Measurement Of State Of Charge Research Articles

A Novel Redacted Extended Kalman Filter and Fuzzy Logic-Based Technique for Measurement of State-of-Charge of Lithium-Ion Battery

Abstract This paper presents a novel technique based on an adaptive approach of redacted extended Kalman filter (REKF) assimilating fuzzy logic features for measuring the state-of-charge (SoC) of lithium-ion batteries. Accurately determining SoC is crucial for maximizing battery capacity and performance. However, existing extended Kalman filtering algorithms suffer from issues such as inadequate noise resistance and noise sensitivity, as well as difficulties in selecting the forgetting factor. The aforementioned REKF technique addresses these challenges adequately for accurate measurement of SoC. The proposed method involves establishing a Thevenin equivalent circuit model and using the recursive least squares with forgetting factor (RLSFF) to identify model parameters. Furthermore, an evaluation factor is established, and to adaptively adjust the value of the forgetting factor, fuzzy control is utilized, which enhances the extended Kalman filtering algorithm with noise adaptive algorithm features to estimate the SoC accurately. This modified algorithm considers the identification results from the parameter estimation step and executes them circularly to achieve precise SoC estimation. Results demonstrate that the proposed method has excellent robustness and estimation accuracy compared to other filtering algorithms, even under variable working conditions, including a wide range of state-of-health (SOH) and temperature. The proposed method is expected to enhance the performance of battery management systems for various applications.

Hybrid SOC and SOH estimation method with improved noise immunity and computational efficiency in hybrid railroad propulsion system

The fuel-cell and lithium-ion battery (LIB) hybrid railroad propulsion system (HRPS) technologies are actively being researched to reduce greenhouse gas (GHG) emissions. It is becoming increasingly important to accurately estimate the state-of-charge (SOC) and state-of-health (SOH) to enhance the safety and efficiency of the system. For a real-time embedded system, computational efficiency and the effect of noise are crucial challenges for SOC and SOH estimation. To overcome these problems, a method is required that improves computational efficiency and minimizes the effect of noise.To reduce computational cost and the effect of noise, the adaptive extended Kalman filter (AEKF) and specific partial capacity with single forgetting factor recursive least square (SPCSFFRLS) methods were combined to estimate SOC and SOH. In AEKF-based SOC estimation, the state and measurement noise covariance matrices were corrected by the mean square error (MSE) of the difference between measured voltage and modeled voltage. Thus, AEKF is effective in conquering measurement noise. The battery capacity was obtained by the SPCSFFRLS using station-to-station operation data. To calculate true specific partial capacity (SPC) without noise, the partial capacity method and SFFRLS were used to update the battery capacity. However, as the current accumulation period becomes longer, an accumulation error due to measurement noise may occur. Therefore, a specific period was analyzed to reduce accumulation error caused by measurement noise in the HRPS load profile. Finally, SOC and SOH co-estimation was demonstrated for a single cell and a parallel-connected battery pack (1S18P). As a result, it was found that the proposed method can reduce the computational time by about 33.0 % average while keeping the SOC and SOH estimation accuracy compared to DEKF. Thus, the proposed method is suitable for real-time embedded system such as HRPS to estimate SOC and SOH.

Battery SOC estimation from EIS data based on machine learning and equivalent circuit model

Estimating the state of charge (SOC) of batteries is fundamental for the proper management and safe operation of numerous systems, including electric vehicles, smart energy grids, and portable electronics. While there is no practical method for direct measurement of SOC, several estimation approaches have been developed, including a growing number of machine-learning-based techniques. Machine learning methods are intrinsically data-driven but can also benefit from a-priori knowledge embedded in a model. In this work, we first demonstrate, through exploratory data analysis, that it is possible to discriminate between different SOC from electrochemical impedance spectroscopy (EIS) measurements. Then we propose a SOC estimation approach based on EIS and an equivalent circuit model to provide a compact way to describe the frequency domain and time-domain behavior of the impedance of a battery. We experimentally validated this approach by applying it to a dataset consisting of EIS measurements performed on four lithium-ion cylindrical cells at different SOC values. The proposed approach allows for very efficient model training and produces a low-dimensional SOC classification model that achieves above 93% accuracy. The resulting low-dimensional classification model is suitable for embedding into battery-powered systems and for online SOC estimation.

