Proposed State Of Charge Estimation Method Research Articles

Whale Optimization Algorithm BP Neural Network with Chaotic Mapping Improving for SOC Estimation of LMFP Battery

The state of charge (SOC) is a core parameter in the battery management system for LMFP batteries. Accurate SOC estimation is crucial for ensuring the safety and reliability of energy storage applications and new energy vehicles. In order to achieve better SOC estimation accuracy, this article proposes an adaptive whale optimization algorithm (WOA) with chaotic mapping to improve the BP neural network (BPNN) model. The SOC estimation accuracy of the BPNN model was improved by utilizing WOA to find the optimal target weight values and thresholds. Comparative simulation experiments (including constant current and working condition discharge experiments) were conducted in Matlab/Simulink R2018a to verify the proposed algorithm and the other four algorithms. The experimental results show that the proposed algorithm had higher SOC estimation accuracy than the other four algorithms, and its prediction errors were less than 1%. This indicates that the proposed SOC estimation method has better prediction accuracy and stability, and has certain theoretical research significance.

Robust state-of-charge estimation for LiFePO4 batteries under wide varying temperature environments

During the driving process of electric vehicles, the ambient temperature exhibits diverse variations with regional characteristics. To achieve robust state of charge (SOC) estimation for lithium-ion batteries under various varying temperature environments, this paper proposes an enhanced model-based closed-loop SOC estimation approach. First, beginning with a mechanistic analysis of batteries, the traditional second-order equivalent circuit model is enhanced by incorporating critical solid-phase diffusion effects during battery operation. Furthermore, utilizing data collected from multiple constant temperature environments, the complete enhanced battery model that accounts for the influence of current rates across a wide temperature range is constructed. Subsequently, under environments of different varying temperature settings, we design a series of complex operation experiments to verify the accuracy and generalizability of the established battery model. Meanwhile, a high-performance adaptive diagonalization of matrix cubature Kalman filter is introduced to address the challenge of fluctuating sampling noises in battery operation. Finally, the robustness and generalization of the proposed SOC estimation method are verified in multiple complex operating experiments under varying temperatures with non-Gaussian noise interferences and with non-full charging schemes. Remarkably, the proposed approach consistently delivers high-precision SOC estimation results across all scenarios, maintaining root mean square error and mean absolute error below 1.5%.

State-of-Charge Estimation of Nickel–Cadmium Batteries Based on Dynamic Modeling of Electrical Characteristics and Adaptive Untrace Kalman Filtering

With the increasing demand for intelligence and automation, and the continuous strengthening of safety and efficiency requirements, the disadvantages of traditional “blind use” of nickel–cadmium batteries have become increasingly prominent, and the lack of state-of-charge (SOC) estimation needs to be changed urgently. For this purpose, a dynamic model of nickel–cadmium battery is established, and an SOC estimation method of nickel–cadmium battery based on adaptive untraced Kalman filter is proposed. Firstly, the experimental platform was built, and the open-circuit voltage and polarization characteristics of nickel–cadmium batteries were analyzed. On this basis, an equivalent circuit model is constructed to reflect the characteristics of nickel–cadmium batteries, and the model parameters were identified by the hybrid pulse power characteristic test; Then, based on the dynamic model, the SOC of the nickel–cadmium battery was estimated by combining with the Sage–Husa adaptive untrace Kalman filtering algorithm. Finally, the SOC estimation effect was verified under two operating conditions: Hybrid pulse power characteristic (HPPC) and constant cyclic charging and discharging power. The experimental results show that the proposed estimation method is insensitive to the initial value of SOC, and can still converge to the real value even if there is 30% error in the initial value. The mean absolute error and root mean square deviation of the final SOC estimation results are both less than 1%. The dynamic model and the proposed SOC estimation method provide valuable reference for the operation control, maintenance, and replacement of nickel–cadmium batteries in the use process.

State of charge estimation of lithium-ion battery based on improved adaptive boosting algorithm

State of charge (SOC) is a crucial battery state to calibrate the remaining charge of lithium-ion batteries in electric vehicles. Accurate battery SOC estimation is one of the keys and difficult issues for lithium-ion batteries. To achieve SOC estimation accurately, an improved adaptive boosting algorithm called AdaBoost.RT algorithm, which is a regression ensemble algorithm and uses the extreme learning machine (ELM) as weak learners, is applied to estimate the SOC of lithium-ion batteries in this paper. And a two-step method is also proposed to denoise experimental data, which are used as the input data of the proposed SOC estimation method, to further enhance the accuracy of SOC estimation. Firstly, a complementary ensemble empirical mode decomposition with adaptive noise method (CEEMDAN) is utilized to decompose the experimental data including battery voltage and current according to the frequency of the signals to form different frequency components. Secondly, a wavelet threshold denoising method (WTD) is combined to denoise these components. The denoised components trend to form a new battery signal by the linear superposition principle and input into the proposed SOC method in this paper. The combined SOC estimation method can be called CEEMDAN-WTD-AdaBoost-ELM (CWAELM) method. To verify the proposed method, experiments are conducted under Beijing dynamic stress test (BJDST), dynamic stress test (DST), federal urban driving schedule (FUDS), and highway driving schedule (US06) conditions at 0 °C, 25 °C and 45 °C temperatures, respectively. The results show that the proposed model has good robustness and accuracy in the SOC estimation of lithium-ion batteries under various working conditions at different temperatures.

