通过对比SOC的预测值与实际测量值,最终证明建立的人工神经网络模型对SOC估算的有效性。     

Overview of research on thermal safety of lithium-ion batteries

随着锂离子电池在生活和工作中的普及,锂离子电池的安全事故逐年增加,锂离子电池的安全研究逐渐引起学术界的关注。研究锂离子电池的热安全性,可以有效分析锂离子电池发生起火和爆炸的内在原因,指导锂离子电池安全性研究的开展。本文介绍了锂离子电池工作过程中产热的来源和影响因素,以及锂离子电池热失控发生时的内部反应和反应对应的温度,并对电池热失控时的热特性参数进行了总结。     

Battery algorithm verification and development using hardware-in-the-loop testing

Battery algorithms play a vital role in hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), extended-range electric vehicles (EREVs), and electric vehicles (EVs). The energy management of hybrid and electric propulsion systems needs to rely on accurate information on the state of the battery in order to determine the optimal electric drive without abusing the battery.In this study, a cell-level hardware-in-the-loop (HIL) system is used to verify and develop state of charge (SOC) and power capability predictions of embedded battery algorithms for various vehicle applications. Two different batteries were selected as representative examples to illustrate the battery algorithm verification and development procedure. One is a lithium-ion battery with a conventional metal oxide cathode, which is a power battery for HEV applications. The other is a lithium-ion battery with an iron phosphate (LiFePO₄) cathode, which is an energy battery for applications in PHEVs, EREVs, and EVs.The battery cell HIL testing provided valuable data and critical guidance to evaluate the accuracy of the developed battery algorithms, to accelerate battery algorithm future development and improvement, and to reduce hybrid/electric vehicle system development time and costs.     

Overview of the modeling of lithium-ion batteries

简述了锂离子电池等效电路模型和电化学模型的研究进展。由于具有耗时短、技术开发效率高等优点,仿真模型被广泛应用于锂离子电池衰减机制分析、状态诊断及寿命预测。锂离子电池仿真模型主要包括等效电路模型和电化学机理模型。等效电路模型主要应用于锂离子电池荷电状态诊断。电化学机理模型主要应用于锂离子电池衰减机制分析和健康状态诊断,并为寿命预测提供技术支持。等效电路模型的结构过于单一,在锂离子电池寿命后期适用性降低。电化学机理模型结构复杂,计算量大,在线性应用能力较差。总结了现阶段常用的锂离子电池等效电路模型和电化学模型的建模原理及模型结构,阐述了每种模型在电池研究中的具体应用,并分析了其各自的优势及局限性。通过以上分析,并结合最新的建模理论,对建立具有高精度、高适用性锂离子电池仿真模型的研究方向进行了展望。