Uniqueness of multilayer perceptron-based capacity prediction for contributing state-of-charge estimation in a lithium primary battery

Lithium primary batteries having high power and energy densities have irreversible energy characteristics, unlike lithium secondary batteries. Therefore, their application in experiments is limited, and capacity information is difficult to derive. The capacity information serves as the initial value in the ampere-hour counting method to estimate the battery state-of-charge (SOC). Therefore, in order to estimate the SOC of a lithium primary battery, it is necessary to predict the capacity information. In this paper, we propose a method to predict the capacity of lithium primary batteries using machine learning techniques. To predict capacity, pre-measurement factors (PMFs) for lithium primary batteries are selected and correlation with capacity is analyzed. PMFs that can be measured before the lithium primary battery operates include AC impedance and open-circuit voltage, weight, and resistance, Resistances were extracted using the closed-circuit voltage experiment. These PMFs derive a correlation coefficient through correlation analysis of the capacity of lithium primary battery. However, the correlation coefficient between individual PMFs and the capacity of lithium primary battery is lower than 0.5. That is, the relationship between PMFs and capacity is not linear. Therefore, this study implements a multilayer perceptron (MLP) model that uses multiple nonlinear parameters. A total of 300 lithium primary battery data is used for model design, and the model is verified by classifying the data into training data and test data. The capacity information predicted through the MLP model was used for the SOC estimation, and the SOC estimation accuracy was analyzed by comparing it with the SOC measurement value.

Online MEMS-Based Specific Gravity Measurement for Lead-Acid Batteries

Traditional methods for measuring the specific gravity (SG) of lead-acid batteries are offline, time-consuming, unsafe, and complicated. This study proposes an online method for the SG measurement to estimate the state-of-charge (SoC) of lead-acid batteries. This proposed method is based on an air purge system integrating with a micro electro mechanical system sensor. Through the proposed strategy, the SoC measurement achieves up to ±1% accuracy. The technique has an SG accuracy of ±0.002% which is better than the glass hydrometer accuracy of ±0.005% in the battery charge reading. The experimental results show that the high accuracy and precise measurements of SG and SoC can be conducted by using the proposed method.

An Invariant Method for Electric Vehicle Battery State-of-Charge Estimation Under Dynamic Drive Cycles

This paper proposes a novel invariant extended Kalman filter (IEKF), a modified version of the extended Kalman filter (EKF), for state-of-charge (SOC) estimation of lithium-ion (Li-ion) battery cells. Unlike conventional EKF methods where the correction term used to update the state is linearly proportional to the output error, this paper employs the IEKF where the correction term is independent of the output error, resulting in a significant reduction in the estimation error and improving the estimation accuracy. In contrast to classic method like the EKF and more contemporary ones like the square root variant of the Cubature Kalman Filter (SCKF), the IEKF can successfully mimic the nonlinear dynamics and mitigate measurement noise stochasticity. Moreover, even if the measurement model fails to fully capture the cell’s dynamics, the IEKF will still sustain a reasonable performance. Hence, IEKF outperforms the conventional EKF, and even the SCKF, which can diverge if a mismatch between the SOC measurement model and the true SOC measurement occurs. The derivation of the proposed method followed by experimental verification using commercial Li-ion battery cells are presented.

Inkjet-printed microband electrodes for a cost-efficient state-of-charge monitoring in redox flow batteries

A readily available prototyping process for the fabrication of low-cost microband electrodes is introduced, which are applicable for the accurate amperometric state-of-charge (SOC) measurement in redox flow battery (RFB) electrolytes. For this purpose, an inexpensive and straightforward inkjet printing of conductive platinum-based and insulating polymer-based inks was investigated. The overall inkjet-printed microband electrode performance in 0.4 M ferri-/ferrocyanide with 0.5 M KCl as supporting electrolyte were compared to sputter-coated microband electrodes and commercial disk microelectrodes using an SOC measurement method based on steady-state amperometry. Absolute root-mean-square deviations (RMSD) are below 1.04% SOC for the printed microband electrodes. The low material costs of about €2.52 (USD2.86) for the presented prototype and the simplicity of the fabrication process could facilitate a widespread application of the amperometric SOC measurement in RFBs.

