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.     

Estimation of the state-of-charge of gel lead-acid batteries and application to the control of a stand-alone wind-solar test-bed with hydrogen support

In hybrid renewable energy systems, batteries act as a DC bus to provide constant voltage and to smooth out commutations between the generating devices. These batteries are usually of a lead-acid type and operate under harsh variable conditions due to fluctuations of both solar radiation and wind speed. Precise knowledge of the state-of-charge of the batteries, and hence of their available energy, play a key role in effecting efficient control and energy management of the installation. The present study had a twofold aim. One objective was to adjust and validate a method based on coulomb counting to estimate the state-of-charge (SOC) of a gelled lead-acid battery which is the DC bus of a hybrid wind-solar system with hydrogen storage. Other works evaluate SOC models based on several parameters, however, the present proposal based on experimental measurements involves only a few parameters. The second objective was to modify the installation's control algorithm to use the battery's calculated SOC as control parameter instead of its voltage. The results of a test-bed system, showing how the system evolved under real operating conditions, constitute a proof-of-concept of the validity of the method.     

Determination of state-of-charge and state-of-health of batteries by fuzzy logic methodology

A practical method of predicting state-of-charge (SOC) and state-of-health (SOH) of battery systems has been developed and tested for several systems. The method involves the use of fuzzy logic mathematics to analyze data obtained by impedance spectroscopy and/or coulomb counting techniques. Fuzzy logic provides a powerful means of modeling complex, non-linear systems without the need for explicit mathematical models. New detailed impedance date has been obtained on the discharge performance of primary lithium/sulfur dioxide cells. Earlier data, obtained by Rutgers co-workers on nickel/metal hydride and other systems, have been reviewed and re-interpreted using fuzzy logic methodology. Devices are being developed for several systems, which will predict the SOC and SOH of batteries without the need to know their previous discharge and/or cycling history.     

On-line monitoring of lead–acid batteries by galvanostatic non-destructive technique

Measurements of charge-acceptance, internal resistance, voltage and self-discharge of a battery reflect its state-of-health (SOH). The galvanostatic non-destructive technique (GNDT) can be used to monitor the SOH of a battery by analyzing its impedance parameters, namely ohmic resistance, charge-transfer resistance and interfacial capacitance. In this technique, the battery is discharged galvanostatically at a substantially low-rate over a short duration, wherein the state-of-charge (SOC) of the battery is not affected. It has been possible to obtain charge-transfer resistance and double-layer capacitance values for both positive and negative plates of a commercial grade 6-V/4-Ah valve-regulated lead–acid battery during its dynamic discharge. The resistive components of the battery are found to be minimum at state-of-charge values between 0.2 and 0.9. The study shows that the optimum performance of the VRLA battery can be achieved at SOC values between 0.2 and 0.9. The ohmic resistance of the battery displays a linear variation with logarithmic values of its SOC. The technique provides an attractive tool for on-line monitoring of lead–acid batteries.     

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.     

Non-intrusive measurement of the state-of-charge of lead-acid batteries using wire-wound coils

Non-intrusive monitoring of the state-of-charge (SOC) of lead-acid batteries by the use of wire wound coils is described. Coils were attached to the outside case of the battery, adjacent to the negative end plate, and excited using ac current of frequency in the range of 1–40 kHz. The change in inductance of the coils was monitored during battery cycling, as the metallic content of the electrode changed. Following correction for temperature changes, the technique is capable of estimating the SOC of a battery to an accuracy of ±10%. The inductance profiles that were obtained changed shape as the batteries aged and also with the exciting frequency. The origins of these changes are discussed.     

