OCV-SOC Curve Research Articles

Open circuit voltage - state of charge curve calibration by redefining max–min bounds for lithium-ion batteries

The relationship between open circuit voltage (OCV) and state of charge (SoC) is essential for SoC estimation of lithium-ion batteries, which can be secured by either low-current OCV test or incremental OCV test, with incremental OCV test demonstrating better results. Nevertheless, low current always leads to a discharge capacity that is inconsistent with maximum available capacity, which is a key parameter in SoC estimation. Therefore, the resultant OCV-SoC curve fails to provide a consistent SoC range and thus affects SoC estimation. In this paper, OCV-SoC curves obtained from low-current OCV tests are calibrated by redefining max–min bounds to improve SoC estimation accuracy. Max–min bounds of SoC are redefined by measuring, calculating and resetting upper and lower cut-off voltages of the OCV-SoC curve. Based on second-order RC model, model parameters are identified online and adaptive square-root unscented Kalman filter (ASRUKF) algorithm is introduced to perform SoC estimation, which is proved to be precise and adaptive. The calibrated OCV-SoC curve achieves an overall better SoC estimation performance than that obtained from the low-current OCV test and incremental OCV test, which is validated by experiments of LiNiMnCoO2 (NMC) batteries at 0°C and 25°C.

SOC estimation of lithium-ion batteries based on the improved Kalman filter

Nowadays, the environmental crisis and energy crisis have been known by people around the world as issues that have to be taken seriously. It is recognized that electric vehicles are more environmentally friendly than traditional fuel cars, but there are still many immature aspects of electric vehicle technology. Battery technology is one of them, and lithium battery is a common power storage tool for electric vehicles. Therefore, it is very important to monitor the status of the battery pack in real-time. Improving the practical capacity, safety, and service life of lithium batteries all have an important impact on the development of electric vehicles. In battery management systems (BMS), the accurate prediction of the battery state of charge (SOC) is particularly important. In this paper, the 18650 lithium battery was selected for the research experiment, and the circuit model of second-order RC was selected. Modeling by MATLAB, the SOC-OCV curve of the battery was determined through the data obtained from the charging and discharging experiment (HPPC), the original least squares method was improved, the forgetting factor was introduced, and the model was identified by the parameters based on the experimental data. The traditional SOC prediction method added noise adaptive rules and iterative theory in the extended Kalman filter(EKF), formed the adaptive Kalman filter (AIEKF) to estimate the SOC of lithium battery, and estimated the SOC using the American urban road cycle (UDDS) data. The results show that the AIEKF algorithm has fewer errors and advantages than the traditional EKF algorithm.

Enhanced state of charge estimation for Li-ion batteries through adaptive maximum correntropy Kalman filter with open circuit voltage correction

Due to the possible interference of non-Gaussian noise in Li-ion battery management systems, there is no guarantee of reliable accuracy when using the extended Kalman filter (EKF) algorithm for battery state of charge (SOC) estimation. A novel EKF algorithm based on the adaptive maximum correntropy criterion (AMCCEKF) is proposed to enhance the robustness of SOC estimation in the paper. The Gaussian kernel function is chosen as the cost function to reconstruct the state error variance and the measurement noise variance. And a kernel width adaptive update strategy is designed to address the constraints of fixed kernel width on SOC estimation performance. In addition, an open circuit voltage (OCV) correction strategy based on terminal voltage innovation and OCV-SOC curve gradient is designed to reduce the impact of OCV error caused by non-Gaussian noise on SOC estimation. Incorporating the AMCCEKF method and the OCV correction strategy, a novel estimation method based on the dOCV-AMCCEKF is offered to perform the SOC estimation. Simulation results under different operating conditions and temperatures show that the maximum absolute error of the dOCV-AMCCEKF method is close to 0.5%, which verifies it can reduce the SOC estimation error in a non-Gaussian noisy interference environment compared with the EKF based method.