OCV-SOC-Temperature Relationship Construction and State of Charge Estimation for a Series– Parallel Lithium-Ion Battery Pack

Accurate state of charge (SOC) estimation of a battery pack is more meaningful than that of a cell in practical applications. The existing methods are difficult to provide an accurate SOC of a battery pack under a wide range of temperature due to cell inconsistency. In this paper, a SOC estimation method for a series-parallel lithium-ion battery pack based on the newly constructed OCV-SOC-temperature relationship was proposed. In the proposed method, firstly the cosine similarity is used to quantify cell inconsistency and the representative cells in the battery pack are determined by the largest cosine similarity value. Secondly, the OCV-SOC curves of the representative cells at different temperatures are used to construct the OCV-SOC-temperature relationship of the battery pack. Finally, a multi-branch fusion method is developed to estimate the SOC of the battery pack through the SOC of each branch by means of a Bayesian probability formula. The effectiveness of the newly constructed OCV-SOC-temperature relationship and the proposed SOC estimation method of the battery pack is verified under various dynamic conditions. The estimated SOC error of the series–parallel battery pack is less than 1.5%, which is approximately 60% lower than the traditional methods.

State-of-Charge Estimation of Ultracapacitor Based on H Infinity Filter Considering Variable Temperature

Abstract The performance and service life of ultracapacitors are highly dependent on accurate modeling and state-of-charge (SOC) estimating. To overcome the model parameter errors caused by the various temperatures and different SOC intervals, the H infinity filter (HIF) is employed to estimate the ultracapacitor SOC based on a variable temperature model. For the application of the HIF method, the Thevenin model is first developed with a small terminal voltage estimation error. Then, the model parameters are optimally identified using the ant colony optimization (ACO) algorithm. Next, a variable temperature model is established to improve the adaptability of the ultracapacitor model, and the HIF is utilized for the ultracapacitor SOC estimation. Finally, to verify the performance of the variable temperature model and the proposed SOC estimation method, a series of experiments are conducted. The analysis results illustrate that the mean absolute error (MAE) of the SOC estimation values based on the variable temperature model is decreased by 39.62% compared to the one based on the nonvariable temperature model. Meanwhile, the proposed state estimation scheme based on the variable temperature model is accurate with estimation values maximum error (ME) and root-mean-squared error (RMSE) less than 0.80% and 0.60%, respectively. The HIF-based SOC estimation method also shows a good robustness with a short convergence time within 90.00 s when the SOC initial error is set to 0.20.

State of charge estimation of lithium-ion battery based on improved Hausdorff gradient using wavelet neural networks

In this paper, a parameter optimization algorithm via the wavelet neural network (WNN) based on fractal derivative is proposed to estimate the state of charge (SOC) from the measured current and voltage data for lithium-ion batteries. The improved Hausdorff derivative is applied to the parameter tuning of a WNN to improve the performance of the SOC estimation. The local property of the improved Hausdorff derivative is used to obtain the chain rule which is consistent with the integer-order derivative, and the parameter tuning formulas in WNN based on the improved Hausdorff gradient are obtained. The adaptive order tuning method is proposed and applied to optimize the parameter tuning. Compared with the integer-order optimization method, this method speeds up the optimization of the parameters and improves the optimization accuracy of SOC estimation. The experiment results verify the effectiveness of the proposed SOC estimation method via the WNN based on fractal derivative.

State of charge estimation at different temperatures based on dynamic thermal model for lithium-ion batteries

In the existing modeling and state of charge (SOC) estimation of lithium batteries, the effect of ambient temperature is often ignored, which brings large errors to the model parameter identification, so the battery modeling and SOC estimation under temperature variation are performed. In this paper, an improved dual-polarized dynamic thermal model is proposed, firstly, the thermal effect of lithium battery is analyzed, and the model parameters adopt the dynamic values of coupling temperature and SOC, and the model is improved in terms of both heat generation mechanism and heat transfer characteristics; then the model parameters are identified based on the least-squares method with variable forgetting factor (VFF-RLS). Finally, the SOC at different temperatures is evaluated based on the dynamic thermal model combined with the extended Kalman filtering (EKF) with nonlinear filtering capability. The results show that the temperature factor is an important and non-negligible influence factor in modeling, the proposed battery model in this paper has high computational accuracy, and the proposed SOC estimation method has better SOC estimation capability for the temperature variation problems.

