Thermal safety boundary of lithium-ion battery at different state of charge

Thermal runaway(TR)is a critical issue hindering the large-scale application of lithium-ion batteries(LIBs).Understanding the thermal safety behavior of LIBs at the cell and module level under different state of charges(SOCs)has significant implications for reinforcing the thermal safety design of the lithium-ion battery module.This study first investigates the thermal safety boundary(TSB)correspondence at the cells and modules level under the guidance of a newly proposed concept,safe electric quantity boundary(SEQB).A reasonable thermal runaway propagation(TRP)judgment indicator,peak heat transfer power(PHTP),is proposed to predict whether TRP occurs.Moreover,a validated 3D model is used to quantitatively clarify the TSB at different SOCs from the perspective of PHTP,TR trigger temperature,SOC,and the full cycle life.Besides,three different TRP transfer modes are discovered.The interconversion relationship of three different TRP modes is investigated from the perspective of PHTP.This paper explores the TSB of LIBs under different SOCs at both cell and module levels for the first time,which has great significance in guiding the thermal safety design of battery systems.

Deep learning-based battery state of charge estimation:Enhancing estimation performance with unlabelled training samples

The estimation of state of charge(SOC)using deep neural networks(DNN)generally requires a considerable number of labelled samples for training,which refer to the current and voltage pieces with knowing their corresponding SOCs.However,the collection of labelled samples is costly and time-consuming.In contrast,the unlabelled training samples,which consist of the current and voltage data with unknown SOCs,are easy to obtain.In view of this,this paper proposes an improved DNN for SOC estimation by effectively using both a pool of unlabelled samples and a limited number of labelled samples.Besides the traditional supervised network,the proposed method uses an input reconstruction network to reformulate the time dependency features of the voltage and current.In this way,the developed network can extract useful information from the unlabelled samples.The proposed method is validated under different drive cycles and temperature conditions.The results reveal that the SOC estimation accuracy of the DNN trained with both labelled and unlabelled samples outperforms that of only using a limited number of labelled samples.In addition,when the dataset with reduced number of labelled samples to some extent is used to test the developed network,it is found that the proposed method performs well and is robust in producing the model outputs with the required accuracy when the unlabelled samples are involved in the model training.Furthermore,the proposed method is evaluated with different recurrent neural networks(RNNs)applied to the input reconstruction module.The results indicate that the proposed method is feasible for various RNN algorithms,and it could be flexibly applied to other conditions as required.

Physics-based battery SOC estimation methods:Recent advances and future perspectives

The reliable prediction of state of charge(SOC)is one of the vital functions of advanced battery management system(BMS),which has great significance towards safe operation of electric vehicles.By far,the empirical model-based and data-driven-based SOC estimation methods of lithium-ion batteries have been comprehensively discussed and reviewed in various literatures.However,few reviews involving SOC estimation focused on electrochemical mechanism,which gives physical explanations to SOC and becomes most attractive candidate for advanced BMS.For this reason,this paper comprehensively surveys on physics-based SOC algorithms applied in advanced BMS.First,the research progresses of physical SOC estimation methods for lithium-ion batteries are thoroughly discussed and corresponding evaluation criteria are carefully elaborated.Second,future perspectives of the current researches on physics-based battery SOC estimation are presented.The insights stated in this paper are expected to catalyze the development and application of the physics-based advanced BMS algorithms.

Review of lithium-ion battery state of charge estimation

The technology deployed for lithium-ion battery state of charge(SOC)estimation is an important part of the design of electric vehicle battery management systems.Accurate SOC estimation can forestall excessive charging and discharging of lithium-ion batteries,thereby improving discharge efficiency and extending cycle life.In this study,the key lithium-ion battery SOC estimation technologies are summarized.First,the research status of lithium-ion battery modeling is introduced.Second,the main technologies and difficulties in model parameter identification for lithium-ion batteries are discussed.Third,the development status and advantages and disadvantages of SOC estimation methods are summarized.Finally,the current research problems and prospects for development trends are summarized.

A new state of charge determination method for battery management system

State of Charge (SOC) determination is an increasingly important issue in battery technology. In addition to the immediate display of the remaining battery capacity to the user, precise knowledge of SOC exerts additional control over the charging/discharging process which in turn reduces the risk of over-voltage and gassing, which degrade the chemical composition of the electrolyte and plates. This paper describes a new approach to SOC determination for the lead-acid battery management system by combining Ah-balance with an EMF estimation algorithm, which predicts the battery’s EMF value while it is under load. The EMF estimation algorithm is based on an equivalent-circuit representation of the battery, with the parameters determined from a pulse test performed on the battery and a curve-fitting algorithm by means of least-square regression. The whole battery cycle is classified into seven states where the SOC is estimated with the Ah-balance method and the proposed EMF based algorithm. Laboratory tests and results are described in detail in the paper.

