Battery Open Circuit Voltage Research Articles

A Curve Relocation Approach for Robust Battery Open Circuit Voltage Reconstruction and Capacity Estimation Based on Partial Charging Data

A Curve Relocation Approach for Robust Battery Open Circuit Voltage Reconstruction and Capacity Estimation Based on Partial Charging Data

Open circuit voltage of an all-vanadium redox flow battery as a function of the state of charge obtained from UV-Vis spectroscopy

A unique feature of redox flow batteries (RFBs) is that their open circuit voltage (OCV) depends strongly on the state of charge (SOC).

Deep neural network-enabled battery open-circuit voltage estimation based on partial charging data

Battery management systems (BMSs) play a vital role in ensuring efficient and reliable operations of lithium-ion batteries. The main function of the BMSs is to estimate battery states and diagnose battery health using battery open-circuit voltage (OCV). However, acquiring the complete OCV data online can be a challenging endeavor due to the time-consuming measurement process or the need for specific operating conditions required by OCV estimation models. In addressing these concerns, this study introduces a deep neural network-combined framework for accurate and robust OCV estimation, utilizing partial daily charging data. We incorporate a generative deep learning model to extract aging-related features from data and generate high-fidelity OCV curves. Correlation analysis is employed to identify the optimal partial charging data, optimizing the OCV estimation precision while preserving exceptional flexibility. The validation results, using data from nickel-cobalt-magnesium (NCM) batteries, illustrate the accurate estimation of the complete OCV-capacity curve, with an average root mean square errors (RMSE) of less than 3 mAh. Achieving this level of precision for OCV estimation requires only around 50 s collection of partial charging data. Further validations on diverse battery types operating under various conditions confirm the effectiveness of our proposed method. Additional cases of precise health diagnosis based on OCV highlight the significance of conducting online OCV estimation. Our method provides a flexible approach to achieve complete OCV estimation and holds promise for generalization to other tasks in BMSs.

Calendar Aging Effect on the Open Circuit Voltage of Lithium-Ion Battery

In recent years, lithium-ion batteries (LiBs) have gained a lot of importance due to the increasing use of renewable energy sources and electric vehicles. To ensure that batteries work properly and limit their degradation, the battery management system needs accurate battery models capable of precisely predicting their parameters. Among them, the state of charge (SOC) estimation is one of the most important, as it enables the prediction of the battery’s available energy and prevents it from operating beyond its safety limits. A common method for SOC estimation involves utilizing the relationship between the state of charge and the open circuit voltage (OCV). On the other hand, the latter changes with battery aging. In a previous work, the authors studied a simple function to model the OCV curve, which was expressed as a function of the absolute state of discharge, q, instead of SOC. They also analyzed how the parameters of such a curve changed with the cycle aging. In the present work, a similar analysis was carried out considering the calendar aging effect. Three different LiB cells were stored at three different SOC levels (low, medium, and high levels) for around 1000 days, and an analysis of the change in the OCV-q curve model parameters with the calendar aging was performed.

Adaptive Constant-Current/Constant-Voltage Charging of a Battery Cell Based on Cell Open-Circuit Voltage Estimation

This paper presents the novel design of a constant-current/constant-voltage charging control strategy for a battery cell. The proposed control system represents an extension of the conventional constant-current/constant-voltage charging based on the so-called cascade control system arrangement with the adaptation of the battery charging current based on the open-circuit voltage parameter estimation. The proposed control strategy features two feedback controllers of the proportional-integral type responsible for: (i) controlling the battery open-circuit voltage towards its fully charged state, and (ii) simultaneously limiting the battery terminal voltage to avoid the battery terminal voltage constraint violation. The open-circuit voltage on-line estimation is implemented by using the system reference adaptive model approach to estimate the linear time-invariant battery equivalent circuit model parameters, whose asymptotic convergence is guaranteed according to Lyapunov stability theory. The proposed concept of the battery charging control is verified by means of simulations using the experimentally obtained model of a lithium iron phosphate battery cell, and it is also compared to other charging methods with respect to charging speed-up potential compared to conventional charging. The proposed method, which can be easily extended to conventional chargers, results in 23.9% faster charging compared to conventional charging, thus representing an inexpensive and straightforward upgrade to conventional battery charging systems.

