Open Circuit Voltage Estimation Research Articles

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.

A new time-adjustable model-based method for fast open-circuit voltage estimation of Lithium-ion cells

Any system ensuring the safety and the intelligent management of Li-ion batteries requires a thorough knowledge of the curve relating their open circuit voltage to their state-of-charge. However, classical industrial methods used to characterize this relationship necessitate a complete charge/discharge cycle at low current, or with very long relaxation times, to limit the influence of the lithium diffusion. Indeed, the latter induces voltage drops of the same order of magnitude as those induced by the conduction and charge transfer phenomena, but with much higher time constants, generally of several hours. These methods are therefore time-consuming and costly. In this paper, we propose to take advantage of both a continuous-time dynamic model of the Li-ion cell and a dedicated moving horizon estimation algorithm to significantly reduce the characterization time, while simultaneously providing the incremental capacity analysis health indicator. The proposed model structure offers the best trade-off between accuracy of the system simulation and number of parameters that can be sufficiently sensitized regarding the limited duration of the characterization protocol. The originality of the moving horizon estimation algorithm consists in centering the moving observation windows on the current time, improving de facto the estimation accuracy. When using real experimental signals of LFP and NCA cells, we obtain an estimation error lower than 5 mV, close to the best-in-class relaxation method classically used by industrial actors, while dividing the whole experiment time by more than 30.

Determination of half-cell open-circuit potential curve of silicon-graphite in a physics-based model for lithium-ion batteries

Lithium-ion batteries with silicon/graphite anodes have the potential to deliver high theoretical capacity. However, these electrodes exhibit significant hysteresis, which presents challenges in accurately estimating the open-circuit potentials (OCP) of the electrodes within a physics-based model. This paper proposes a method to establish the relationship between the electrode OCP and stoichiometry. Galvanostatic intermittent titration technique (GITT) tests are performed on half-cells to measure the charge and discharge OCP. To account for hysteresis, a hysteresis factor is defined to balance the lithiation and de-lithiation OCP. The estimated open-circuit voltage (OCV) of the full-cell is obtained by subtracting the anode OCP from the cathode OCP. The OCP and hysteresis factor are then optimized by minimizing the error between the measured OCV and the estimated OCV. Two different OCV test methods, namely the incremental method and C/30 galvanostatic method, are compared. The OCV estimation for fresh cells shows good agreement with experimental values, with root-mean-square errors (RMSEs) below 6.682 mV. To evaluate the effectiveness of the obtained OCPs in the full-cell model, the optimized OCPs are incorporated into the physics-based model. Under the Hybrid Pulse Power Characterization (HPPC) test, the electrochemical model utilizing the optimized OCP with the incremental OCV and C/30 galvanostatic OCV exhibits RMSEs of 10.587 mV and 11.016 mV, respectively, in predicting the cell voltage. Finally, the OCP identification method is assessed with cells at different aging states. The OCV predictions for degraded cells maintain RMSEs below 9.074 mV, thus validating the effectiveness of the developed OCP estimation method.

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.

Terminal voltage prediction of Li-Ion batteries using Combined Neural Network and Teaching Learning Based Optimization algorithm

The static and dynamic model parameters are critical parameters for the accurate estimation of open-circuit voltage and the terminal voltage of a Lithium-Ion (Li-Ion) battery. This work mainly focuses on the investigation of an accurate method for predicting the open-circuit voltage and terminal voltage of Li-Ion batteries. This work proposes an Enhanced Neural Network and Teaching Learning-Based Optimization (ENN-TLBO) algorithm to predict the terminal voltage of a Li-Ion battery used in electric vehicle applications. In this work, the static model is obtained by using a neural network (NN) while the dynamic model parameters are predicted by using the Teaching Learning Based Optimization technique (TLBO). In the NN method, different network parameters are varied to predict the accurate State of Charge (SoC)-Open Circuit Voltage (OCV) relation of the battery. The proposed static models are validated by the performance metrics such as RMSE and R2, the best static model is identified by low Root Mean Square Error (RMSE) and high R2 value of the model. In recent times many optimization algorithms are reported in the literature to predict the dynamic model, which requires certain algorithm-specific tuning parameters that affects the performance of the algorithm. The proposed TLBO algorithm is implemented with lesser tuning parameters to predict the hysteresis constant of the dynamic model. The RMSE value of the predicted terminal voltage at different temperature profiles is calculated to validate the proposed method. The proposed method has several advantages such as fewer tuning parameters, simple to implement, and high accuracy.

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.

