Coulomb Counting Method Research Articles

A Novel Fitting Polynomial Approach for An Accurate Soc Estimation In Li-Ion Batteries Considering Temperature Hysteresis

Lithium-ion batteries are essential to modern technology, requiring accurate estimation of the state of charge (SOC) for optimal performance. Traditional methods such as Coulomb Counting (CC) are ineffective in the face of temperature variations, leading to inaccuracies in SOC estimation, which in turn cause obvious deformation of hysteresis curves. To address this, this paper introduces a novel method called Polynomial Fit State of Charge (FPSOC), for effective SOC estimation. This method incorporates a fifth-degree polynomial fitting that accounts for a wide range of temperature variations (from -10°C to +80°C), a feature that, according to the authors, has not been offered by all previously published methods. A series of simulation tests using the MATLAB/Simulink tool are conducted under various temperature profiles to evaluate the effectiveness of the FPSOC method. The results demonstrate the notable superiority of the FPSOC model compared to the CC method, with a significantly reduced RMSE of only 0.93% compared to 6.77% of the CC model. Particularly effective at low SOC levels (30%), the FPSOC model demonstrates precision up to six times higher compared to the CC model. Additionally, when evaluated against other recent SOC estimation techniques such as CM, RLSF, EKF, DST, BBDST, ASMO, LPM_H, LSTM-SA Group A and B, and baseline ECM-ID, The FPSOC method proves extremely accurate, with the lowest average error under different temperature conditions.

Estimation of SOC for Li-ion battery-powered three-wheeled electric vehicle using machine learning methods

Abstract The Battery Management System (BMS) serves as the heart of the electric vehicle system, in which estimating the state of charge (SOC) is the crucial part of the BMS to ensure the durability, reliability, and sustainability of the battery pack. Due to its nonlinear characteristics, accurately estimating the SOC for a slow degradation of the charge is highly cumbersome. The literature provides a series of machine learning algorithms (MLA) to estimate and predict the SOC of lithium-ion (Li-ion) battery systems for electric vehicle (EV) applications. The literature has proposed various MLA, coulomb counting, and different Kalman filter methods to address this challenge and estimate the SOC of Li-ion battery systems for EV applications. This research looks at the differences and similarities between the coulomb counting method, the unscented Kalman filter method, and a number of machine learning algorithms. These include linear regression, polynomial linear regression, support vector regression, decision trees, random forests, artificial neural networks (ANN), and long short-term memory (LSTM). The goal is to assess the MLAs' accuracy in estimating battery SOC. Analyzing model errors optimizes the battery's performance parameter. We identify ANN and LSTM as the two most efficient methods for accurate SOC estimation in an EV-operated BMS system by evaluating the performance indices of the aforementioned machine learning methodologies. Once again, the LSTM model for SOC estimation has proven to be highly accurate in analyzing the discrepancy between the actual and predicted traveling ranges of the designed prototype. We design a MATLAB/SIMULINK EV powertrain by collecting realtime data from the Li-ion battery pack, analyzing the SOC variation data, and using the previously mentioned MLA in the Python platform to estimate the SOC and its accuracy. It highlights the effectiveness of advanced MLAs in improving SOC estimation, thereby enhancing the performance and reliability of EV battery systems.

Relating state-of-charge to impedance and equivalent circuit parameters in lithium-ion cells: An experimental study

The wide-band impedance of Lithium-ion (Li-ion) batteries has become a focal point for researchers interested in State-of-Charge (SOC) estimation and battery modeling. However, the interplay between SOC, wide-band impedance, and model parameters has not been extensively explored. This paper addresses this gap by investigating how SOC impacts the impedance and Equivalent Circuit Model (ECM) parameters of a Li-ion battery cell through empirical measurements. The study utilizes discharge tests to vary the SOC and employs the Coulomb Counting Method (CCM) to estimate the approximate SOC from the response signals. A novel measurement technique is introduced and applied to determine a cell’s wide-band impedance at different SOC levels. This innovative approach uses a Pseudo-random Impulse Sequence (PRIS) perturbation signal to measure wide-band cell impedance. The collected impedance data is then used to estimate the ECM parameters. Experimental findings reveal an inverse relationship between impedance and SOC. Additionally, the ECM parameters exhibit significant correlation with SOC, particularly in mid- to low-frequency regions. This research highlights that SOC profoundly influences ECM parameters, offering valuable insights into the performance of Li-ion batteries under various conditions.

