Active cell balancing of lithium-ion battery pack based on average state of charge

Differences in the environment and parameters of lithium-ion battery (LiB) cells may lead the residual capacity between the battery cells to be inconsistent, and the battery cells may be damaged due to overcharging or overdischarging. In this study, an active balancing method for charging and discharging of LiB pack based on average state of charge (SOC) is proposed. Two different active balancing strategies are developed according to the different charging and discharging states of LiB pack. When the LiB pack is charging, charging balance strategy is performed, wherein the battery cells whose SOC is higher than the average SOC of the LiB pack are balanced to increase the charging capacity of the entire LiB pack. When the LiB pack is discharging or static standing, discharging balance strategy is performed, wherein the batter cells whose SOC is lower than the average SOC of the LiB pack are balanced to increase the discharging capacity of the entire LiB pack. The experimental results show that the proposed active balancing method can reduce the inconsistency of residual energy between the battery cells and improve the charging and discharging capacity of the LiB pack.     

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.     

Influence of equalization on LiFePO4 battery inconsistency

Capacity inconsistency among cells is a serious problem in battery energy storage systems. The inconsistency reduces battery pack lifetimes and increases their usage costs. Active balancing is an effective technique for performance equalization between cells. Here, a quantitative analysis method based on an active balancing circuit is developed to explain how battery capacity inconsistency is affected by balancing parameters. Four balancing parameters, namely, charge/discharge current rate, initial capacity difference before balancing, balancing current, and balancing time, have been considered to explain the effects on battery capacity consistency. The results indicate that (1) the inconsistency of batteries can be reduced more effectively for discharging with active balancing at small current rates; (2) for an improved balancing effect, it is necessary to maintain a high ratio (β) of balancing current to charging/discharging current during the balancing process; thus, a larger balancing current is significant; (3) the value of capacity difference reduction ratio (α) grows more dramatically in discharging at large initial battery capacity differences; and (4) when the balancing current only marginally changes, following the increase of current rate, the ratio (γ) of balancing time to charging duration decreases and the balancing effect becomes inconspicuous in charging, while γ increases and the balancing effect reduces in discharging. These influence regulations can help us make and optimize balancing control strategies for LiFePO₄ batteries. Copyright © 2017 John Wiley & Sons, Ltd.     

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.     

Active equalization control strategy of Li-ion battery based on state of charge estimation of an electrochemical-thermal coupling model

Aiming at solving the problem of poor battery cell consistency caused by excessive decay of cell capacity or increased internal resistance during the operation of lithium-ion battery packs for vehicles, the paper proposes an active equalization control with 12-V power supply as an equalization energy source, which achieves efficient energy replenishment of individual cells with low power. The electrochemical-thermal coupling model of lithium-ion battery is built, and the order reduction of large-scale system theory ensures that the model had higher accuracy and lower amount of calculation, which is suitable for vehicle battery management system (BMS). Then the extended Kalman filter algorithm is used to calculate the real-time state of charge (SOC) of each cell and set as an equalization variable. The equalization simulation circuit is built with MATLAB/Simulink, the experimental platform of active equalization system for battery packs is constructed, and the battery packs are tested for equalization in static state. The simulation and experimental results show that the proposed active equalization control strategy can rapidly improve the voltage inconsistency between single cells, and the energy transfer efficiency can reach about 85% during the equalization process.     

Inflection point Ah-total integration method for real-time integration to correct lithium battery SOC

对18650及26650磷酸铁锂电池的充放电电流、电压等数据分析表明：在电池循环老化过程中，虽然容量电压曲线两端曲率最大（拐点）处的SOC值有所变化，但是其电压保持不变。因此在估算SOC过程中，当放电电压达到拐点电压时，将此时的SOC修正为对应的拐点SOC，可以一定程度上优化安时积分法由于初始SOC而估算不准的问题。在此基础上提出一种新型的拐点修正安时积分算法，综合考虑温度、充放倍率、循环老化等因素对SOC估算精度的影响，引入充放电曲线拐点概念，建立SOC实时估算数学模型，减小消除安时积分法存在的累计误差问题。对比传统安时积分法估算精度，结果表明：SOC拐点修正安时积分实时估算法的误差在3%，说明该方法在实际工况中具有可行性，并且估算精度较高，可为SOC实时估算与检测提供重要参考。     

Shunt current loss of the vanadium redox flow battery

The shunt current loss is one of main factors to affect the performance of the vanadium redox flow battery, which will shorten the cycle life and decrease the energy transfer efficiency. In this paper, a stack-level model based on the circuit analog method is proposed to research the shunt current loss of the vanadium redox flow battery, in which the SOC (state of charge) of electrolyte is introduced. The distribution of shunt current is described in detail. The sensitive analysis of shunt current is reported. The shunt current loss in charge/discharge cycle is predicted with the given experimental data. The effect of charge/discharge pattern on the shunt current loss is studied. The result shows that the reduction of the number of single cells in series, the decrease of the resistances of manifold and channel and the increase of the power of single cell will be the further development for the VRFB stack.     