A novel method for determination of SOC of Lead-acid battery in E-Rickshaw application

Electric rickshaws are a popular mode of short-distance transportation in cities and an alternative to petrol- or diesel-powered auto-rickshaws because of their low capital and running cost and less human effort compared to pedal rickshaws. E Rickshaws can operate if there is energy or charge in the battery. State of Charge (SoC) indicates the amount of energy left in the batter. Accurate estimation of SoC is essential for estimating how long the vehicle can travel apart from overcharge and under-discharge protection resulting in life enhancement. Conventional methods of SoC prediction are based on the estimation of open-circuit voltage. These are inaccurate and time-consuming and become erroneous as the battery ages. A method for quick determination of SoC using a high current short-time discharge is proposed for both old and new batteries. Experimental investigations on 90Ah C10 batteries using the said methodology can determine the correct state of charge in any condition with a maximum error of ± 8 %.

Online Estimation of Open Circuit Voltage Based on Extended Kalman Filter with Self-Evaluation Criterion

Open circuit voltage (OCV) is crucial for battery degradation analysis. However, high-precision OCV is usually obtained offline. To this end, this paper proposes a novel self-evaluation criterion based on the capacity difference of State of Charge (SoC) unit interval. The criterion is integrated into extended Kalman filter (EKF) for joint estimations of OCV and SoC. The proposed method is evaluated in a typical application scenario, energy storage system (ESS), using a LiFePO4 (LFP) battery. Extensive experimental results show that a more accurate OCV and incremental capacity and differential voltage (IC-DV) can be achieved online with the proposed method. Our method also greatly improves the accuracy of SoC estimation at each SoC point where the maximum estimation error of SoC is less than 0.3%.

A Novel Approach for the Open-circuit Voltage Estimation of Lithium-ion Batteries by epsilon SVR

A Novel Approach for the Open-circuit Voltage Estimation of Lithium-ion Batteries by epsilon SVR

Fast Open Circuit Voltage Estimation of Lithium-Ion Batteries Using a Relaxation Model and Genetic Algorithm

Battery Open Circuit Voltage (OCV) is of fundamental characteristic for enabling battery modeling and states estimation. However, the traditional OCV measurement method takes a very long time to make the battery reaches its equilibrium, which is rather inconvenient and cannot be performed online for battery energy storage application. Motived by this, this paper proposes an effective method for fast OCV estimation in the relaxation process. In this work, a novel relaxation model is designed for capturing the voltage response of a battery during relaxation time and the Genetic Algorithm (GA) is further applied for optimizing the model parameters and acquiring accurate OCV estimation results. Experimental results confirm the validity of the proposed method under different State of Charges (SOCs), current rates, ambient temperatures, and aging conditions. The results suggest that the proposed method can accurately and quickly estimate battery OCV, which only takes 10 minutes of measurement data (more than 2 hours for the traditional method) and the maximum estimation error is limited to merely 1.8 mV.

Improved P&O MPPT algorithm with efficient open-circuit voltage estimation for two-stage grid-integrated PV system under realistic solar radiation

Considering the vast improvement of photovoltaics (PVs) efficiency, this paper proposes a robust modified perturb and observe (MPO) maximum power point tracking (MPPT) algorithm. The control strategy of the proposed MPO-MPPT algorithm is based on dividing the solar module/cell P-V curve into four operating areas depending on the open-circuit voltage estimation method. The two areas, which are located far from the maximum power point (MPP), utilize large voltage fixed step-size that improves the tracking speed of the PV system. Otherwise, a small step-size P&O MPPT algorithm is used to minimize the steady-state oscillations for the other two areas due to the close locations of these areas to the MPP. As well, the proposed tracking algorithm eliminates the necessity of wide small step-size iterations by concentrating small step-size search area to 15% of the whole P-V operation region. To prove the effectiveness of the proposed MPO algorithm compared with the conventional MPPT techniques; sinusoidal, ramp, and one-day (10 hr.) irradiance profiles are applied to the solar PV system. The proposed MPO-MPPT algorithm based solar PV system has been built using MATLAB/SIMULINK software. Obviously, the system results confirm the theoretical analysis of the proposed algorithm, which boosts the PV system tracking efficiency to 99.7%.

Robust state-of-charge estimation for lithium-ion batteries based on an improved gas-liquid dynamics model

The battery analytical model often plays an important role in accurately estimating the online state-of-charge of the battery. The improved gas-liquid dynamics battery model is proposed to simulate the physicochemical behaviors of a lithium-ion battery. The estimation equations of both iterative open-circuit voltage and terminal voltage are deduced according to this model. A strong robust state-of-charge estimation method is designed without coupling optimization algorithm based on the iterative open-circuit voltage estimation equation. Experimental results of the Li(NiMnCo)O2 batteries under the Dynamic Stress Test cycle, the New European Driving Cycle, the Federal Urban Driving Schedule cycle, the Urban Dynamometer Driving Schedule cycle and the constant current test confirm the efficacy of the proposed approach. Moreover, this method has excellent robustness against the initial error of 50% state-of-charge which is eliminated within 6 s under five working conditions and it provides a reliable state-of-charge estimation for the sampling data of different sampling periods between a second and 60 s.

