船用电池管理系统及SOC算法研究

依照中国船级社《纯电池动力船舶检验指南》(CCS GUIDANCE NOTES GD22-2019)要求,设计了一种船舶通用型电池管理系统.针对大容量电池系统电池荷电状态估算不准确问题,开发了一种扩展的卡尔曼滤波算法.经试验验证,所设计的电池管理系统能够满足船用要求;所采用的SOC算法能够较准确地估算出电池剩余电量.     

基于区域极点配置电池荷电状态的估计方法

电池管理系统中蓄电池荷电状态的测量是非常重要的。在光伏系统中,蓄电池荷电状态决定了电池管理系统的充放电策略。根据一类电池等效电路模型,利用蓄电池荷电状态观测器误差系统的极点分布,给出蓄电池荷电状态观测器的设计方法。该方法可以根据圆盘极点位置的变化调整观测器荷电状态观测值的误差。通过仿真可以看到,基于极点配置的荷电状态观测器的有效性。     

基于SOC的动力电池组主动均衡研究

动力电池单体容量不一致会导致动力电池组在充放电过程中失去平衡,影响动力电池组的使用效果,降低动力电池组的使用寿命.为了解决这一难题,本工作设计了一种基于荷电状态(SOC)值的主动均衡电池管理系统.该电池管理系统利用安时积分法估计动力电池组中各电池单体的SOC值,在此基础上计算动力电池组的SOC平均值,然后将各动力电池单体的SOC值与动力电池组的SOC平均值进行比较,若二者的差值大于或小于设定的阈值,电池管理系统将控制DC/DC变换器和开关电路对相应的电池单体进行充电或放电,实现电池组能量的转移,从而达到均衡的目的.以4块动力电池单体串联而成的动力电池组为例,对该主动均衡电池管理系统的有效性进行了仿真验证.仿真结果表明,经过该系统的主动均衡,3608 s后各动力电池单体之间的SOC差值由初始的7.8％迅速减小到3％以内,5600 s后减小到1％,大大降低了动力电池中各电池单体SOC值的不一致.     

储能用锂离子电池管理系统研究

锂离子电池因其性能优异在高电压大容量的储能系统得到了广泛的应用。锂离子电池管理系统是延长电池循环寿命,维护电池安全运行的关键。针对储能用锂离子电池的特性,该文讨论了储能用锂离子电池管理系统的结构,重点介绍了电池管理系统的主要功能,特别是单体电池数据采集功能、电池状态估计功能和均衡管理功能,并分析了状态估计和均衡管理方法的优缺点,对其实现策略进行了评价。     

基于并行卡尔曼滤波器的锂离子电池荷电状态估计

针对新能源电动汽车的电量显示与安全管理问题,对其锂离子电池的荷电状态展开研究,提出了基于并行卡尔曼滤波器的全寿命下的电池荷电状态(state of charge,SOC)估计算法.建立了电池Thevenin一阶RC等效电路模型,通过开路实验的数据处理获取静态OCV-SOC关系表达式,并利用具有动态遗忘因子的最小二乘法对模型参数进行了辨识.以安时积分法为状态传递方程,在扩展卡尔曼滤波的基础上利用最大似然估计准则使模型噪声协方差具有自学习能力.考虑模型参数随电池寿命衰减而改变的问题设计并行结构的滤波器来分别进行电池状态估计和参数修正,保证了数据传递中的纯洁性和独立性,从而实现了全寿命下的SOC估计.经过仿真实验验证算法的快速收敛性与实时性,估计精度在2％以内.     