EV Battery Management using Adaptive Kalman Filter and ECC

This initiative addresses the critical concerns of enhancing battery management and easing the calculation of battery capacity in electric vehicles. By using Coulomb counting method, the State of Charge (SoC) of Electric Vehicle (EV) batteries is estimated by analysing real-time battery data through simulations and interpolation techniques. The primary objective is to offer precise SoC estimation to mitigate the range anxiety. Furthermore, this research proposes methods of estimating the SoC of Lithium-Ion batteries and an enhanced coulomb counting model with Adaptive Kalman Filter to estimate the state of charge with higher accuracy. This comprehensive approach seeks to address critical challenges in EV battery management contributing significantly to the electric vehicle technology for the society.

Design Of Electric Motorcycle Variable with Battery Management System

This study focuses on conceptualization and development of a battery management system (BMS) with two main functions, battery monitoring and management, in the context of brushless direct current motors (BLDCs). The main challenge in variable estimation is to protect the battery from potential risks during the charge and discharge cycle. The new proposed resolution combines a comprehensive BMS with monitoring capabilities for charge (SoC), health (SoH), voltage, current and battery temperature. In addition, a protective mechanism is incorporated to prevent variables from overshooting safety parameters. This research uses two different methodologies for estimating SOC, coulomb counting and open circuit voltage. In experimental tests, resistance potentiometers of 1,650, 3,300 and 0 were used, with SoC estimates of 37%, 19% and 65%, while coulomb counting method has a marginal error of 1.13%. On the contrary, the open-circuit voltage method generated a SoC estimate of 0% for all potentiometer resistance, with an error rate of 0.64 %. As a result, the open circuit voltage method is chosen because of its superior accuracy compared to the coulomb counting method. The state assessment of the battery showed a value of 100% after seven cycles. In addition, a protective system has been implemented to ensure that battery variables remain within the safe thresholds throughout the charge and discharge process. Consequently, the implementation of this BMS is expected to significantly improve overall performance and extend battery life.

A Method for Estimating the State of Charge and Identifying the Type of a Lithium-Ion Cell Based on the Transfer Function of the Cell

This paper proposes a new method for assessing the state of charge (SoC) and identifying the types of different lithium-ion cells used in the battery systems of light electric vehicles. A particular challenge in the development of this method was the SoC estimation time, as the method is intended for implementation in the control system of a bicycle charging station, where the state of charge must be determined immediately after the bicycle is plugged in in order to start the charging process as quickly as possible according to the appropriate charging algorithm. The method is based on the identification of the transfer function, i.e., the dynamic response of the battery voltage to the current pulse. In the learning phase of this method, a database of reference transfer functions and corresponding SoCs for a specific type of battery cell is created. The transfer functions are described by coefficients determined through the optimization procedure. The algorithm for estimating the unknown battery cell SoCs is based on the comparison of the measured voltage response with the responses of the reference transfer functions from the database created during the learning process to the same current signal. The comparison is made by calculating the integral of the square error (ISE) between the response of the specific reference transfer function and the measured voltage response of the battery cell. Each transfer function corresponds to a specific SoC and cell type. The specific SoC of the unknown battery is determined by quadratic interpolation of the SoC near the reference point with the smallest ISE for each battery type. The cell type detection algorithm is based on the fact that the integral squared error criterion near the actual SoC for the actual cell type changes less than the squared error criterion for any other battery cell type with the same SoC. An algorithm for estimating the SoC and cell type is described and tested on several different cell types. The relative error between the estimated SoC and the actual SoC was used as a measure of the accuracy of the algorithm, where the actual SoC was calculated using the Coulomb counting method.