Verification and analysis of a Battery Energy Storage System model

Energy Storage System modelling is the foundation for research into the deployment and optimization of energy storage in new and existing applications. The increasing penetration of renewable energy into electrical grids worldwide means energy storage is becoming a vital component in the modern electrical distribution system. In this paper, a model for a Battery Energy Storage System developed in MATLAB/Simulink is introduced and subsequently experimentally verified against an existing 2 MW installation operated by The University of Sheffield (Willenhall). The model presented in this paper achieved average Root Mean Square Error (RMSE) values of 1.29% and 1.05% when comparing the state of charge (SOC) of the model with two different methods of SOC measurement at the Willenhall installation. The methodology behind fine tuning the model to decrease the average RMSE is presented and discussed.

A Hybrid Method for Lithium-Ion Batteries State-of-Charge Estimation Based on Gated Recurrent Unit Neural Network and an Adaptive Unscented Kalman Filter

Abstract Among the battery state of charge (SOC) estimation methods, the Kalman-based filter algorithms are sensitive to the battery model while the neural network (NN)-based algorithms are decided by hyperparameters. In this paper, a hybrid approach composed of a gated recurrent unit (GRU) NN and an adaptive unscented Kalman filter (AUKF) method is proposed. A GRU NN is first used to acquire the nonlinear relationship between the battery SOC and battery measurement signals, and then an AUKF is utilized to filter out the output noise of the NN to further improve estimation accuracy. The hybrid method avoids the establishment of accurate battery models and the search for optimal hyperparameters. The data of dynamical street test and US06 test are used as training dataset and validation dataset, respectively, while the data collected from the tests under federal urban driving schedules and Beijing driving cycle conditions are taken as testing dataset. As compared with some hybrid methods proposed in other literature, the hybrid method has the best estimation accuracy and generalization for various driving cycles at different ambient temperatures. The root mean square error and the mean absolute error all are less than 1.5%, and the maximum absolute error is less than 2%. In addition, it also exhibits powerful robustness against the abnormal values of the battery signals and can converge to the true value in just 5 s.

SoC threshold optimization for battery storage in frequency regulation considering uncertainty of SoC measurement and automatic generation control fatigue loss of thermal power system

The min/max state of charge (SoC) thresholds of battery storage (BS), which challenge the economics of frequency regulation (FR), have a certain degree of uncertainty and need to be artificially re-calibrated after a period of operation time. To tackle this challenge, a SoC threshold optimization method is proposed for joint FR by thermal power (TP) and battery storage (BS) by comprehensively considering the automatic generation control (AGC) fatigue loss cost of TP and the aging degradation cost of BS. The proposed method consists of three parts: 1) the AGC fatigue loss cost of TP is computed by considering motion loss cost (e.g., steam turbine rotor, oil servo-motor, steam gate valve stem) and static loss cost (e.g., steam drum, cylinder); 2) the aging degradation cost of BS is formulated by considering also the uncertainty of SoC; 3) the min/max SoC operation thresholds of BS are optimized through the surrogate Lagrangian relaxation (SLR) method by comprehensive considering of the AGC fatigue loss and aging degradation cost of FR. Results of extensive simulation tests have clarified and verified the economic effectiveness of proposed min/max SoC threshold optimization method for the joint frequency regulation by thermal power and battery storage.