混合动力摩托车动力电池的监测系统研究

环境和能源问题要求未来的汽车提高效率、降低污染,辅助混合动力电动汽车将成为主要的发展方向。本文以混合动力摩托车为平台,研究设计了动力电池的监测系统,该系统可实时对每块电池的电压、电流、温度进行检测,并估计电池的荷电状态,并可对电池的电压、温度及电池间电压不均衡性进行监测预警,提供能量管理系统的控制参数。     

磷酸铁锂电池火灾危险性

由于稳定性好、可靠性高等优点,近年来磷酸铁锂电池在储能和变电系统中得到大量应用.为研究大容量磷酸铁锂电池的火灾危险性,通过自主设计的锂离子电池火灾测试平台,开展了 228 A-h磷酸铁锂电池的热滥用测试,系统研究了该大型电池的燃烧过程及产热规律,对比分析了不同荷电状态(SOC)下目标电池的火灾特性.结果表明电池的燃烧行为可大致分为初次射流火、稳定燃烧、多次射流火以及火焰熄灭等阶段;燃烧行为会进一步加速电池温度的上升,而对于荷电状态较高的电池,内短路是造成其温度迅速跃升的关键因素;荷电状态较高的电池燃烧过程更加剧烈,具体表现为电池温度、热释放速率(HRR)、燃烧热将会更高,相应电池的燃烧时间也将更加短暂.此外,高温会造成电池电压的微量衰减,但是电池的安全泄压时间往往早于电压跳水时间.本研究结果旨在为锂离子电池系统在储能、变电等领域的安全设计及火灾防控技术提供理论和技术支撑.     

基于自适应灾变遗传-循环神经网络的锂离子电池SOC估计

锂离子荷电状态(State of charge,SOC)的精准估计是锂离子电池安全稳定运行的基础.传统的误差反向传播(Back propagation,BP)神经网络估计SOC的精度不高,而循环神经网络(Recurrent neural network,RNN)也容易陷入局部最优.针对这些问题,提出了自适应灾变遗传-循...     

液流电池理论与技术——荷电状态的表征

电池实际可放出的瓦时容量与实际可放出的最大瓦时容量的比值定义为荷电状态,准确测定荷电状态对储能应用十分重要。本文从理论和应用角度,讨论全钒液流电池荷电状态的理论概念、工程定义和主要影响因素;提出2种确定最大瓦时容量的方法,其中实测法准确度更高,包含钒离子跨膜迁移、水分子扩散、负极电解液析氢和被氧化的信息,用于表征储能系统的荷电状态具有实际价值;阐述最大瓦时容量、电化学瓦时容量和理论瓦时容量的区别与联系。所提出的荷电状态确定方法,能够用于全钒液流电池SOC的估计。     

Theoretical and technological aspects of flow batteries：Measurement of state of charge

电池实际可放出的瓦时容量与实际可放出的最大瓦时容量的比值定义为荷电状态,准确测定荷电状态对储能应用十分重要。本文从理论和应用角度,讨论全钒液流电池荷电状态的理论概念、工程定义和主要影响因素;提出2种确定最大瓦时容量的方法,其中实测法准确度更高,包含钒离子跨膜迁移、水分子扩散、负极电解液析氢和被氧化的信息,用于表征储能系统的荷电状态具有实际价值;阐述最大瓦时容量、电化学瓦时容量和理论瓦时容量的区别与联系。所提出的荷电状态确定方法,能够用于全钒液流电池SOC的估计。     

Experimental validation for Li-ion battery modeling using Extended Kalman Filters

The battery management systems (BMS) is an essential emerging component of both electric and hybrid electric vehicles (HEV) alongside with modern power systems. With the BMS integration, safe and reliable battery operation can be guaranteed through the accurate determination of the battery state of charge (SOC), its state of health (SOH) and the instantaneous available power. Therefore, undesired power fade and capacity loss problems can be avoided. Because of the electrochemical actions inside the battery, such emerging storage energy technology acts differently with operating and environment condition variations. Consequently, the SOC estimation mechanism should cope with the probable changes and uncertainties in the battery characteristics to ensure a permanent precise SOC determination over the battery lifetime.This paper aims to study and design the BMS for the Li-ion batteries. For this purpose, the system mathematical equations are presented. Then, the battery electrical model is developed. By imposing known charge/discharge current signals, all the parameters of such electrical model are identified using voltage drop measurements. Then, the extended kalman filter (EKF) methodology is employed to this nonlinear system to determine the most convenient battery SOC. This methodology is experimentally implemented using C language through micro-controller. The proposed BMS technique based on EKF is experimentally validated to determine the battery SOC values correlated to those reached by the Coulomb counting method with acceptable small errors.     