Design of battery state of charge monitoring and control system using coulomb counting method based

<span>Lead-acid batteries are commonly used in photovoltaic systems to store solar energy for continuous use. However, lead-acid batteries have a relatively short lifespan due to frequent over-charging and over-discharging. A battery management system (BMS) is essential for accurately predicting the battery state of charge (SoC) value in order to extend the battery lifespan. In this research, a BMS is developed using the coulomb counting method to estimate the SoC value of a lead-acid battery. The coulomb counting algorithm provides a reliable estimation of the battery’s SoC value by calculating the incoming and outgoing currents. The BMS also uses two normally closed relays to prevent overcharging and over-discharging. The first relay turns on when the SoC reaches 100% full charge and turns off when the SoC decreases to 70%. The second relay turns on when the SoC reaches 20%. The BMS was tested using Blynk, a cloud-based internet of things (IoT) platform. The results showed that the BMS successfully provided monitoring and reliable control of the lead-acid battery, with a low margin of error. This demonstrates that the developed BMS can be practically implemented in photovoltaic (PV)-battery systems to extend the battery lifespan and improve the overall performance of the system.</span>

Online state‐of‐charge estimation by modified Coulomb counting method based on the estimated parameters of lithium‐ion battery

SummaryThe state‐of‐charge (SOC) of the battery is the most critical parameter for an electric vehicle (EV) dashboard. Generally, the Coulomb counting (CC) method is used for SOC estimation, but the conventional CC method has some errors due to initial SOC determination. This work uses battery modeling and parameter estimation of a lithium‐iron‐phosphate (LFP) battery to determine the initial SOC. The equivalent circuit models (ECMs) have been used for battery parameters estimation by the least‐squares estimation (LSE) algorithm. This algorithm uses only experimental terminal voltage and current data from Arbin battery testing system. The ECM parameters vary with battery SOC, and the differential capacity plot has explained the variation. SOC can be estimated using estimated parameters and the relationship between SOC and open‐circuit voltage (OCV). Additionally, a modified CC method is proposed to solve the problem of estimating initial SOC by determining an OCV‐SOC relation using experimental data. The modeling of the battery is validated by comparing ECMs with physical battery terminal voltage profiles.

An improved coulomb counting method based on non-destructive charge and discharge differentiation for the SOC estimation of NCM lithium-ion battery

Accurate state of charge (SOC) estimation plays a crucial role in the safe and efficient operation of batteries. The coulomb counting (CC) approach has been used extensively in industrial production as a relatively easy-to-use and reliable algorithm for estimating battery SOC. However, the accuracy of standard CC method is constrained and it cannot satisfy some complex operating environment requirements. In this paper, a CC approach based on non-destructive charge and discharge differentiation is proposed on NCM battery. This method corrects the battery's initial SOC value using the open-circuit voltage method. It also corrects the actual capacity of the battery based on temperature, discharge rate and aging. Finally, it corrects the CC method's cumulative error using non-destructive capacity differentiation (dQ/dV) and voltage differentiation (dV/dQ). Experimental results show the maximum estimation error of the proposed approach is 3.33 % in the temperature range of −10 °C to 45 °C which is 15.57 % lower than the traditional OCV - CC method at −10 °C.

A Battery Capacity Estimation Framework Combining Hybrid Deep Neural Network and Regional Capacity Calculation Based on Real-World Operating Data

Efficient battery capacity estimation is of utmost importance for safe and reliable operations of electric vehicles (EVs). This paper proposes a battery capacity estimation framework based on real-world EV operating data collected from forty electric buses of the same model operating in two cities. First, a reference capacity calculation method is presented by combining the Coulomb counting method with the incremental capacity analysis method. Then the impacts of temperature, current, and State-of-Charge on battery degradation are quantitatively analyzed. Using the historical probability distributions as battery health features, a hybrid deep neural network model that combines a convolutional neural network with a fully-connected neural network is proposed for battery capacity estimation. The validation results show that the proposed model outperforms the state-of-the-art methods and reaches a mean absolute percentage error of 2.79 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> , while maintaining low computational cost.