Parameter identification and state-of-charge estimation approach for enhanced lithium–ion battery equivalent circuit model considering influence of ambient temperatures

It is widely accepted that the variation of ambient temperature has great influence on the battery model parameters and state-of-charge(SOC) estimation, and the accurate SOC estimation is a significant issue for developing the battery management system in electric vehicles. To address this problem, in this paper we propose an enhanced equivalent circuit model(ECM) considering the influence of different ambient temperatures on the open-circuit voltage for a lithium-ion battery. Based on this model, the exponential-function fitting method is adopted to identify the battery parameters according to the test data collected from the experimental platform. And then, the extended Kalman filter(EKF) algorithm is employed to estimate the battery SOC of this battery ECM. The performance of the proposed ECM is verified by using the test profiles of hybrid pulse power characterization(HPPC) and the standard US06 driving cycles(US06) at various ambient temperatures, and by comparing with the common ECM with a second-order resistance capacitor. The simulation and experimental results show that the enhanced battery ECM can improve the battery SOC estimation accuracy under different operating conditions.

Application of Power Electronics and Control for Dual Battery Packs Management with Voltage Balancing and State of Charge Estimation

Energy storage, such as lead acid batteries, is necessary for renewable energy sources’ autonomy because of their intermittent nature, which makes them more frequently used than traditional energy sources to reduce operating costs. The battery storage system has to be monitored and managed to prevent serious problems such as battery overcharging, over-discharging, overheating, battery unbalancing, thermal runaway, and fire dangers. For voltage balancing between batteries in the pack throughout the charging period and the SOC estimate, a modified lossless switching mechanism is used in this research’s suggested battery management system. The OCV state of charge calculation, in the beginning, was used in conjunction with the coulomb counting approach to estimate the SOC. The results reveal that correlation factor K has an average value of 0.3 volts when VM ≥ 12 V and an average value of 0.825 when VM ≤ 12 V. The battery monitoring system revealed that voltage balancing was accomplished during the charging process in park one after 80 seconds with a SOC difference of 1.4% between Batteries 1 and 2. On the other hand, the system estimates the state of charge during the discharging process in two packs, with a maximum DOD of 10.8 V for all batteries. The project’s objectives were met since the BMS estimated SOC and achieved voltage balance.

Reduced Switching-Frequency State of Charge Balancing Strategy for Battery Integrated Modular Multilevel Converter

A modular multilevel converter(MMC)integrated with split battery cells(BIMMCs)is proposed for the battery management system(BMS)and motor drive system.In order to reduce the switching losses,the state of charge(SOC)balancing strategy with a reduced switching-frequency(RSF)is proposed in this paper.The proposed RSF algorithm not only reduces the switching losses,but also features good balancing performance both in the unbalanced and balanced initial states.The results are verified by extensive simulations in MATLAB/Simulink surroundings.

Adaptive Kalman filter based state of charge estimation algorithm for lithium-ion battery

In order to improve the accuracy of the battery state of charge（SOC） estimation, in this paper we take a lithiumion battery as an example to study the adaptive Kalman filter based SOC estimation algorithm. Firstly, the second-order battery system model is introduced. Meanwhile, the temperature and charge rate are introduced into the model. Then, the temperature and the charge rate are adopted to estimate the battery SOC, with the help of the parameters of an adaptive Kalman filter based estimation algorithm model. Afterwards, it is verified by the numerical simulation that in the ideal case, the accuracy of SOC estimation can be enhanced by adding two elements, namely, the temperature and charge rate.Finally, the actual road conditions are simulated with ADVISOR, and the simulation results show that the proposed method improves the accuracy of battery SOC estimation under actual road conditions. Thus, its application scope in engineering is greatly expanded.

Kalman Filters versus Neural Networks in Battery State-of-Charge Estimation: A Comparative Study

Battery management systems (BMS) must estimate the state-of-charge (SOC) of the battery accurately to prolong its lifetime and ensure a reliable operation. Since batteries have a wide range of applications, the SOC estimation requirements and methods vary from an application to another. This paper compares two SOC estimation methods, namely extended Kalman filters (EKF) and artificial neural networks (ANN). EKF is a nonlinear optimal estimator that is used to estimate the inner state of a nonlinear dynamic system using a state-space model. On the other hand, ANN is a mathematical model that consists of interconnected artificial neurons inspired by biological neural networks and is used to predict the output of a dynamic system based on some historical data of that system. A pulse-discharge test was performed on a commercial lithium-ion (Li-ion) battery cell in order to collect data to evaluate those methods. Results are presented and compared.