Parameter and State of Charge Estimation Simultaneously for Lithium‐Ion Battery Based on Improved Open Circuit Voltage Estimation Method

Herein, the improved open‐circuit voltage (OCV) estimation method is developed and applied to estimate the model parameters and state of charge (SOC) for lithium‐ion batteries simultaneously. First, the OCV and SOC mapping relationship with temperature dependence is explored with the help of the low‐current OCV test and the improved OCV estimation method is proposed for all test temperatures. Afterward, the dual adaptive extended Kalman filter (DAEKF) based on the residual sequence is utilized to identify the model parameters and estimate the SOC simultaneously. Finally, the proposed approach is verified with 50% initial SOC error and compared with the residual sequence‐based DEKF method at different temperatures under the Federal Urban Driving Schedule (FUDS) test. The results of this study indicate that the proposed DAEKF based on the proposed improving OCV estimation method and residual sequence could achieve higher SOC estimation accuracy with good robustness at all test temperatures.

Performance evaluation of online open-circuit voltage estimation method for photovoltaic system

In this paper, an online method is presented for the estimation of open-circuit voltage ( $$V_{oc}$$ ) of the photovoltaic (PV) system. This technique analytically calculates the $$V_{oc}$$ by sensing the voltage, current, and temperature of the PV system without interrupting the power flow to load. The online technique is accurate, fast, and easy to implement. The accuracy and potency of the online technique are verified by both simulation and practicals results of a 245 W PV panel. Moreover, for the fair analysis, the recently developed analytical technique, i.e, two temperature sensor method is compared with the online method. It is verified from the hardware results that the accuracy of the online technique is higher than the two temperature sensor method.

A fast capacity estimation method based on open circuit voltage estimation for LiNixCoyMn1-x-y battery assessing in electric vehicles

Abstract With the widespread popularity of electric vehicles (EV), effective assessment for retired EVs has become increasingly critical. Unlike traditional internal combustion vehicles, for EV, batteries account for a large proportion of the entire vehicle cost. Therefore, a fast battery capacity estimation method based on open-circuit voltage (OCV) estimation is forthwith proposed. The method calculates capacity using the ratio of the change in electric quantity to the corresponding change in state-of-charge (SOC), and the SOC is estimated via a fast OCV estimation method proposed in this paper. The fast test procedure includes a charging/discharging test and a short rest, which take less than 30 minutes in total and provide the data for the battery capacity estimation. For estimation, a weighted voltage relaxation model, containing two parallel resistor–capacitor (RC) components, is established. Its parameters are then optimized using the beetle antenna search algorithm with the approximate OCV range obtained in the test and an early voltage relaxation curve. The results of the experiments show that the proposed model and algorithm can accurately estimate the OCV, and the capacity estimation can be quickly realized in half an hour while limiting inaccuracy to less than 3%.

Comparative study of lithium‐ion battery open‐circuit‐voltage online estimation methods

As an important property and distinct characteristic of different lithium-ion batteries, open-circuit-voltage (OCV) online estimation can provide useful information for battery monitoring and fault diagnosis. However, studies dedicated to battery OCV estimation are not as much as the research efforts on state-of-charge determination and parameter identification such as capacity and resistance. Hence, a general discussion for selecting the battery OCV estimation algorithm is proposed in this study. To this end, modelling process of extended state-space model and autoregressive exogenous model is presented in detail. Four estimation algorithms, namely, Luenberger observer, Kalman filter, recursive least-square with forgetting factor and recursive least-square with variable forgetting factor are selected and compared in terms of estimation accuracy, computational cost, parameter tuning and robustness to parameter variations. Based on real battery cell parameters and environmental conditions, simulation results have shown that even if they are less robust to model uncertainty, observer-based methods exhibit better estimation performances than regression-based ones.