Estimation of State of Charge (SoC) Using Modified Coulomb Counting Method With Open Circuit Compensation For Battery Management System (BMS)

Abstract— Battery is one of the important components in the development of renewable energy technology. This paper presents a method for estimating the State of Charge (SoC) for a 4Ah Li-ion battery. State of Charge (SoC) is the status of the capacity in the battery in the form of a percentage which makes it easier to monitor the battery during use. Coulomb calculations are widely used, but this method still contains errors during integration. In this paper, SoC measurement using Open Circuit Voltage Compensation is used for the determination of the initial SoC, so that the initial SoC reading is more precise, because if the initial SoC reading only uses a voltage sensor, the initial SoC reading is less precise which affects the next n second SoC reading. In this paper, we present a battery management system design or commonly known as BMS (Battery Management System) which focuses on the monitoring function. BMS uses a voltage sensor in the form of a voltage divider circuit and an ACS 712 current sensor to send information about the battery condition to the microcontroller as the control center. Besides, BMS is equipped with a protection relay to protect the battery. The estimation results of the 12volt 4Ah Li-ion battery SoC with the actual reading show an error of less than 1%.Keywords—Battery Management System, Modified Coulomb Counting, State of Charge.

WITHDRAWN: Design, simulation analysis of universal battery management system for EV applications

Abstract Now a days, Electric Vehicles (EVs) are familiar with abundant types of battery chemistries with different specifications. Battery Management System (BMS) is an essential and important device of an EV for smooth operation and long life. In present scenario, a separate BMS has been used for the particular battery. So, there is a need of a common BMS for all type of EVs. This paper proposes a simulation design of common BMS for four different batteries namely Nickel-Metal-Hydride, Nickel-Cadmium, Lithium-Ion and Lead-Acid. A common simulation and mathematical modelling of aforementioned four types of battery is executed here. The proposed system is designed to manipulate and control the instantaneous values of the battery parameters like Battery voltage, Current, Power consumption and State of Charge (SoC). This proposed BMS continuously monitors the battery parameters with high accuracy, especially in SoC measurement. The working of BMS is controlled and monitored by using Extended Kalman Filter (EKF). The entire mathematical modelling and simulation work of BMS is analysed by using MATLAB/Simulink.

A Study on State of Charge and State of Health Estimation in Consideration of Lithium-Ion Battery Aging

Due to rapid development of industries around the world, more and more consumption of fossil fuels was unavoidable, resulting in serious environmental problems. The many pollutant emissions—a major contributor to global warming and weather pattern change—have been at the center of concern. In order to solve this issue, research and development of electric vehicles and energy storage systems made great progress and successfully introduced products in the market. Nevertheless, accurate measurement of the state of charge (SOC) and state of health (SOH) of the Li-ion battery, the most popular electric energy storage device, has not yet been fully understood due to the nature of battery aging. In this study, ideas to estimate the capacity and ultimately SOC and SOH of Li-ion batteries are discussed. With these ideas, we expect not only to accommodate the issues with battery aging but also to implement an algorithm for an on-board battery management system. The key idea is to chase and monitor internal resistance continuously in a fast and reliable manner in real time. With further investigation of the key idea, we also fully expect to come up with a reliable SOC and SOH measurement scheme in the near future.

Research on a Soft Measurement Method for SOC of Electric Vehicle Battery

Aiming at the problem that the State of charge (SOC) can not be directly measured, the SOC soft sensing method based on subtractive clustering and adaptive fuzzy neural network is introduced. Firstly, the structure of AFNN was decided by SC; Secondly, by adopting the back-propagation algorithm and least square method respectively, the front and back parameters of AFNN were optimized, and the study efficiency of the parameters was raised. Finally, the fuzzy membership functions and rules which generated automatically by AFNN are applied to the soft measurement of battery SOC for HEV. Under the CYC-HWFET working conditions, the working voltages, currents and surface temperature of battery are used to predict the value of SOC, and the results indicate that the prediction model possesses higher predicted accuracy, and the errors between the real value and the soft measurement value are small.