引入PID反馈的SHAEKF算法估算电池SOC

电池荷电状态(SOC)的估算精度是电动汽车电池组的重要指标。为提升SOC估算精度,在融合Sage-Husa扩展卡尔曼滤波(SHEKF)算法与自适应扩展卡尔曼滤波(AEKF)算法的基础上,增加比例积分微分(PID)反馈环节,形成改进算法。采用粒子群优化(PSO)算法对二阶RC等效电路模型进行参数辨识;用开源电池数据集对模型和算法进行实验和分析。改进的SHAEKF算法在电池动态应力测试(DST)、北京动态应力测试(BJDST)和美国联邦城市驾驶(FUDS)等工况下的平均估计误差都在1%以内,与单纯的融合算法SHAEKF算法相比,最大误差可减小5%。

基于AR-ECM平均差异模型的串联电池组SOC、容量多尺度联合估计方法

考虑电池单体老化差异所致的电池组不一致性,针对串联电池组荷电状态(state of charge,SOC)、容量估计问题,提出一种基于自回归等效电路模型(autoregression equivalent circuit model,AR-ECM)的平均差异模型(mean-difference model,MDM)。基于此模型,提出串联电池组SOC、容量多尺度联合估计算法。该算法由2个部分组成,一是基于AR-ECM的MDM及差异化模型参数辨识策略:条件辨识策略和定频分组辨识策略;二是基于多时间尺度H无穷滤波(multi-timescale H infinity filter,Mts-HIF)的电池组SOC、容量联合估计算法。通过将所提出MDM中的自回归平均模型(autoregression mean model,AR-MM)与传统MDM中的n阶RC平均模型(nRC mean model,nRC-MM)比较,结果表明所提出的AR-MM在复杂运行工况下具有更优的动态跟随性能。依据最小化信息量准则(akaike information criterion,AIC),AR-MM具有更优的复杂度与精度的权衡。通过与基于多时间尺度扩展卡尔曼滤波(multi-timescale extended Kalman filter,Mts-EKF)联合状态估计算法比较,结果表明所提出的Mts-HIF状态估计算法具有更优的鲁棒性、精度和收敛速度。

基于多新息扩展卡尔曼滤波的锂离子电池SOC估计

锂电池具有高能量密度、循环寿命长等优点而被广泛应用于电动汽车动力装置,但车辆运行状况复杂多变,且电池内部呈现高度非线性的性质,导致电池荷电状态(state of charge, SOC)难以准确计算。为优化锂电池SOC估计精度,构建结合Warburg元件的分数阶二阶RC模型,采用自适应遗传算法进行参数辨识;融合多新息理论和扩展卡尔曼滤波算法,提出基于多新息扩展卡尔曼滤波(multi innovation extended Kalman filter, MIEKF)的锂离子电池SOC估计算法,并利用试验数据验证该方法的有效性,为提高SOC估计精度和车载锂电池的循环使用寿命提供了新的方法途径和实践支撑。

基于LWOA-LSTM的大容量锂电池SOC估计

准确预测锂电池荷电状态(SOC)对电池安全运行至关重要,分析在电网不同模式下的SOC更是锂电池全面推广的基础。提出一种基于莱维飞行的鲸鱼优化算法(LWOA)优化长短时记忆神经网络(LSTM),对调频模式下的大容量锂离子电池SOC进行估计。首先,分析LSTM神经网络和LWOA算法,构建LWOA-LSTM模型,进行参数优化;然后,选取调频模式下大容量锂离子电池组实验数据,对数据进行预处理和模型训练;最后,实现调频模式下锂电池的SOC估计。试验结果表明:所构建模型能准确预测锂电池SOC,较WOA-LSTM模型,评估指标RMSE和MAE分别降低了25.55%、28.71%,R^(2)上升了0.76%。

A Two-Layer Fuzzy Control Strategy for the Participation of Energy Storage Battery Systems in Grid Frequency Regulation

To address the frequency fluctuation problem caused by the power dynamic imbalance between the power system and the loadwhen a large number of newenergy sources are connected to the grid,a two-layer fuzzy control strategy is proposed for the participation of the energy storage battery system in FM.Firstly,considering the coordination of FM units responding to automatic power generation control commands,a comprehensive allocation strategy of two signals under automatic power generation control commands is proposed to give full play to the advantages of two FM signals while enabling better coordination of two FM units responding to FM commands;secondly,based on the grid FM demand and battery FM capability,a double-layer fuzzy control strategy is proposed for FM units responding to automatic power generation control commands in a coordinated manner under dual-signal allocation mode to precisely allocate the power output depth of FM units,which can control the fluctuation of frequency deviation within a smaller range at a faster speed while maintaining the battery charge state;finally,the proposed Finally,the proposed control strategy is simulated and verified inMatlab/Simulink.The results show that the proposed control strategy can control the frequency deviation within a smaller range in a shorter time,better stabilize the fluctuation of the battery charge level,and improve the utilization of the FM unit.