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.     

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.     

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.     

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.     

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。     

5kW锂电池模组BMS的研究与开发

设计了以MSP430为控制核心的用于5 k W锂电池管理系统(BMS).建立了关于电池荷电状态的模型,在实际估计中,采用开路电压和按时积分相结合的方法且有较高的精度;采取电池均衡充电的方案,补偿了电池容量的差异性,进而使得电池组的使用寿命延长.电池荷电状态估算的改进方案解决了按时计量法无法确定初始荷电状态、难以精准测得库仑效率等问题,确保了电池管理系统处于稳定工作状态.该系统具有抗干扰能力极强、硬件电路可靠、且十分经济的特点.经过实验验证,利用该系统进行SOC剩余容量估计的结果较为精确.     

Estimation of power battery SOC based on firefly BP neural network

针对BP神经网络算法对电动汽车电池荷电状态（state of charge,SOC）估算的缺陷,提出一种基于萤火虫（firefly algorithm,FA）神经网络的SOC估算方法。以磷酸铁锂电池为测试对象,在ARBIN公司生产的EVTS电动车动力电池测试系统装置上进行测试,收集锂电池的各项性能参数。采用端电压和放电电流作为输入参数,SOC作为输出参数,建立FA-BP神经网络模型,用于估算锂离子电池充放电过程中的任一状态下的SOC。仿真实验结果表明,与现有的BP神经网络估算方法相比,基于FA-BP神经网络的锂电池SOC估算方法准确度高,具备很好的实用性。     

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.     

SOC estimation of lithium battery based on adaptive untracked Kalman filter

电池荷电状态（SOC）的准确估计是电池管理系统的关键问题,对电池的可靠性和安全性至关重要。由于多数情况下建立的电池模型精度不够高、电池系统的噪声统计是未知的或不准确的,这都会对锂离子电池系统的SOC估计会产生较大影响。本文采用二阶RC等效模型,可减小电池模型带来的误差;同时结合SageHusa滤波算法与无迹卡尔曼滤波（UKF）算法提出了一种新的SOC估计方法,基于噪声统计估计器的自适应无迹卡尔曼（AUKF）滤波算法,它可以对系统噪声进行实时修正以提高SOC的估算精度。并通过比较AUKF和UKF来验证SOC估计方法的准确性和有效性。实验结果表明,AUKF具有更高的SOC估计精度和自适应能力,在脉冲放电工况和动态工况下的估计精度均能保持在4.68%以内,可以有效地估计电池的SOC值。     

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。     

The balancing efficiency analysis of the switched-capacity method

在大规模的电池储能应用中,单体电池需要串并联应用,以使电压和容量达到要求,而生产,使用过程中的差异导致电池单体的不一致性是影响储能电站寿命的主要因素之一.储能电池在长期充放电过程中,蓄电池组内各单体电压的离散性会逐渐变大,致使整组电池寿命下降甚至失效,因此普遍采取各种均衡电路降低电池不一致性的影响.本文对几种常用的蓄电池组均衡电路做了简单的比较,包括能量耗散型均衡电路和能量非耗散型均衡电路,着重对开关电容法工作原理及其工作效率进行了分析,得出开关电容法的均衡效率与电池初始状态有关,而与电容容值大小无关等结论.     

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.     

A comprehensive review on inconsistency and equalization technology of lithium-ion battery for electric vehicles

The rapid growth of transportation demand has been enlarged strongly which has promoted electric vehicles powered by lithium-ion batteries. However, the inconsistencies within the battery pack will deteriorate over the lifecycle and affect the performance of electric vehicles. Therefore, various thermal management systems and equalization systems have been applied in battery management system to deal with the inconsistencies, extend battery service life, and improve safety performance. This review summarizes the origination of inconsistency within lithium-ion batteries from production to usage process, and then introduces the classification methods and application scenarios of the balance management system in detail. Based on the circuit topology, equalization systems can be classified into passive and active topologies. Active topologies are widely researched due to the advantages of high equalization efficiency and high speed, and the state-of-art innovations are presented and compared from the prospective of circuit, energy flow, efficiency and system complexity. In addition, this review focuses on the mainstream equalization strategies based on the analysis of balancing variables and control algorithms in terms of efficiency, complexity and stability, especially in the areas of variables optimal selection and advanced control algorithms. It is expected that innovations such as cloud control methods and hybrid balancing systems equipped with thermal management will become the future direction of lithium-ion equalization technologies.