Hybrid State of Charge Estimation of Lithium-Ion Battery Using the Coulomb Counting Method and an Adaptive Unscented Kalman Filter

This paper establishes an accurate and reliable study for estimating the lithium-ion battery’s State of Charge (SoC). An accurate state space model is used to determine the parameters of the battery’s nonlinear model. African Vultures Optimizers (AVOA) are used to solve the issue of identifying the battery parameters to accurately estimate SoC. A hybrid approach consists of the Coulomb Counting Method (CCM) with an Adaptive Unscented Kalman Filter (AUKF) to estimate the SoC of the battery. At different temperatures, four approaches are applied to the battery, varying between including load and battery fading or not. Numerical simulations are applied to a 2.6 Ahr Panasonic Li-ion battery to demonstrate the hybrid method’s effectiveness for the State of Charge estimate. In comparison to existing hybrid approaches, the suggested method is very accurate. Compared to other strategies, the proposed hybrid method achieves the least error of different methods.

Trajectory optimization for takeoff and landing phase of UAM considering energy and safety

This study performs trajectory optimization for takeoff and landing of urban air mobility (UAM) considering the energy efficiency and safety. The longitudinal model of single-seater electric vertical takeoff and landing (eVTOL) is used. In order to minimize the energy consumption, the battery state-of-charge, estimated by the coulomb counting method, is set as the objective function and the vortex ring state avoidance constraint is applied to secure the safety. The state variables are restricted in consideration of the passenger's ride quality and the takeoff and landing reference volume. The numerical optimization is conducted using direct collocation method, and the effects of the objective function and constraint are analyzed. In addition, a simulation applying a uniform wind environment is also performed to analyze the effect of the wind environment.

A Novel Method for SoC Estimation of Lithium-Ion Batteries Based on Kalman Filter in Electric Vehicle

In recent years, the energy crisis has become more and more serious. Li-ion batteries are used in grids because of their benefits such as contributing to the intermittent generation of renewable energy sources and stabilizing the grid. In addition, li-ion batteries are widely used in electric vehicles due to their long cycle life and high energy density. Li-ion battery state of charge (SoC) is an important indicator for safety. Therefore, the SoC estimation of li-ion batteries is important. Today, there are different methods to determine the state of the SoC in many applications. The traditional estimation method, the ampere-hour integration method and the coulomb counting method, has a cumulative error and cannot achieve good results in a working environment with Gaussian noise. For this purpose, in this study, firstly, the Thevenin equivalent model was created for battery SOC estimation, and then the Kalman filter algorithm was applied. Thus, the estimation error caused by Gaussian noise is eliminated. SoC estimation was simulated for the battery model created in the MATLAB/Simulink program using this method. Using these simulation results, the charge/discharge characteristics of the battery were obtained. However, the SoC estimation has been made for the charging and discharging processes of the battery. In the simulation, the charge value was recorded for 6 hours. The data recorded every 10 minutes gave results very close to the true value.

On the Usage of Battery Equivalent Series Resistance for Shuntless Coulomb Counting and SOC Estimation

In this paper, a feasibility study of a shuntless coulomb counting method for estimating the state of charge (SOC) of a battery is presented. Contrary to conventional coulomb counting, the proposed method does not require an external resistive shunt; it instead only requires voltage measurements performed on the battery under test while it is operating. The current is measured indirectly using the battery’s equivalent series resistance (ESR). The method consists of a preliminary calibration phase where the ESR and the open-circuit voltage of the battery are measured for different SOCs and stored in look-up tables (LUTs). Then, in the subsequent operational phase, the method uses these LUTs together with the measured voltage at the battery terminals to estimate the SOC. The performance of the proposed method is evaluated on a sample lithium polymer (LiPo) battery, using a realistic current profile derived from the Worldwide Harmonized Light-Duty Vehicles Test Procedure (WLTP). The results of this experimental evaluation demonstrate a SOC estimation root-mean-square error of 0.82% and a maximum SOC error of 1.45%. These results prove that the proposed method is feasible in a practical scenario.