Least‐squares based coulomb counting method and its application for state‐of‐charge (SOC) estimation in electric vehicles

Coulomb counting method is a convenient and straightforward approach for estimating the state‐of‐charge (SOC) of lithium‐ion batteries. Without interrupting the power supply, the remaining capacities of them in an electric vehicle (EV) can be calculated by integrating the current leaving and entering the batteries. The main drawbacks of this method are the cumulative errors and the time‐varying coulombic efficiency, which always lead to inaccurate estimations. To deal with this problem, a least‐squares based coulomb counting method is proposed. With the proposed method, the coulombic losses can be compensated by charging/discharging coulombic efficiency η and the measurement drift can be amended with a morbid efficiency matrix. The experimental results demonstrated that the proposed method is effective and convenient. Copyright © 2016 John Wiley & Sons, Ltd.     

An enhanced temperature-dependent model and state-of-charge estimation for a Li-Ion battery using extended Kalman filter

Development of high-fidelity mathematical models and state-of-charge (SOC) estimation of Li-ion battery becomes a significant challenge when the temperature effects are considered. In this paper, we propose an enhanced temperature-dependent equivalent circuit model for a Li-ion battery and applied it for battery parameters estimation and model validation, as well as SOC estimation. First, the new battery model is elaborated, including a newly integrated resistance-capacitor structure, a static hysteresis voltage and a temperature compensation voltage term. The forgetting factor least square approach is utilized to realize the parameter identification. Next, the proposed battery model is employed to estimate battery SOC by incorporating the extended Kalman filter algorithm. Finally, simulation results are provided to demonstrate the superior performance of the proposed battery model in comparison with the common first-order Thevenin temperature model. Compared with Thevenin model, the maximal values of relative reconstruction error and root mean squared error with the proposed battery model are decreased by about 33.3% and 50.0%, respectively, for the battery terminal output voltage, 50.0% and 53.0%, respectively, for the SOC estimation, under three different test profiles.     

Control Algorithm of Fuel Cell and Batteries for Distributed Generation System

This paper intends to propose a novel control algorithm for utilizing a polymer electrolyte membrane fuel cell (PEMFC) as a main power source and batteries as a complementary source, for hybrid power sources for distributed generation system, particularly for future electric vehicle applications. The control, which takes into account the slow dynamics of a fuel cell (FC) in order to avoid fuel (hydrogen and air) starvation problems, is obviously simpler than state machines used for hybrid source control. The control strategy lies in using an FC for supplying energy to battery and load at the dc bus. The structure is an FC current, battery current, and battery state-of-charge (SOC) cascade control. To validate the proposed principle, a hardware system is realized by analogical circuits for the FC current loop and numerical calculation (dSPACE) for the battery current and SOC loops. Experimental results with small-scale devices (a 500 W PEM FC and 33 Ah, 48 V lead-acid battery bank) illustrate the excellent control scheme during motor drive cycles.     

VRLA automotive batteries for stop&go and dual battery systems

The electrical power requirements for vehicles are continuing to increase and evolve. A substantial amount of effort has been directed towards the development of 36/42 V systems as a route to higher power with reduced current levels but high implementation costs have resulted in the introduction of these systems becoming deferred. In the interim, however, alternator power outputs at 14 V are being increased substantially and at the same time the requirements for batteries are becoming more intensive. In particular, stop&go systems and wire-based vehicle systems are resulting in new demands. For stop&go, the engine is stopped each time the vehicle comes to rest and is restarted when the accelerator is pressed again. This results in an onerous duty cycle with many shallow discharge cycles. Flooded lead–acid batteries cannot meet this duty cycle and valve-regulated lead-acid (VRLA) batteries are needed to meet the demands that are applied. For wire-based systems, such as brake-by-wire or steer-by-wire, electrical power has become more critical and although the alternator and battery provide double redundancy, triple redundancy with a small reserve battery is specified. In this case, a small VRLA battery can be used and is optimised for standby service rather than for repeated discharges. The background to these applications is considered and test results under simulated operating conditions are discussed. Good performance can be obtained in batteries adapted for both applications. Battery management is also critical for both applications: in stop&go service, the state-of-charge (SOC) and state-of-health (SOH) need to be monitored to ensure that the vehicle can be restarted; for reserve or back-up batteries, the SOC and SOH are monitored to verify that the battery is always capable of carrying out the duty cycle if required. Practical methods of battery condition monitoring will be described.     