Effect of Sensors Sensitivity on Lithium-Ion Battery Modeled Parameters and State of Charge: A Comparative Study

The accurate estimation of the state of charge (SOC) is usually acknowledged as one of the essential features in designing of battery management system (BMS) for the lithium-ion batteries (LIBs) in electric vehicles (EVs). A suitable battery model is a prerequisite for correct SOC measurement. In this work, the first and second order RC autoregressive exogenous (ARX) battery models are adopted to check the influence of voltage and current transducer measurement uncertainty. The Lagrange multiplier method is used to estimate the battery parameters. The sensitivity analysis is performed under the following conditions: Current sensor precision of ±5 mA, ±50 mA, ±100 mA, and ±500 mA and voltage sensor precision of ±1 mV, ±2.5 mV, ±5 mV, and ±10mV. The comparative analysis of both models under the perturbed environment has been carried out. The effects of the sensor’s sensitivity on the different battery structures and complexity are also analyzed. Results shows that the voltage and current sensor sensitivity has a significant influence on SOC estimation. This research outcome assists the researcher in selecting the optimal value of sensor accuracy to accurately estimate the SOC of the LIB.

Correlation between Battery Voltage under Loaded Condition and Estimated State of Charge at Valve-Regulated Lead Acid Battery on Discharge Condition using Open Circuit Voltage Method

One of the common methods that developed to predict state of charge is open circuit voltage (OCV) method. The problem which commonly occurs is to find the correction parameter between open circuit voltage and loaded voltage of the battery. In this research, correlation between state of charge measurement at loaded condition of a Panasonic LC-VA1212NA1, which is a valve-regulated lead acid (VRLA) battery, and open circuit voltage had been analyzed. Based on the results of research, correlation between battery’s measured voltage under loaded condition and open circuit voltage could be approached by two linearization area. It caused by K v ’s values tend to increase when measured voltage under loaded condition V M < 11.64 volt. However, K v values would be relatively stable for every V M ≥ 11.64 volts. Therefore, estimated state of charge value, in respect to loaded battery voltage, would increase slower on V M < 11.64 volts and faster on other range.