A novel static model prediction method based on machine learning for Li-ion batteries operated at different temperatures

In electric vehicles (EVs) and microgrid systems, lithium-ion (Li-ion) batteries are the most realistic choice for energy storage and utilization applications. The precise prediction of the static and dynamic properties of the battery is crucial for these applications. Traditional methods use system identification and optimization concepts to predict the static characteristics; these strategies require both higher-order terms and model re-tuning for each operating temperature. In order to create a unique static model of the battery, this work suggests a machine learning (ML)-based prediction method that uses data-driven concepts to develop the prediction model between the input and output variables with more accuracy. In order to create an accurate prediction model based on the given dataset, a complete dataset of the entire process is fed to the training algorithm. In the conventional ML approach, for each operating temperature a prediction model is developed to predict the static model. But in the proposed technique, a unique ML based model is suggested to determine the relationship between the battery's open circuit voltage (OCV) and State of Charge (SoC) at various operating temperatures. To identify the best prediction model, performance metrics such as root mean square (RMSE) and R2 are used. From the results, it is identified that the neural network-based SoC-OCV prediction is the best among the strategies that have been proposed. Since the predicted model is unique, it can be used at any operating temperature without requiring the prediction model to be adjusted. The proposed model can be used to estimate battery parameters and battery state such as SoC in EV applications.

A Composite State of Charge Estimation for Electric Vehicle Lithium-Ion Batteries Using Back-Propagation Neural Network and Extended Kalman Particle Filter

Accurate estimation of battery state of charge (SOC) plays a crucial role for facilitating intelligent battery management system development. Due to the high nonlinear relationship between the battery open-circuit voltage (OCV) and SOC, and the shortcomings of traditional polynomial fitting approach, it is an even more challenging task for predicting battery SOC. To address these challenges, this paper presents a composite SOC estimation approach for lithium-ion batteries using back-propagation neural network (BPNN) and extended Kalman particle filter (EKPF). First, a second order resistance capacitance model is established to make parameters identification of a lithium-ion battery cell using recursive least squares algorithm with forgetting factors (FFRLS) approach. Then, BPNN is used to fit the desired OCV-SOC relationship with relatively high precision. Next, by incorporating the extended Kalman filter (EKF) into the particle filter (PF), an expected EKPF approach is presented to realize the SOC estimation. Last, the performances of SOC estimation using different methods, namely the PF, EKF and the EKPF are compared and analyzed under constant current discharge and urban dynamometer driving schedule working conditions. The experimental results show that the proposed method has higher accuracy and robustness compared to the other two SOC estimation methods.

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.

An adaptive capacity estimation approach for lithium-ion battery using 10-min relaxation voltage within high state of charge range

Capacity estimation is essential for battery health management during the whole lifecycle. The data-driven technique has shown advanced performance in battery capacity estimation. However, the strict limitations on application scenarios and the long duration for feature determination are still the bottlenecks of existing data-driven estimation methods. This study proposes a data-driven capacity estimation method only using 10-min relaxation voltage data, which is adaptable to the high state of charge (SOC) range. The experiments of commercial batteries are designed to investigate the coupling relationship between relaxation voltage, battery aging, and charging cut-off SOC. Further, a novel dual Gaussian process regression (GPR) framework is put forward, in which one GPR is responsible for the battery open-circuit voltage (OCV) estimation using the sequential relaxation voltage feature, and another GPR estimates battery capacity with the corresponding relaxation voltage feature and the estimated OCV. Quantitative experimental results demonstrate that the proposed approach can achieve accurate OCV estimation with extremely sparse voltage data. When SOC is larger than 90%, the capacity estimation achieves a mean absolute error of 2.493% over the battery lifecycle, showing a noticeable improvement over the traditional estimation method.