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。     

An adaptive Kalman filtering based State of Charge combined estimator for electric vehicle battery pack

Ah counting is not a satisfactory method for the estimation of the State of Charge (SOC) of a battery, as the initial SOC and coulombic efficiency are difficult to measure. To address this issue, a new SOC estimation method, denoted as “AEKFAh”, is proposed. This method uses the adaptive Kalman filtering method which can avoid filtering divergence resulting from uncertainty to correct for the initial value used in the Ah counting method. A Ni/MH battery test procedure, consisting of 8.08 continuous Federal Urban Driving Schedule (FUDS) cycles, is carried out to verify the method. The SOC estimation error is 2.4% when compared with the real SOC obtained from a discharge test. This compares favorably with an estimation error of 11.4% when using Ah counting.     

混合动力汽车磷酸铁锂动力电池建模与SOC计算CSCD

为了准确和方便地研究混合动力汽车中的磷酸铁锂动力电池的性能,基于Thevenin电池模型,考虑了温度对模型的影响,通过库仑计数法估算电池荷电状态（SOC）。针对该电池,通过HPPC试验识别电池模型参数,在Matlab/Simulink中建立物理仿真模型进行仿真计算。研究表明：所使用的Thevenin电池模型精度高,对比模拟和实测端电压结果,两者变化趋势基本相同,端电压平均误差为3.6 V,最大误差为12.6 V,占电池额定电压0.79%,能真实的模拟电池充放电特性;结合库仑计数法计算电池SOC,能有效控制SOC的估算值在高精度范围内。模拟SOC和实测SOC结果进行对比表明,SOC精度保持在3%以内。     

Discrimination of Li-ion batteries based on Hamming network using discharging-charging voltage pattern recognition for improved state-of-charge estimation

Differences in electrochemical characteristics among Li-ion batteries and factors such as temperature and ageing result in erroneous state-of-charge (SoC) estimation when using the existing extended Kalman filter (EKF) algorithm. This study presents an application of the Hamming neural network to the identification of suitable battery model parameters for improved SoC estimation. The discharging-charging voltage (DCV) patterns of ten fresh Li-ion batteries are measured, together with the battery parameters, as representative patterns. Through statistical analysis, the Hamming network is applied for identification of the representative DCV pattern that matches most closely of the pattern of the arbitrary battery to be measured. Model parameters of the representative battery are then applied to estimate the SoC of the arbitrary battery using the EKF. This avoids the need for repeated parameter measurement. Using model parameters selected by the proposed method, all SoC estimates (off-line and on-line) based on the EKF are within ±5% of the values estimated by ampere-hour counting.     

Battery open-circuit voltage estimation by a method of statistical analysis

The basic task of a battery management system (BMS) is the optimal utilization of the stored energy and minimization of degradation effects. It is critical for a BMS that the state-of-charge (SoC) be accurately determined. Open-circuit voltage (OCV) is directly related to the state-of-charge of the battery, accurate estimation of the OCV leads to an accurate estimate of the SoC. In this paper we describe a statistical method to predict the open-circuit voltage on the basis of voltage curves obtained by charging batteries with different currents. We employ a dimension reduction method (Karhunen–Loeve expansion) and linear regression. Results of our modelling approach are independently validated in a specially designed experiment.     

Multi-parameter battery state estimator based on the adaptive and direct solution of the governing differential equations

We report the development of an adaptive, multi-parameter battery state estimator based on the direct solution of the differential equations that govern an equivalent circuit representation of the battery. The core of the estimator includes two sets of inter-related equations corresponding to discharge and charge events respectively. Simulation results indicate that the estimator gives accurate prediction and numerically stable performance in the regression of model parameters. The estimator is implemented in a vehicle-simulated environment to predict the state of charge (SOC) and the charge and discharge power capabilities (state of power, SOP) of a lithium ion battery. Predictions for the SOC and SOP agree well with experimental measurements, demonstrating the estimator's application in battery management systems. In particular, this new approach appears to be very stable for high-frequency data streams.     