Stability Analysis of EKF-Based SOC Observer for Lithium-Ion Battery

The state of charge (SOC) plays a critical role in battery management systems. This paper discusses the stability of the nonlinear SOC observer based on the extended Kalman filter. The model characterizing the lithium-ion battery nonlinearity is the basis of the stability analysis. After balancing the accuracy and the complexity of the models, the Thevenin battery model and the logarithmic fitting OCV (open circuit voltage) model are employed. The stability of the SOC observer is theoretically analyzed from two aspects: model parameters and system nonlinearity. Furthermore, the impact of system noises and nonlinear characteristics on the estimation is explored in a numerical way. For the estimation of SOC, the nonlinearity is mainly reflected in the OCV-SOC function. It is found out that the gradient variation of the OCV-SOC curve is not conducive to the estimation, especially when the gradient is small and the voltage noise is large. In order to improve the estimation performance, the role of matrices Q and R as the design parameters of the SOC observer is discussed. The results indicate that the observer is able to exhibit good stability and performance under appropriate settings.

Temperature sensor-free parameter and state joint estimation for battery pack in electric vehicles

Model based state estimation methods rely heavily on a relatively stable open-circuit-voltage (OCV)-state-of-charge (SOC) curve which is described as temperature dependent in traditional definition. However, the temperature of not every cell can be measured accurately in EVs, which restricts their application. To decouple the influences of temperature on battery characteristics, this paper proposes a new method to describe the relation between OCV and SOC, which simplifies a traditional four-dimensional mapping between temperature, aging, OCV and SOC into a temperature-independent three-dimensional mapping. These newly-defined capacity and OCV-SOC curve are independent of battery temperature, which are verified based on a large number of test data under different temperatures and aging conditions. A cooperative estimation of model parameters and state based on dual extended Kalman filter (DEKF) algorithm for a battery pack is developed by building a parameter database of noise characteristics into DEKF algorithm. Battery experimental and hardware-in-the-loop platform is built to verify the performances of the proposed method for battery management systems (BMSs) in all climate EVs. The results indicate that the proposed method can accurately estimate battery SOC with the error within 3 % after fast convergence regardless of battery initial SOC and capacity degradation.

A novel combined online method for SOC estimation of a Li-Ion battery with practical and industrial considerations

Estimation of the state of charge (SOC) in the battery management systems (BMS) is very crucial. However, the BMS consistently suffers from inaccuracy of SOC estimation and high computational burden in industrial applications. Herein, we propose a novel combined SOC estimation approach with high accuracy, simplicity in implementation and, robustness that can be implemented on the low-cost microcontrollers. The basis of the method is to online identification the open circuit voltage (OCV) value of the battery simultaneously with other unknown parameters of the battery equivalent circuit model (ECM). Using this identified OCV, SOC is extracted from the OCV-SOC curve, directly. This direct method is an open-loop estimation method and tends to get lower SOC accuracy compared with known methods. To overcome this disadvantage, a combined method is proposed. Hence, to estimate SOC in this method, in the middle part of the OCV-SOC curve (linear part) and after identifying OCV from recursive least square algorithm (RLS) method with a forgetting factor, SOC is estimated directly via OCV-SOC curve. Furthermore, in the other parts of the curve especially non-linear parts, SOC is estimated with the EKF algorithm. In summary, not only estimation accuracy will increase, but also the computational burden of the EKF will decrease. In this paper, voltage and current samples of the battery at each sample time are the inputs of the algorithms and gained from the known “LA-92 dynamic driving cycle” test. To validate the efficiency of the presented method, results are compared with the EKF algorithm and direct method. The experimental results show that the proposed method reduces the EKF calculations by almost 15 % and guarantee high accuracy SOC estimation. The maximum error of the proposed method is <2.5 %.

Time-efficient identification of lithium-ion battery temperature-dependent OCV-SOC curve using multi-output Gaussian process

Time-efficient identification of lithium-ion battery temperature-dependent OCV-SOC curve using multi-output Gaussian process

Exploring the Hysteresis Effect in SOC Estimation of Li-ion Batteries

The global energy crisis and serious environmental pollution make governments attach importance to the development of regenerating green energy. As the best battery system at present, lithium-ion battery has developed rapidly into a new generation of energy storage power. SOC estimation is an important indicator in the development of lithium-ion batteries. If SOC can be properly estimated, it will be pivotal to the investigation and exploitation of batteries, and a number of algorithms for SOC estimation have already emerged. This paper first introduces several current SOC estimation methods, and then explains the influencing factors and hysteresis effect of SOC. Then the SOC estimation value was obtained by open circuit voltage method, based on the mean value calculated by the random forest output OCV-SOC curve to reduce hysteresis effect. The effect of temperature on SOC was analysed by plotting the OCV-SOC profiles for charging and discharging at different temperatures. Finally, this paper is summarized and puts forward the improvement.