Dual fuzzy-based adaptive extended Kalman filter for state of charge estimation of liquid metal battery

Liquid metal batteries (LMBs) are alternatives to conventional lithium-ion batteries due to their specific benefits including high current density and long cycle life. Accurate state of charge (SOC) estimation is an important evaluation index for the battery management system (BMS), which is of great significance to ensure the safe operation of batteries. However, the estimation accuracy of SOC is influenced by many factors, including self-aging and external operating environment changes. Therefore, an online battery model with real-time parameter updates is necessary for accurate SOC estimation. In this paper, a novel dual fuzzy-based adaptive extended Kalman filter (DFAEKF) method is proposed for the SOC estimation of LMBs. Firstly, a second-order RC equivalent circuit model is established to describe the battery's behavior. The forgetting factor recursive least squares (FFRLS) is utilized to identify the model parameters and reconstruct the battery open circuit voltage (OCV). Secondly, the dual adaptive extended Kalman filter (DAEKF) is derived from the battery model. And an intelligent noise estimator is designed based on a fuzzy inference system, which adaptively adjusts the length of the residual innovation sequence (RIS), to update the noise covariance. Finally, the DFAEKF algorithm is proposed for the battery SOC and parameter co-estimation. The online estimated ohmic resistance is employed to calculate the state of health (SOH) of the battery. The proposed DFAEKF is verified through different experiments and compared to conventional algorithms. Experimental results show that the DFAEKF has higher accuracy (error < 1 %) and stronger robustness. The proposed method can also be applied to other model-based state estimation areas.

Operando monitoring of the open circuit voltage during electrolyte filling ensures high performance of lithium-ion batteries

The wetting process plays an important role in battery production efficiency and battery quality, including available energy density, cycling life, power, and battery consistency. A convenient and efficient method for characterizing electrolyte filling, which becomes more crucial for lithium-ion batteries (LIBs) with a large format or super energy density, is desperately needed in the battery industry. Herein, we propose the operando monitoring of the open circuit voltage (OCV) during the electrolyte filling process. It is found that battery OCV drops dramatically to − 0.80 V at the beginning of filling (within 300 ms), and then characteristically recovers to 0.10 V with the wetting process, involving valuable information about the electrolyte filling process. Insights of the correlation between electrolyte wetting process and battery OCV are further stimulated using an equivalent circuit model. The recovery rate of OCV can be a critical indicator to quantify the electrolyte wetting process, verifying by batteries used separators with different wettability. This study provides a practical and effective tool to ensure the high-quality electrolyte infiltration process of LIBs.

Study on the Relationship Between Open-Circuit Voltage, Time Constant And Polarization Resistance of Lithium-Ion Batteries

Battery management system (BMS) is an important role in battery applications. In BMS, the accurate estimation of the state of charge (SOC) of lithium-ion batteries is most important. Open circuit voltage (OCV) is very important for the accurate estimation of SOC. In order to obtain accurate SOC, the relationship between OCV and SOC requires real-time and accuracy. Due to the difference in lithium-ion concentration and battery internal resistance in the lithium-ion battery, OCV has the characteristics of relaxation. It is necessary to study the relaxation behavior of battery OCV. In this paper, the OCV behavior is studied and focuses on the relationship of the time constant and polarization resistance with SOC during relaxation. The results show that, when the SOC is 30%–100%, the time constant and polarization resistance of lithium-ion batteries are the smallest, the performance is the most stable, and the SOC estimation accuracy is the highest. When the battery is overcharged or SOC is low, the performance of the battery is obviously unstable. Finally, it is proposed that the SOC range of the battery can be roughly estimated by calculating the time constant, and the current aging degree of the battery can be judged.

The lithium battery SOC estimation on square root unscented Kalman filter

Accurate state of charge (SOC) estimation is still one of the most important academic and application problems in the field of new energy so far. In order to improve SOC accuracy of lithium cobalt oxide battery, the authors obtained the SOC data with battery open circuit voltage (OCV) by charge/discharge experiments. Besides, the parameters of second-order RC equivalent circuit, including polarization capacitors and resistances, were identified through the rebound voltage test under different SOC condition. The model simulation data was carried out with MATLAB/Simulink by applying user-defined current. The simulation results whose error value was less than 25 mV fit the experimental data quite well and it well verified the correctness of the battery model and the accuracy of the parameters identifications. In this paper, a square root UKF algorithm was constructed Systematically on the lithium cobalt oxide battery model to estimate the SOC under complex conditions and environment. By comparing the SOC estimation results between traditional Kalman filter with the SR-UKF, the data indicated that SR-UKF algorithm our proposed can increase the robust of the filter and the estimation error can be reduced to less than 1.5%.