A review on battery management system from the modeling efforts to its multiapplication and integration

Progress in battery technology accelerates the transition of battery management system (BMS) from a mere monitoring unit to a multifunction integrated one. It is necessary to establish a battery model for the implementation of BMS's effective control. With more comprehensive and faster battery model, it would be accurate and effective to reflect the behavior of the battery level to the vehicle. On this basis, to ensure battery safety, power, and durability, some key technologies based on the model are advanced, such as battery state estimation, energy equalization, thermal management, and fault diagnosis. Besides, the communication of interactions between BMS and vehicle controllers, motor controllers, etc is an essential consideration for optimizing driving and improving vehicle performance. As concluded, a synergistic and collaborative BMS is the foundation for green‐energy vehicles to be intelligent, electric, networked, and shared. Thus, this paper reviews the research and development (R&D) of multiphysics model simulation and multifunction integrated BMS technology. In addition, summary of the relevant research and state‐of‐the‐art technology is dedicated to improving the synergy and coordination of BMS and to promote the innovation and optimization of new energy vehicle technology.     

Second-order RC modeling and parameter identification of electric vehicle power battery

建立精确的动力电池模型是电池管理系统（battery management system,BMS）开发过程中的重要环节,电池系统具有较强的非线性特性,其模型参数随多种因素的变化而变化。在电池模型参数辨识过程中,考虑的可变因素越多,辨识结果越准确,但模型的运行速度将降低,影响其实际应用。在各种可变因素中,电池荷电状态（state of charge,SOC）对电池模型参数的影响最为显著,对不同SOC下电池模型参数进行辨识并应用于电池模型,将在提高模型精度的同时保持较好的实时性。本文以动力锂电池为对象,采用二阶RC等效电路模型,通过试验得到电池组在不同SOC下的回弹电压数据,采用最小二乘拟合法辨识不同SOC状态下的模型参数。在此基础上搭建模型参数随SOC变化的实时仿真模型,并对模型进行仿真和试验验证,结果表明模型具有较高的精度和实时性。     

A comparative study of lumped equivalent circuit models of a lithium battery for state of charge prediction

Battery modeling plays an important role in remaining range prediction and battery management system development. An accurate and realistic battery model is essential to design an efficient electric storage system. The goal of this paper is to investigate the performance of different circuit topologies for diffusion process in the equivalent circuit models (ECMs). The theory of diffusion process approximation by using resistive‐capacitor (RC) networks is explained in frequency domain. The terminal voltage predictive capabilities of the ECMs are compared and validated with test data. The numerical simulation results show that model prediction accuracy and computation burdens increase along with the number of RC pairs. The ECM with three RC networks is the best choice in terms of the balance between accuracy and complexity for ternary lithium batteries. In addition, a novel method of combining unscented Kalman filter (UKF) algorithm with initial state of charge (SOC) acceleration convergence for SOC estimation is proposed. The results of urban dynamometer driving schedule (UDDS) show that ECM with three RC networks has the best comprehensive performance on calculation cost and SOC estimation accuracy.     

Design of a thermal management system for a battery pack in an electric vehicle using Dymola

As the world moves toward more green and efficient means of modes of transport, electric vehicles are the most suitable and ideal choice to fulfill this requirement. Rapid developments in the field of battery technology are the main reason for their progress, but thermal management in such systems has been an area of concern for a long time. The work undertaken is to design and develop a battery management system (BMS) with a specific focus on the thermal behavior of the battery pack with varying vehicle loads as well as environmental conditions. To design an efficient BMS, one needs to model the battery behavior covering the thermal as well as electrical aspects of the battery. Apart from the battery model, a mathematical model of the electrical vehicle to mimic the various road load conditions for battery also needs to be modeled. Depending on the need for cooling based on battery behavior, the cooling circuit is modeled for the battery pack used. The entire study has been carried out using Dymola, a mathematical modeling software.     