A novel Co-estimation framework of state-of-charge, state-of-power and capacity for lithium-ion batteries using multi-parameters fusion method

A battery management system can intelligently manage and maintain battery systems by effectively estimating and predicting battery internal states. Owing to battery nonlinear characteristics related to various influence factors, the estimation of battery internal states should consider the available capacity and ohmic internal resistance. This paper proposes a co-estimation framework for state of charge (SOC), state of power (SOP) and battery available capacity. Firstly, the first-order equivalent model method is used to identify the battery parameters by recursive least squares algorithm with variable forgetting factor, and the SOC-OCV curve of the battery is obtained by combining the ampere-time integration method. Secondly, three Kalman filters are utilized to estimate battery SOCs and the maximum available capacity and internal resistance are estimated by a forgetting factor recursive least square algorithm. Then peak current and power are estimated under the composite constraints of the estimated capacity and internal resistance. Finally, the experimental data are collected at temperatures 25 °C and 40 °C to verify and analyze the proposed method. The results of battery state estimation indicate that the proposed framework can accurate estimation battery internal states and also provide an effective reference for the driving of powered vehicles.

State of Charge Estimation of Lithium-Ion Batteries Based on an Improved Sage-Husa Extended Kalman Filter Algorithm

An improved Sage-Husa extended Kalman filter (SHEKF) algorithm is intended to improve the accuracy and stability of SOC prediction. In this paper, two different exponential weighting algorithms are used to adaptively select the forgetting factor for adaptive noise estimation. Moreover, the OCV-SOC curve is obtained using a 7-segment linear fitting method before the algorithms estimate the SOC. In addition, by combining this improved method with a third-order RC equivalent circuit model in the dynamic stress test (DST) case the convergence time is reduced by 0.15 s compared to the second-order RC equivalent circuit model. Following that, four different types of comparison experiments are carried out by comparing the improved algorithm to EKF and other SHEKF algorithms.The estimation accuracy under DST conditions of 0 °C, 25 °C and 45 °C is approximately 0.5%, 2.2% and 1.3% improvement compared to the EKF algorithm.

Open-circuit voltage curve reconstruction for degrading lithium-ion batteries utilizing discrete curve fragments from an online dataset

A complete open-circuit voltage (OCV) curve plotted against the state of charge (SOC) for degrading batteries, as a core indicator for battery state estimation and health diagnostics, is very important for whole-life management of battery. Unfortunately, such a curve is almost impossible to obtain in online battery management systems. Due to the uncontrollable OCV sample opportunities, only a series of isolated curve fragments consisting of scarce and discrete OCV-SOC points can be collected. Due to the unavoidable SOC estimation error, the relative position between fragments is uncertain. In order to reconstruct a complete OCV-SOC curve utilizing these isolated OCV curve fragments, an online and training-free curve reconstruction method is developed in this paper. Using this method, all isolated fragments from an online dataset are adaptively rearranged and uniquely located based on the positional interlock between different fragments, and fragments with abnormal state of health (SOH) or measurement errors are screened out. The test results demonstrated that the reconstruction method has a good stability, rapidity, and accuracy. The root mean square error of the curve reconstruction is well controlled within 5 mV throughout the battery's entire lifetime.

A Model Fusion Method for Online State of Charge and State of Power Co-Estimation of Lithium-Ion Batteries in Electric Vehicles

In this paper, a model fusion method (MFM) is proposed for online state of charge (SOC) and state of power (SOP) co-estimation of lithium-ion batteries (LIBs) in electric vehicles (EVs). Firstly, a particle swarm optimization-genetic algorithm (PSO-GA) method is cooperated with a 2-RC <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CPE</sub> fractional-order model (FOM) to construct battery open-circuit voltage (OCV)-SOC curve, which only relies on a part of dynamic load profile without the prior knowledge of an initial SOC. Secondly, a dual extended Kalman filter (DEKF) algorithm based on a 1-RC model is employed to identify the model parameters and estimate battery SOC with the extracted OCV-SOC curve. Furthermore, battery polarization dynamics in a SOP prediction window is analyzed from two aspects: (1) self-recovery; and (2) current excitation. They are separately simulated using 2-RC <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CPE</sub> FOM and 1-RC model, and then integrated through a model fusion for online SOP estimation, which enables an analytical expression of battery peak charge/discharge current in a prediction window without weakening the nonlinear characteristic of FOM. Experimental results demonstrate the improved performance of the proposed MFM for online discharge SOP estimation, where the mean absolute error and root mean square error are only 0.288 W and 0.35 W, respectively, under the urban dynamometer driving schedule profile.