Estimation of battery open-circuit voltage and state of charge based on dynamic matrix control - extended Kalman filter algorithm

State of Charge (SOC) estimation is one of the most important functions of the battery management system for new energy vehicles. Extended Kalman Filter (EKF) algorithm has been widely used in SOC estimation of lithium-ion batteries (LiBs). However, the model parameters in the SOC estimation algorithm change with the aging of the battery, which makes EKF unable to obtain accurate estimation results. Because of these defects, a battery model is built based on the second-order RC equivalent circuit model. The parameters of the LiB model are identified by an off-line test and the battery simulation model is calibrated. Then, based on the simulation model, the influence of Open Circuit Voltage (OCV) deviation on SOC estimation accuracy of the EKF algorithm is quantitatively analyzed. The results show that the deviation of OCV will affect the accuracy of SOC estimation by the EKF algorithm. When the OCV deviation reaches 15 mV, the SOC estimation error will reach 5%. Subsequently, a method of updating model parameters based on the Dynamic Matrix Control (DMC) algorithm is proposed. And the DMC-EKF algorithm is used to estimate OCV and SOC. The results show that after the DMC algorithm is used to linearize the RC network, the identified OCV parameter deviation is <10 mV, and the SOC deviation estimated by the DMC- EKF algorithm is <5%, which can meet the application requirements. The EKF algorithm can estimate the SOC more accurately after updating the OCV by the DMC algorithm. Compared with EKF algorithm and UKF algorithm without online OCV updating, DMC-EKF algorithm reduces the maximum deviation of SOC estimation by at least 2%. The proposed DMC-EKF algorithm has good accuracy and robustness.

A novel data-driven method for mining battery open-circuit voltage characterization

Lithium-ion batteries (LiB) are widely used in electric vehicles (EVs) and battery energy storage systems, and accurate state estimation relying on the relationship between battery Open-Circuit-Voltage (OCV) and State-of-Charge (SOC) is the basis for their safe and efficient applications. To avoid the time-consuming lab test needed for obtaining OCV-SOC curves, this study proposes a data-driven universal method by using operation data collected onboard about the variation of OCV with ampere-hour (Ah). To guarantee high reliability, a series of constraints have been implemented. To verify the effectiveness of this method, the constructed OCV-SOC curves are used to estimate battery SOC and State-of-Health (SOH), which are compared with data from both lab tests and EV manufacturers. Results show that a higher accuracy can be achieved in the estimation of both SOC and SOH, for which the maximum deviations are less than 3.0% and 2.9% respectively.

Optimisation based 3-dimensional polynomial regression to represent lithium-ion battery's open circuit voltage as function of state of charge and temperature

An accurate representation of Open Circuit Voltage (OCV) as a function of State of Charge (SoC) and temperature is a vital component of Battery Management System (BMS). The conventional representation of OCV through either look up table or temperature dependent equations requires enormous memory. In this article a novel approach of representing the OCV with a three dimensional polynomial equation through optimization based regression analysis is presented. The OCV of the cell is obtained from the incremental OCV test data performed at three different temperatures 0 °C, 25 °C and 45 °C. These experimental data of OCV is subjected to optimization based regression algorithms. The present study compares various optimization algorithms for three dimensional regressions through statistical parameters such as RMSE, MAE and Variance. Differential Evolution provides the least RMSE of 1.48 × 10−2, 1.34 × 10−2 and 0.37 × 10−2 for Charging, Discharging and Average OCV with a reduced MAE and variance which is in order of 10−9 and 10−5. It also provides optimised output in only 178 iterations which is lower than other algorithms considered in the article. The memory requirement of the proposed methodology of representation is of 30 bytes irrespective of number of data points. The computational time is reduced by ‘3P + 1’ cycles where P is the time taken for computing a power term in an ECU. Therefore, the comparison of the proposed methodology for three dimensional OCV representation with aforementioned methods reveals reduction of memory requirement and computational complexity in BMS.