Battery Management Optimization Considers The State Of Charge Using The Coulomb Counting Method

The Battery Management System (BMS) is important because more and more electronic devices and vehicles use batteries as a power source. Without BMS, battery charging can become unstable, resulting in overcharging and reducing battery life. Therefore, BMS is becoming increasingly important as part of the development of safer and more efficient technologies, resulting in more reliable and safer electronic devices and vehicles. The Coulomb Counting method is used to determine the state of charge of the battery at the beginning and end of charging. The coulomb counting method is very accurate for determining the state of charge because it calculates the incoming and outgoing electric charge from the read current value. The voltage from source to load uses a buck converter to step down the voltage from 27.3V source to 25.5V load voltage. The Coulomb Counting (CC) method is used to estimate the SOC of the battery being monitored. The CC method is accurate for estimating the SOC value during charging and discharging but cannot determine the initial SOC value. The results of the current test have an accuracy of 96.03% and the voltage test has an accuracy of 99.22%.

Soft Short-circuit Fault Diagnosis of the Lithium-ion Battery Pack Based on an Improved EKF Algorithm

Background: Lithium-ion batteries are widely used in new energy vehicles and energy storage systems due to their superior performance. However, lithium batteries are prone to safety problems in the use process, so the fault diagnosis technology of lithium batteries has attracted more attention. Objective: This study aimed to ensure the safety of lithium batteries and accurately and timely diagnose the soft short circuit (soft SC) fault of lithium battery. Methods: Aiming at the energy storage lithium battery pack, this study proposed a soft short-circuit fault diagnosis method for the lithium-ion battery pack based on the improved Extended Kalman Filter (EKF) algorithm. First, the 1st-order RC equivalent circuit model of normal battery and soft SC fault battery was established, and model parameters were identified using Recursive Least Squares with Forgetting Factor (FFRLS). Then, using the improved EKF, the state of charge (SOC) of a single cell was estimated, and the difference between the calculated SOC and the estimated SOC by the coulomb counting method was used to detect soft SC faults and compared them with the reference data. Finally, the SC resistance value indicated the severity of the fault. Results: The proposed method could accurately diagnose the soft short circuit fault, and the error was found to be lower than the traditional EKF algorithm. The estimation error was about 0.4% for the battery with slight failure and about 1.5% for the battery with serious failure. Conclusion: The experimental results showed that the improved EKF algorithm could estimate the SOC difference more accurately, and the effect of soft SC fault diagnosis was better. At the same time, it could quantitatively identify the size of the short circuit resistance, which is very helpful for the subsequent management of the battery system.

A novel hybrid machine learning coulomb counting technique for state of charge estimation of lithium-ion batteries

Accurate state of charge (SOC) estimation is crucial to ensure safe and reliable operation of battery systems. However, the capacity of an operating battery is difficult to measure, and some existing SOC estimation methods either rely too much on the established battery physical model, such as the Kalman filter and particle filter algorithm, or the number of training samples required is too large, and the online estimation performance is poor, such as neural network and Gaussian regression algorithm, cannot achieve good online estimation results. To solve these problems, this paper proposes a novel hybrid SOC estimation method, movIRVM-Coulomb, which integrates moving mean, incremental learning, relevance vector machine (movIRVM), and coulomb counting method. The hybrid algorithm uses the movIRVM algorithm and Coulomb counting method to estimate SOC alternately so that the estimated value is kept in the accuracy of the first estimation, which avoids the error accumulation problem when using a single algorithm to predict multiple times and improves the generalization performance of the algorithm. Firstly, the offline estimation model based on the RVM algorithm and the online estimation model based on the incremental learning RVM(IRVM) algorithm are established by using only 10 % to 30 % training data approximately. Secondly, aiming at the problem that the estimation result of the RVM algorithm fluctuates violently, this paper combines the moving average strategy to make the geometric average of SOC value by setting a reasonable moving window. This method further improves the estimation accuracy, fitting ability and generalization performance of the RVM algorithm. Finally, jointing the coulomb counting method to further improve the online estimation accuracy. The effectiveness of the proposed algorithm is verified by a single-cell public data set, battery pack experimental test data set and Advisor simulation data set. The experimental results highlight that the estimation accuracy of the movIRVM-Coulomb algorithm is >30 % improved compared with that of the movIRVM algorithm and its root means square error can be restricted within 2 % in a wide temperature range and variable dynamic conditions, including operating conditions of US06, UDDS, NYCC and 1015. Overall, it is manifesting that the movIRVM-Coulomb algorithm requires only a few training samples to have high estimation accuracy, strong generalization ability, and excellent robustness.