In-Situ Optical Monitoring of Vanadium Redox Flow Battery State-of-Charge

Vanadium redox flow batteries (VRFBs) are an attractive technology for a variety of energy storage applications.1,2 Monitoring of the state of charge (SoC) is important for any battery system. Additionally, in VRFBs, transfer of vanadium ions across the membrane 3 and side reactions, such as hydrogen formation 4 at the negative electrode, can result in the SoC becoming unbalanced (e.g. more VV on the positive side than VIIon the negative). Therefore independent monitoring of the SoC of both electrolytes is important for effective operation of VRFB technology. The individual SoCs of the positive and negative sides of a VRFB may be monitored in a number of ways. However, many of these methods have significant limitations; in particular they do not provide readings that are truly independent of the cell output. Spectroscopic monitoring of SoC is independent of electrochemistry and offers the possibility of performing in-situanalysis. The negative electrolyte in a VRFB usually consists of VII and VIII in H2SO4. The mole percentage of VII represents the SoC of the electrolyte. Since absorbance varies linearly with mole percentage of VII it is easy to construct a calibration curve for the negative electrolyte that relates absorbance to SoC. The absorbance of a given negative electrolyte will, of course, depend on the actual composition of the electrolyte. In practice a suitable calibration procedure is needed and estimates need to be made of the precision of measurement of SoC. In principle, however, the determination of SoC from absorbance measurements in a VII-VIIIelectrolyte is straightforward. The positive electrolyte in a VRFB usually consists of VIV and VV in H2SO4. The mole percentage of VV represents the SoC of the electrolyte. Since absorbance varies with percentage of VV in a very non-linear manner 5 SoC is not uniquely determined by an absorbance measurement at a single wavelength, as it is for the negative electrolyte. Empirical methods 6 that use look-up spectra have been proposed. However, we have shown 7-11that these elaborate methods are unnecessary since the spectroscopic behavior can be analyzed with considerable precision once the underlying solution chemistry is understood. The procedures 7for determining SoC of positive electrolytes are not as straightforward as they are for negative electrolytes and involve measurements (and corresponding calibration curves) at two different wavelengths. In practice, both calibration curves will vary with electrolyte composition not only in the values of absorbance but also in the shape of the curves. Absorbance varies strongly both with total vanadium concentration and with sulfate concentration. Thus, for the positive electrolyte, a good quantitative understanding of the behavior of absorbance with electrolyte composition needs to be obtained before a suitable procedure for SoC measurement can be implemented. Based on our earlier ex-situ results,7-11 we have extended our experiments to include in-situmonitoring of SoC by UV-visible spectroscopy as illustrated schematically in Fig. 1. We will discuss our results with particular emphasis on the behavior of the positive electrolyte. Fig. 1 Schematic of VRFB connected to an optical SoC monitor. The negative and positive SoC can be determined using the absorbance at one and two wavelengths, respectively. Acknowledgements The authors would like to acknowledge funding from Enterprise Ireland through Commercialisation Fund CF/2013/3303. R.P. Lynch would like to acknowledge the receipt of an IRCSET-Marie Curie International Mobility Fellowship in Science, Engineering and Technology (grant no. INSPIRE PCOFUND-GA-2008-229520). References H. Bindner, C. Ekman, O. Gehrke and F. Isleifsson, in Characterization of Vanadium Flow Battery, Risø Report (2011)M.J. Watt-Smith, P. Ridley, R.G.A. Wills, A.A. Shah and F.C. Walsh, J. Chem. Technol. Biotechnol. 88, 126 (2013)X. Teng, Y. Zhao, J. Xi, Z. Wu, X. Qiu and L. Chen, J. Power Sources 189, 1240 (2009)A.H. Whiteheadand M. Harrer, J. Power Sources 230, 271 (2013)P. Blanc, C. Madic and J.P. Launay, Inorg. Chem. 21, 2923 (1982)Z. Tang, D.S. Aaron, A.B. Papandrew and T.A. Zawodzinski, Jr., ECS Trans. 41 (23), 1 (2012)D.N. Buckley, X. Gao, R.P. Lynch, M.J. Leahy, A. Bourkeand G. Flynn, European Patent EP 13195315 (Application Date: December, 2013)X. Gao, M. Leahy and D.N. Buckley, 221st Meeting of the Electrochemical Society (ECS), Seattle, USA, 8 May, MA2012-01 388 (2012)X. Gao, R.P. Lynch, M. Leahy and D. N. Buckley, ECS Transactions 45 (26), 25 (2013)D.N. Buckley, X. Gao, R.P. Lynch, N. Quill, and M.J. Leahy, J. Electrochem. Soc. 161 (4), A524 (2014)N. Quill, C. Petchsingh, R. P. Lynch, X. Gao, D. Oboroceanu, D. Ní Eidhin, M. O'Mahony, C. Lenihan, and D. N. Buckley, ECS Transactions in press (2015) Figure 1