Heavy 2D VSi2N4: High capacity and full battery open-circuit voltage as Li/Na-ion batteries anode

High specific capacity and full battery open-circuit voltage are two critical specifications for high-performance secondary Li-ion or Na-ion batteries (LIBs/NIBs). Advancement of two-dimensional (2D) materials have given more chances to helping stockpiling capacity and augmenting full battery open-circuit voltage. Herein, using density functional theory computational strategy, we predict that VSi2N4, a recently proposed 2D material with sizeable atomic mass, can be an ultra-high capacity LIBs (1312 mAh/g) and NIBs (492 mAh/g) anode, much larger than 372 mAh/g of graphite (LiC6) and hard carbon (NaC6). The calculated average open-circuit voltages (OCV) are 0.02–0.06 V for LIBs, and 0.02–0.17 V for NIBs, very beneficial to increasing the full battery’s operating voltage. It additionally satisfies other demands for promising LIBs/NIBs anode. 2D VSi2N4 exhibits favourable electrical conductivity with and without Li or Na adsorption. The diffusion study shows that the material has good rate performance (∼0.33 eV and ∼0.16 eV for Li and Na diffusion barrier, respectively). Furthermore, the material shows high structural stability and excellent cycling performance due to the tiny lattice changes (∼0.05%–∼0.87% for Li, ∼0.38%–∼0.95% for Na) during Li or Na intercalation. All the mentioned results propose that heavy 2D VSi2N4 can be an extremely encouraging LIBs or NIBs anode.

Throughput Estimation of K-zone Gbps Radio Links Operating in the E-band

This paper presents a method for post-fault operation with reduced performance of a permanent magnet synchronous motor following a battery supply open circuit fault. The approach upgrades the previously developed fast model-oriented supply fault detection algorithm based on comparing actual and estimated values of a DC-link voltage. The latter is determined by the model of supply circuit and observer of battery open-circuit voltage. Implementing a post-fault concept does not require additional hardware, as it uses quantities from an already existent vector control algorithm. After the fault is detected, the algorithm is upgraded by a DC-link voltage cascade control loop. An analytical approach to parametrization of the controller, based on the linearization and the reduction of the system's transfer function, is also proposed. Simulations and experimental results have validated the performance of the post-fault algorithm.

Enhanced Lithium-ion battery model considering critical surface charge behavior

Battery model is the basis of battery efficient and safe management. The widely used equivalent circuit model (ECM) generally shows poor behavior in predicting battery terminal voltage at low sate of charge (SOC), increasing the risk in the urgent use of a battery at low voltage greatly. To model strong nonlinearity of battery open circuit voltage (OCV), a solid-phase diffusion equation based surface SOC is proposed to characterize OCV behavior and establish the new structure of the enhanced ECM to describe low SOC behavior more precisely. Finally, a battery test platform was built to conduct battery tests for model validation. The results show that the root mean square error (RMSE) of the battery terminal voltage obtained from the proposed model at low SOC has been reduced to 8 mV compared with the RMSE of 17 mV from the traditional ECM model. It is expected that the proposed model can be employed in battery management systems to effectively improve the reliability and safety of emergency use of a battery at low SOC.

Real Time Li-Ion Battery Bank Parameters Estimation via Universal Adaptive Stabilization

This paper proposes an accurate and efficient Universal Adaptive Stabilizer (UAS) based online parameters estimation technique for a 400 V Li-ion battery bank. The battery open circuit voltage, parameters modeling the transient response, and series resistance are all estimated in a single real-time test. In contrast to earlier UAS based work on individual battery packs, this work does not require prior offline experimentation or any post-processing. Real time fast convergence of parameters' estimates with minimal experimental effort enables update of battery parameters during run-time. The proposed strategy is mathematically validated and its performance is demonstrated on a 400 V, 6.6 Ah Li-ion battery bank powering an induction motor driven prototype electric vehicle (EV) traction system.