End-Cloud Collaboration Approach for State-of-Charge Estimation in Lithium Batteries Using CNN-LSTM and UKF

The accurate estimation of the state of charge (SOC) plays a crucial role in ensuring the range of electric vehicles (EVs) and the reliability of the EVs battery. However, due to the dynamic working conditions in the implementation of EVs and the limitation of the onboard BMS computational force, it is challenging to achieve a reliable, high-accuracy and real-time online battery SOC estimation under diverse working scenarios. Therefore, this study proposes an end-cloud collaboration approach of lithium-ion batteries online estimate SOC. On the cloud-side, a deep learning model constructed based on CNN-LSTM is deployed, and on the end-side, the coulomb counting method and Kalman’s filter are deployed. The estimation results at both sides are fused through the Kalman filtering algorithm, realizing high-accuracy and real-time online estimation of SOC. The proposed approach is evaluated with three dynamic driving profiles and the results demonstrate the proposed approach has high accuracy under different temperatures and initial errors, where the root means square error (RMSE) is lower than 1.5% and the maximum error is lower than 5%. Furthermore, this method could achieve high-accuracy and real-time SOC online estimation under the cyber hierarchy and interactional network (CHAIN) framework and can be extended to multi-state collaborative online estimation.

An improved state of charge estimation of lithium-ion battery based on a dual input model

ABSTRACT The state of charge (SOC) is critical for the safety and reliable utilization of the battery. However, SOC estimation accuracy of lithium iron phosphate (LiFePO4) battery is a challenging task because of its flat voltage plateau and its performance is affected greatly by temperature. An improved open circuit voltage (OCV) combined with coulomb counting (CC) method based on the dual input model of ambient temperature and OCV is proposed. This approach is employed to obtain the initial value of SOC based on the dual input model over a wide range of temperature, and to establish SOC estimation model of modified CC with Simulink. The temperature entropy coefficient and differential analysis has been analyzed under different SOC conditions. The SOC maximum errors predicted by the proposed OCV-CC method maintain within 3.10%, while the maximum errors of the traditional OCV-CC increase from 4.78% to 23.64% under the temperature of 35°C ~−15°C. The results show the estimation accuracy of SOC using the proposed OCV-CC method is improved obviously, especially under low temperature.

Enhanced Coulomb Counting Method for SoC and SoH Estimation Based on Coulombic Efficiency

The battery performance decreases as the charging/discharging cycles increase. Thus, a battery management system (BMS) is essential to properly estimating the battery states. In order to enhance the performance of the BMS, an accurate estimation method for lithium-ion batteries state is proposed. The main drawback of the coulomb counting method (CCM) for estimating a state of charge (SoC) is the error of initial value. To make-up this problem, the open circuit voltage (OCV) method which includes the internal resistance of the battery has been applied to update the initial value. In this paper, an enhanced coulomb counting (ECC) method is proposed to improve the accuracy of SoC estimation. Due to the battery aging by repeated charging/discharging cycles, the charging/discharging times become reduced and it can be formulated as a function of coulombic efficiency. Using the power equation to the battery, the state of health (SoH) can be estimated according to the change in the internal resistance. In the proposed flowchart, after the completion of charging/discharging in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> cycle, the internal resistance, coulombic efficiency, and capacities are calculated and those resultants will be utilized in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> + <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</i> cycle. The proposed methods are verified by 3 kW energy storage system and the comparative experiment results are also presented to point out its effectiveness.