Factors Affecting Spectroscopic State-of-Charge Measurement

Vanadium redox flow batteries (VRFBs) are an attractive technology for a variety of energy storage applications.1,2 The catholyte and the anolyte in these batteries are circulated through the electrodes from reservoirs. The active species in the catholyte are VO2+ and VO2 + (i.e. VIV and VV) while the active species in the anolyte are V3+ and V2+. VRFBs have a major advantage over other flow batteries in that cross-contamination due to transport through the separating membrane is effectively eliminated.3 Monitoring of the state of charge (SoC) is important for any battery system. Additionally, in VRFBs, transfer of vanadium ions across the membrane4 and side reactions such as hydrogen formation5 at the negative electrode can result in SoC becoming unbalanced (e.g. more VV on the positive side than VII on the negative). Therefore independent monitoring of SoC of both electrolytes is important for effective operation of VRFB technology.Both overall SoC of a VRFB and individual SoCs of the positive and negative sides (determined by the VIII/VII and VIV/VV ratios in the respective electrolytes) may be monitored in a number of ways using electrodes. However, these methods have drawbacks.6 Spectroscopic monitoring of SoC is independent of electrochemistry and offers the possibility of performing in-situ analysis. VII, VIII, VIV, and VV aqueous species have strong absorbance spectra in the visible region.6- 12 If the absorbance is a linear combination of that of the constituents UV-visible spectroscopy is a straightforward method of measuring the concentration and ratio of mixtures: e.g. for VIII-VII mixtures or very dilute VIV-VV mixtures.6-12 At higher concentrations, the absorbance of VIV-VV mixtures is a highly non-linear function of the mole fraction of VIV. For VIV-VV mixtures, this non-linearity has been shown to be due to the formation of a complex between the VIV and VV species. 6-8 Tang et al. 10 and Liu et al. 11 addressed the problem of non-linearity by developing an empirical method of estimating SoC. However, the non-linearity can be quantitatively explained6-8 allowing precise methods12 of optical monitoring of SoC in VRFBs. Factors, such as the concentration of sulphate and vanadium, can significantly affect this non-linearity.6,8 In this presentation, we present a detailed study of these factors in relation to spectroscopic SoC measurement. Acknowledgements The authors acknowledge funding from Enterprise Ireland through Commercialisation Fund CF/2013/3303. The material in this research is partly based upon works supported by Science Foundation Ireland through the Charles Parson Initiative (CPI). References H. Bindner, C. Ekman, O. Gehrke and F. Isleifsson, in Characterization of Vanadium Flow Battery, Risø Report (2011)M.J. Watt-Smith, P. Ridley, R.G.A. Wills, A.A. Shah and F.C. Walsh, J. Chem. Technol. Biotechnol. 88, 126 (2013)M. Rychcik and M. Skyllas-Kazacos, J. Power Sources 22, 59 (1988)J. Xi, Z. Wu, X. Qiu and L. Chen, J. Power Sources 166, 531 (2007)A.H. Whiteheadand M. Harrer, J. Power Sources 230, 271 (2013)D.N. Buckley, X. Gao, R.P. Lynch, N. Quill, and M.J. Leahy, J. Electrochem. Soc. 161 (4), A524 (2014)X. Gao, R.P. Lynch, M. Leahy and D. N. Buckley, ECS Transactions 45 (26), 25 (2013)P. Blanc, C. Madic and J.P. Launay, Inorg. Chem. 21, 2923 (1982)M. Skyllas-Kazacos and M. Kazacos, J. Power Sources 196, 8822 (2011)Z. Tang, D.S. Aaron, A.B. Papandrew and T.A. Zawodzinski, Jr., ECS Trans. 41 (23), 1 (2012)L. Liu, J. Xi, Z. Wu, W. Zhang, H. Zhou, W. Li and X. Qiu, J. Appl. Electrochem. 42(12), 1025 (2012)D.N. Buckley, X. Gao, R.P. Lynch, M.J. Leahy, A. Bourkeand G. Flynn, European Patent EP 13195315 (Application Date: 2 December 2013)

Support Vector Machines Used to Estimate the Battery State of Charge

The aim of this study is to estimate the state of charge (SOC) of a high-capacity lithium iron manganese phosphate (LiFeMnPO4) battery cell from an experimental dataset using a support vector machine (SVM) approach. SVM is a type of learning machine based on statistical learning theory. Many applications require accurate measurement of battery SOC in order to give users an indication of available runtime. It is particularly important for electric vehicles or portable devices. In this paper, the proposed SOC estimator extracts model parameters from battery charging/discharging testing cycles, using cell current, cell voltage, and cell temperature as independent variables. Tests are carried out on a 60 Ah lithium-ion cell with the dynamic stress test cycle to set up the SVM model. The SVM SOC estimator maintains a high level of accuracy, better than 6% over all ranges of operation, whether the battery is charged/discharged at constant current or it is operating in a variable current profile.

High resolution state of charge monitoring of vanadium electrolytes with IR optical sensor

This article presents experimental studies of state of charge (SOC) monitoring of vanadium species in sulfuric acid. An IR sensor for run-time measurement of SOC of electrolytes of vanadium RedOx flow battery was constructed. The presented method allows the independent measurement of the SOC of catholyte as well as anolyte using identical sensors with an accuracy better than ±1%.