Analysis of open circuit voltage and state of charge of high power lithium ion battery

Electric vehicles (EVs) are the emerging technologies in the transport sector around the world. EV runs using an electric motor with electricity which is stored in Li-ion batteries (LIBs). Because of its superior qualities LIBs become the market leader for the usage in EVs. With the increased penetration of LIBs in EVs, it is important to monitor the charging and discharging process of the batteries. Accurate estimation of state of charge (SoC) and open circuit voltage (OCV) is essential for the better control of EV. In the present study LIB of 40 Ah nickel manganese cobalt (NMC) cell chemistry has been used for estimation of state of charge at different C-rates as 0.3, 0.5, 1 and 2 C rates. The relationship between the SoC and OCV is nonlinear however relationship between SoC and Ah is linear. Slight rise has been observed in cell terminal temperature at lower C rating at higher C-rating it is found that temperature rise is more (around 10 ℃). Hence, it is important to consider the C-rate of the battery from SoC-OCV curve of the battery. Also, higher C-rate may lead to incorrect estimation of the SoC, reduction in battery life and may lead to potential safety risks.

Study of hysteresis voltage state dependence in lithium-ion battery and a novel asymmetric hysteresis modeling

The relationship between open-circuit voltage (OCV) and state of charge (SOC) of lithium-ion battery, as an important battery parameter, is widely used in battery state estimation and safety management. However, the hysteresis phenomenon, which makes it difficult to obtain the OCV-SOC curve. Firstly, the hysteresis behavior in ternary lithium battery is studied in detail for temperature, current rate, SOC and charge/discharge path. Based on the experimental data, the dependence of the hysteresis behavior is summarized and analyzed. Finally, an asymmetric hysteresis operator is proposed and a hysteresis model is established to accurately characterize the OCV-SOC relationship of the battery. The model was validated by experimental data with a maximum error of 0.0170 V and an absolute average error of 0.0063 V. The results show that the model can accurately characterize the battery hysteresis effect and has positive effects for battery condition estimation and safety management.

A novel battery state estimation model based on unscented Kalman filter

Accurate estimation of the state of charge (SOC) of batteries is very important for real-time monitoring and safety control of electric vehicles. Four aspects of efforts are applied to promote the accuracy of SOC estimation. Firstly, the state-space equation of the battery model based on the Thevenin model is established and the parameters of the model are identified by the forgetting factor recursive least square method. Secondly, aiming at the nonlinear relationship between the open-circuit voltage (OCV) and SOC, the least square support vector machine is proposed to establish the mapping relationship between OCV and SOC. Thirdly, the influence of fitting accuracy of the OCV-SOC curve on SOC estimation is analyzed. Based on this, an error model is proposed, and a joint estimator using an adaptive unscented Kalman filter algorithm combining the error model is proposed. Finally, compared with the estimated SOC results of the traditional SOC estimation method, the experimental results show that the proposed model has better estimation ability and robustness.

A comparative study of the influence of different open circuit voltage tests on model‐based state of charge estimation for lithium‐ion batteries

A predetermined relationship between open circuit voltage (OCV) and state of charge (SoC) is the premise of model-based SoC estimation algorithms. Commonly used OCV tests to obtain the offline OCV-SoC curves include the low current OCV test and the incremental OCV test. In this paper, in addition to comparing the low current OCV test with the incremental OCV test which applies 0.5 C current-rate, the incremental OCV tests which apply 0.2, 0.3, and 1 C current-rates are compared as well. Moreover, from the perspective of test accuracy and test duration, the optimal relaxation time for incremental OCV test is investigated. The recursive least squares method with a forgetting factor (FFRLS) is used to identify the parameters of the battery model, and the adaptive extended Kalman filter (AEKF) is employed to estimate SoC. The results indicate that the SoC estimator which applies the OCV-SoC curve obtained from the 0.5 C incremental OCV test performs better than other estimators at 25°C and 45°C. But at 0°C, the SoC estimator which applies the OCV-SoC curve obtained from the 0.2 C incremental OCV test has the best estimation performance. In addition, 3 h relaxation time is the optimal choice at 25°C and 45°C, while 4 h relaxation time is recommended at 0°C.