Modeling of kinetic behavior of the lead dioxide electrode in a lead–acid battery by means of electrochemical impedance spectroscopy

In this paper a mathematical model for the electrochemical impedance of a positive electrode in the lead–acid battery was developed. The mechanisms of the electrode processes involved in the batteries were obtained from the literature. Having selected the rate-controlling step as well as the fast reversible (equilibrated) and irreversible steps, we embarked on the formulation of I–V behavior using Butler–Volmer and Fick's law for charge and mass transfer process, respectively. The equivalent circuit consists of resistors, capacitors and mass transfer elements. Each electrical element expresses physico-chemical parameters such as diffusion coefficient, concentration, rate constant, etc. So, impedance plots can be used to determine the effects of each parameter on the performance of the cells. The simulation model also shows that the state-of-charge (SOC) plays an important role in the Nyquist plot of the positive electrode. Model results are compared with experimental results for cell potential and different SOC.     

State of charge estimation method based on the extended Kalman filter algorithm with consideration of time-varying battery parameters

In developing battery management systems, estimating state-of-charge (SOC) is important yet challenging. Compared with traditional SOC estimation methods (eg, the ampere-hour integration method), extended Kalman filter (EKF) algorithm does not depend on the initial value of SOC and has no accumulated error, which is suitable for the actual working condition of electric vehicles. EKF is a model-based algorithm; the accuracy of SOC estimated by this algorithm was greatly influenced by the accuracy of battery model and model parameters. The parameters of battery change with many factors and exhibit strong nonlinearity and time variance. Typical EKF algorithm approximates battery as a linear, time-invariant system; however, this approach introduces estimation errors. To minimize such errors, previous studies have focused on improving the accuracy of identifying battery parameters. Although studies on battery model with time-varying parameters have been carried out, few have studied the combination of time-varying battery parameters and EKF algorithm. A SOC estimation method that combines time-varying battery parameters with EKF algorithm is proposed to improve the accuracy of SOC estimation. Battery parameter data were obtained experimentally under different temperatures, SOC levels, and discharge rates. The results of parameter identification are made into a data table, and the battery parameters in the EKF system matrix are updated by looking up the data in the table. Simulation and experimental results shown that, average error of SOC estimated by the proposed algorithm is 2.39% under 0.9 C constant current discharge and 2.4% under 1.3 C, which is 1.91% and 2.35% lower than that of EKF algorithm with fixed battery parameters. Under intermittent discharge with constant current (1.1 C) and capacity (10%), the average error of SOC estimated by the proposed algorithm is 1.4%, which is 0.3% lower than that of EKF algorithm with fixed battery parameters. The average error of SOC estimated by the proposed algorithm under the New European Driving Cycle (NEDC) is 1.6%, which is 0.2% lower than that of EKF algorithm with fixed battery parameters. Relative to the EKF algorithm with fixed battery parameters, the proposed EFK algorithm with time-varying battery parameters yields higher accuracy.     

Storage of a lithium-ion secondary battery under micro-gravity conditions

‘HAYABUSA’ is a Japanese inter-planetary spacecraft built for the exploration of an asteroid named ‘ITOKAWA.’ The spacecraft is powered by a 13.2 Ah lithium-ion secondary battery. To realize maximum performance of the battery for long flight operation, the state-of-charge (SOC) of the battery was maintained at ca. 65% during storage, in case it is required for a loss of attitude control. The capacity of the battery was measured during flight operations. Along with the operation in orbit, a ground-test battery was discharged, and both results showed a good agreement. This result confirmed that the performance of the lithium-ion secondary battery stored under micro-gravity conditions is predictable using a ground-test battery.