Analysis of edge losses on silicon heterojunction half solar cells

Halved and shingled solar cells are a powerful technology to reduce cell-to-module losses and ultimately increase the output power of a photovoltaic module. The combination of this approach with high-efficiency silicon solar cells architectures, like heterojunction or passivated contacts solar cells, is a promising option, but comes also with new challenges. In particular, the low level of carrier recombination in these cells makes them highly sensitive to any additional defects, as it is the case for the unpassivated edge created when splitting in half a solar cell. In order to optimize the cutting processes, an accurate method to characterize edge losses, adapted to high-efficiency solar cells, is required. In this work, two approaches are proposed and compared. In the first one, edge losses are modelled with a parallel diode, and the associated recombination current (J02edge) is fitted with current-voltage measurements, as usually done in previous studies. The conditions of application of this method are widely discussed and precisely redefined for an accurate extraction of parameters in the case of high-efficiency solar cells. The second method proposed consists in simply calculating relative losses out of the main current-voltage parameters. We demonstrate that simple estimation of open-circuit voltage (Voc) losses is perfectly adapted to our characterization needs, provided that adapted measurement methodology is applied. Results obtained with both methods are then compared on heterojunction cells and discussed. If these two characterization techniques both provide accurate results for the laser scribing processes tested, the method based on open-circuit voltage loss comparison shows slightly less dispersion. Moreover, this last method does not rely on curve fitting, making it faster and insensitive to modeling issues. The Voc approach appears thus more adapted for a fine optimization of different cutting processes and eventual characterization step implemented in an industrial environment. However, the alternative J02edge method based on curve fitting remains of high interest, for example to compare cell losses obtained with usual values reported in the literature, provided that extraction of this parameter is done with the conditions assessed in this work.

Reconciling the value of Schottky barriers in small molecular organic photovoltaics from J-V and C-V measurements at low temperatures towards the estimation of open circuit voltage at 0 K

Abstract Open circuit voltage of a photovoltaic system, in general, is constrained by the Schottky barrier (SB) heights formed at the electrodes. Here, the SB heights which are inhomogeneous in nature at the anode-semiconductor junction of a donor-acceptor-acceptor molecule, 2-[(7-(4-[N,N-bis(4-methylphenyl)amino]phenyl)-2,1,3-benzothia-diazol-4-yl)methylene] propane-dinitrile (DTDCPB), mixed with C70 as a bulk heterojunction have been studied thoroughly by inserting an insulating layer of MoO3 with different thicknesses (6 nm, 12 nm, 18 nm) and measuring the current density-voltage (J-V) and capacitance-voltage (C–V) characteristics of the photovoltaics under a large temperature range of 100K–300 K for proper estimation of open circuit voltage (Voc). Experimental results reveal a linear inverse temperature dependence of SB heights in the whole temperature range. The mismatch in the extracted values of SB heights from the independent measurements of J-V and C–V vanishes under the consideration of non-linear temperature dependence of built-in potential (Vbi) leading to the legitimate prediction of Voc.

Optical isolation mechanism based MPPT for PV array under partial shading condition

The status of each photovoltaic (PV) module, whether bypass or not, can play a vital role in the designing of an efficient MPPT, especially for partial shading conditions. In this paper, an optocoupler integrated circuit (opto-IC) sensing based MPPT is presented. Initially, an electronic circuit of opto-IC is proposed for each module. Compared to analog sensing, this circuit is low power, less complex, inexpensive, fast and one package solution. Thereafter, a matrix network is devised, which uses the data of opto-IC to perform the following two tasks: (1) if partial shading is identified, only critical regions of I-V curve are examined to detect the global maximum (GM), and (2) if uniform condition is identified, the PV array is set at 0.8 of array’s open-circuit voltage. Finally, P&O is used to reach the MPP precisely. Another important aspect of proposed MPPT is the smart estimation of open-circuit voltage of array through opto-IC data. A comparative analysis between multiple MPPTs is presented, according to which the proposed method outperforms other MPPTs in the context of convergence speed and dynamic efficiency.

Implementation of an SOC-based four-stage constant current charger for Li-ion batteries

Abstract This study implements a possible use of the state of charge (SOC) instead of the charge voltage limit (Vlimit) to control the charging process for a four-stage constant current charging strategy. To determine the charging current in each stage, an iterative optimization procedure based on Taguchi method is employed to find near-optimal values. To control the change of the charging stage and terminate the charging process, the Coulomb counting method combined with battery’s open circuit voltage estimation is adopted for SOC estimation. Tests of Sanyo 840 mA h, 3.6 V lithium-ion (Li-ion) batteries have been conducted with a Keithley 2230-30-1 triple power supply and a Prodigit 3332 F dual electronic load. The implemented charger has an input voltage of 12 V, output currents of 1.176 A, 0.840 A, 0.588 A, and 0.336 A, as well as an output voltage ranged from 0.168 V to 0.588 V. By performing the experiments, the proposed charging strategy has shorter charging time than the equivalent constant current constant voltage (CCCV) and the Vlimit-based charging methods. However, it yields a slightly lower charging efficiency than the equivalent CCCV and pulse current charging methods.