Joint estimation of state of charge and state of health for lithium‐ion battery based on dual adaptive extended Kalman filter

Lithium-ion batteries (LIBs) are widely used in electric vehicles due to its high energy density and low pollution. As the key monitoring parameters of battery management system (BMS), accurate estimation of the state of charge (SOC) and state of health (SOH) can promote the utilization rate of battery, which is of great significance to ensure the safe use of LIBs. In this paper, a novel dual Kalman filter method is proposed to achieve simultaneous SOC and SOH estimation. This paper improves the estimation accuracy of SOC and SOH from the following four aspects. Firstly, the widely used equivalent circuit model is established as the battery model in this paper, and the forgetting factor recursive least squares (FFRLS) method is applied to identify the model parameters. Secondly, two kinds of single-variable battery states are established to analyze the influence of OCV-SOC curve and battery capacity on SOC estimation. Based on this, an error model is proposed combined with Kalman filter to achieve better estimation results of SOC and SOH. Besides, to promote the accuracy of SOC estimation, based on the error innovation sequence (EIS) and residual innovation sequence (RIS), the improved dual adaptive extended Kalman filter (IDAEKF) algorithm based on dynamic window is proposed. Finally, the superiority of the proposed model is verified under different cycles. Experimental results show that the estimation error of SOC and SOH is controlled within 1%.

Current Sensorless Microcontroller-Based Battery Management System with SOC and Active Cell Balancing

Battery management system (BMS) has become an important research topic following the trend and development of the electric vehicle. Although research on Active Cell Balancing, SOC, and current estimation has been carried out, the previous work mostly focused on comparing and developing methods. In this research, we demonstrate the process of designing BMS hardware using a low-cost microcontroller and without using a current sensor. The SOC simulation results produce an RMSE of 0.0832% for the 100% -10% SOC-OCV curve, and the current estimation simulation produces an RMSE of 0.2576 A, while for testing using a 6-ohm pulse load, the RMSE error value is 0.3960 A. The Active Cell Balancing method was successfully performed in simulation with Simulink. Furthermore, our simulation and test results suggest that complex battery models and multiple SOC-OCV curves can be used for better current and OCV estimation results. Our experimental results are also useful to develop a guideline to design a microcontroller-based BMS.

Fitting the OCV-SOC relationship of a battery lithium-ion using genetic algorithm method

In response to the need of reducing fossil fuel dependence and environmental impacts for ground transportation, electric vehicles (EVs) powered by lithium-ion batteries (LIBs) are being intensively researched and they have placed on the forefront as alternative vehicles. The state of charge (SOC) is one of the most important states of LIBs that is monitored online. However, the model-based method state of charge estimation requires an accurate Open circuit voltage (OCV), which is an important characteristic parameter of lithium-ion batteries, that is used to estimate battery state of charge (SOC). Therefore, accurate OCV modeling is a great significance for lithium-ion battery management. The polynomial OCV model uses the polynomial function to establish the relationship between OCV and SOC mapping. In this paper,8th degree polynomial fitting curve is considered and the genetic algorithm optimization method is proposed for estimating the parameters. The results show that the root mean square error can be decreased to 0.002. However, the best fitting OCV-SOC curve can increase the accuracy of the model and improve the accuracy of battery state estimation.

An Accurate State of Charge Estimation Method for Lithium Iron Phosphate Battery Using a Combination of an Unscented Kalman Filter and a Particle Filter

An accurate state of charge (SOC) estimation of the battery is one of the most important techniques in battery-based power systems, such as electric vehicles (EVs) and energy storage systems (ESSs). The Kalman filter is a preferred algorithm in estimating the SOC of the battery due to the capability of including the time-varying coefficients in the model and its superior performance in the SOC estimation. However, since its performance highly depends on the measurement noise (MN) and process noise (PN) values, it is difficult to obtain highly accurate estimation results with the battery having a flat plateau OCV (open-circuit voltage) area in the SOC-OCV curve, such as the Lithium iron phosphate battery. In this paper, a new integrated estimation method is proposed by combining an unscented Kalman filter and a particle filter (UKF-PF) to estimate the SOC of the Lithium iron phosphate battery. The equivalent circuit of the battery used is composed of a series resistor and two R-C parallel circuits. Then, it is modeled by a second-order autoregressive exogenous (ARX) model, and the parameters are identified by using the recursive least square (RLS) identification method. The validity of the proposed algorithm is verified by comparing the experimental results obtained with the proposed method and the conventional methods.