工作温度对磷酸铁锂电池SOC影响及研究进展

储能电池在新能源并网、新能源汽车等产业领域发挥着重要作用,为了对电池进行有效地控制与管理,需要配备必要的电池管理系统,电池荷电状态（SOC）是其中最为重要的一环。磷酸铁锂（LiFePO4,LFP）电池SOC与多个影响因素密切相关,呈强非线性,本文重点归纳温度对磷酸铁锂电池SOC的影响。首先将工作温度对开路电压、实际容量、充放电效率、自放电率及电池老化等电池特性的影响进行归纳总结,随后通过对工作温度的影响规律进行分析、总结和归纳,基于经典“开路电压 + 安时积分”法将温度参数直接或间接引入到SOC的实时估算模型中,得到考虑温度参数的新模型,进而提高电池SOC的估算精度。     

An online state of health estimation method based on battery management system monitoring data

A state of health (SOH) estimation method that can be achieved online and only requires battery management system (BMS) detection data is proposed in this article. In the State of Health mathematical model proposed in this article, the using time of power battery is treated as an independent variable and SOH is treated as a hidden variable. And the mathematical model just used online process data from BMS. So it would make the SOH estimation method more suitable for actual engineering. Then, the article proposes an interleaved time model parameter update framework to reduce the computational complexity of the algorithm in a single sampling period. In this framework, we propose a fast model parameter identification algorithm that uses nonlinear least squares to initialize a genetic algorithm searched range. Finally, the whole method is verified by using the NASA database. The results prove that the proposed online SOH estimation method has higher SOH estimation accuracy and is more suitable for engineering applications in the field of electric vehicles than the existing SOH estimation methods.     

Model‐based unscented Kalman filter observer design for lithium‐ion battery state of charge estimation

Accurate battery state‐of‐charge is essential for both driver notification and battery management units reliability in electric vehicle/hybrid electric vehicle. It is necessary to develop a robust state of charge (SOC) estimation approach to cope with nonlinear dynamic battery systems. This paper proposed an estimation method to identify the SOC online based on equivalent circuit battery model and unscented Kalman filter technique. Firstly, the parameters of dynamic battery model are identified offline and validated through typical electric vehicle road operation to guarantee its precision. Then the performance with respect to converge time, observer accuracy, robustness against system modeling errors, and mismatched initial SOC guess values is investigated. The accuracy of proposed estimation algorithm is validated under improved hybrid power pulse characterization test and New European Driving Cycle. Experiment and numerical simulation results clearly demonstrate that the proposed method is highly reliable with good robustness to different operating conditions and battery aging.     

Research on parameter identification and state of charge estimation of improved equivalent circuit model of Li-ion battery based on temperature effects for battery thermal management

The performance and parameters of Li-ion battery are greatly affected by temperature. As a significant battery parameter, state of charge (SOC) is affected by temperature during the estimation process. In this paper, an improved equivalent circuit model (IECM) considering the influence of ambient temperatures and battery surface temperature (BST) on battery parameters based on second-order RC model have been proposed. The exponential function fitting (EFF) method was used to identify battery model parameters at 5 ambient temperatures including −10°C, 0°C, 10°C, 25°C and 40°C, fitting the relationship between internal resistance and BST. Then, the SOC of the IECM was estimated based on the extended Kalman filter (EKF) algorithm. Using the result calculated by the Ampere-hour integration method as the standard, the data of battery under open circuit voltage (OCV) test profile and dynamic stress test (DST) profile at different ambient temperatures has been compared with the ordinary second-order RC model, and the advantages of the SOC estimation accuracy with IECM was verified. The numerical results showed that the IECM can improve the estimation accuracy of battery SOC under different operating conditions.