21700锂离子电池在不同SOC下的热失控实验研究

为提升电池热安全、减少新能源汽车热灾害,揭示不同荷电状态(SOC)下对电池热失控危害的影响机制。在SOC为100%~0%几个荷电状态下研究了21700锂电池的热失控特性,包括电池在热失控当中的表面温度、工作电压、质量损失、能量、TNT当量和破坏半径等。结果表明:电池的温升幅度随SOC的增大而升高,高电量电池热失控触发所需的时间更短,100%SOC电池在603 s触发热失控,相比于25%SOC缩短了59.1%,其危险系数更大;SOC越大,电池热失控后的质量损失也越大;电池热失控过程释放的能量、TNT当量与破坏半径均随SOC的增加而增大,电池的热失控危害性与SOC之间呈现出正相关关系。

基于SOC的串联连接锂电池能量均衡控制研究

串联锂电池的SOC均衡控制对提高电池寿命具有重要意义。针对锂电池单体SOC表现出离散性的不同情况,本文研究了一种主动均衡与被动均衡相结合的混合均衡方案,其中主动均衡器拓扑由多绕组反激变换器实现,被动均衡器由电阻与开关组成并联在单体电池两端,详细分析了混合均衡器的工作原理。在控制策略上讨论了锂电池SOC的离散性对均衡速度的影响,引入表征SOC离散度的标准差和表征离散原因的系数以实现SOC不同离散情况下的快速均衡。所提出的混合均衡器拓扑和控制方案能够使耗能与均衡速度获得优化,实验结果验证了文中理论的可行性。

全钒液流电池建模及SOC在线估计研究进展

全钒液流电池(VRFB)具有高安全、长寿命的优势,在大规模电力储能领域中具有广阔的应用前景。高精度的电池模型及准确的电池荷电状态(SOC)估计是全钒液流电池实际应用的重要技术基础,也是其规模应用面临的主要挑战。本文对全钒液流电池仿真模型、模型参数辨识、SOC监测与在线估计,以及全钒液流电池特有的SOC估计影响因素进行综述。首先介绍了电化学模型和等效电路模型2类仿真模型,分析比较了几种用于VRFB的等效电路模型的原理及优缺点。重点综述了全钒液流电池荷电状态监测方法,包括:安时积分法、开路电压法、电位滴定法、电导率法和光学分析法,以及更具工程应用前景的荷电状态在线估计方法。总结了全钒液流电池模型参数离线与在线辨识技术,介绍了基于滤波算法与数据驱动算法的荷电状态在线估计方法。在全钒液流电池SOC估计特异性影响因素方面,讨论了包括钒离子的跨膜迁移、负极氧化副反应、负极析氢反应和温度对参数辨识与荷电状态估计的影响规律,总结展望了全钒液流电池建模及SOC在线估计面临的问题及未来研究方向。

Fuzzy Model for Estimation of the State-of-Charge of Lithium-Ion Batteries for Electric Vehicles

A fuzzy model was established to estimate the state of charge（SOC） of a lithium-ion battery for electric vehicles.The robust Gustafson-Kessel（GK） clustering algorithm based on clustering validity indices was applied to identify the structure and antecedent parameters of the model.The least squares algorithm was utilized to determine the consequent parameters.Validation results show that this model can provide accurate SOC estimation for the lithium-ion battery and satisfy the requirement for practical electric vehicle applications.

数据分布多样性对锂电池SOC预测的泛化影响

数据驱动模型预测荷电状态(SOC)依赖高质量的实验数据,在应用于实际使用场景下的分布多样的锂电池组数据时会出现预测的准确性不稳定即泛化能力差的情况,限制了模型的实际应用。研究实际场景下的大规模数据的分布多样性对SOC预测模型的泛化性影响具有重要意义。因此,对32个锂电池组的实际运行数据集进行研究,采用经典算法与多输入多输出(MIMO)策略结合来预测多步SOC,对每份数据分别建立模型进行SOC预测,研究了不同算法的应用效果并分析了数据分布多样性对模型的泛化能力的影响规律。结果表明:对大规模的锂电池组数据,LR-MIMO模型训练精度普遍优于RF-MIMO、KNN-MIMO、LSTM-MIMO模型,其预测未来0.5 h的SOC的R^(2)一般在0.98及以上,MAPE基本低于0.05。与其他模型相比,LR-MIMO模型有优秀的预测性能,预测其他数据集的R^(2)基本在0.95以上。而KNN-MIMO模型的预测精度与RF-MIMO模型相当,R^(2)大致在0.7以上,LSTM-MIMO模型的预测性能因数据集不同存在较明显的差异;当数据满足SOC与电压的相关系数≥0.9、SOC和电压分布范围广、核密度曲线呈左偏趋势、分布较均匀时,可使模型训练精度提高。