State-of-charge estimation of lithium-ion battery pack by using an adaptive extended Kalman filter for electric vehicles

State-of-charge (SOC) estimation is an important aspect for modern battery management systems. Extended Kalman filter (EKF) has been extensively used in battery SOC estimation. However, EKF cannot obtain accurate estimation results when the model parameters have strong uncertainty or/and the accurate initial value of noise covariance matrix is unknown. To overcome these defects, the parameters of Lithium-ion battery model on the basis of the second-order resistor–capacitor (RC) equivalent model are identified, and then an improved adaptive EKF (IAEKF) of SOC estimation method for Lithium-ion battery pack is proposed for enhancing estimation accurate and robustness. In IAEKF, the statistical characteristics of measurement noise is adaptively corrected using a forgetting factor, namely, Sage–Husa EKF (SHEKF), and the error covariance matrix is adaptively corrected in accordance with the innovation, in which the calculation of the actual innovation covariance matrix adopts the variable sliding window length. Results of numerical simulation and experiment show that the proposed SOC estimation method can accurately estimate SOC under complex driven condition and has strong robustness to the uncertainty of model parameters and the initial value of the noise covariance matrix.

A Method of State-of-Charge Estimation for EV Power Lithium-Ion Battery Using a Novel Adaptive Extended Kalman Filter

Battery management system (BMS) is one of the key subsystems of electric vehicle, and the battery state-of -charge (SOC) is a crucial input for the calculations of energy and power. Therefore, SOC estimation is a significant task for BMS. In this paper, a new method for online estimating SOC is proposed, which combines a novel adaptive extended Kalman filter (AEKF) and a parameter identification algorithm based on adaptive recursive least squares (RLS). Specifically, according to the first order R-C network equivalent circuit model, the battery model parameters are identified online using the RLS with multiple forgetting factors. Based on the identified parameters, the novel AEKF is used to accurately estimate the battery SOC. The online identification of parameter tracks the varying model. At the same time, due to the novel AEKF algorithm to dynamically adjust the system noise parameter, excellent accuracy of the SOC real-time estimation is obtained. Experiments are set to evaluate the accuracy and robustness of the proposed SOC estimation method. The simulation test results indicate that under DST and UDDS conditions, the maximum absolute errors are less than 0.015 after filtering convergence. In addition, the maximum absolute error is less than 0.02 in the simulation of DST with current and voltage measurement noise, so is in DST with current offset sensor error. The tests indicate that the proposed method can accurately estimate battery SOC and has strong robustness.

A Novel Fractional Order Model for State of Charge Estimation in Lithium Ion Batteries

Battery models are the cornerstone to battery state of charge (SOC) estimation and battery management systems in electric vehicles. This paper proposes a novel fractional-order model for a battery, which considers both Butler–Volmer equation and fractional calculus of constant phase element. The structure characteristics of the proposed model are then analyzed, and a novel identification method, which combines least squares and nonlinear optimization algorithm, is proposed. The method is proven to be efficient and accurate. Based on the proposed model, a fractional-order unscented Kalman filter is developed to estimate SOC, while singular value decomposition is applied to tackle the nonlinearity of Butler–Volmer equation and fractional calculus of constant phase element. The systematic comparison between the proposed model and traditional fractional order model is carried out on two LiNiMnCo lithium-ion batteries at different temperatures, ageing levels, and electric vehicle current profiles. The comparison results show that the proposed model has higher estimation accuracy in battery terminal voltage and SOC than the traditional model over wide range of temperature and ageing level under electric vehicle operation conditions. Furthermore, the hardware-in-the-loop test validates that the proposed SOC estimation method is suitable for SOC estimation in electric vehicles.

A Novel Adaptive SOC Estimation Method for a Series-connectedLithium-ion Battery Pack Under Fast-varying Environment Temperature

This paper proposes a model-based state-of-charge (SOC) estimation method for a series-connected battery pack under fast-varying environment temperature. For accurately evaluating the SOC of the battery pack equipped with passive balance control, the relationship of battery pack and in-pack cell in the available capacity is analyzed. A filtering process is applied to select the alterable reference cell (ARC) for supporting the modeling framework of SOC estimation. An adaptive SOC estimator is presented by using an optimized recursive least squares algorithm to identify all cells parameters, and using an adaptive extended Kalman filter algorithm (AEKF) to estimate the pack generalized SOC in real-time. Furthermore, a bias correction approach is developed to compensate the cell available capacity at low temperature based on the cell resistance of off-line identification and the open-circuit voltage (OCV) of on-line estimation. The experimental verification is conducted through the modified Federal Urban Driving Schedule (FUDS) cycles with environment temperature varying from 25°C to -35°C. Results show that the accuracy of the proposed SOC estimation method is considerably high, and the correction approach can compensate the cell available capacity effectively with acceptable error.

A Novel State-of-Charge Estimation Method of Lithium-Ion Batteries Combining the Grey Model and Genetic Algorithms

In order to guarantee safe and reliable operation of electric vehicle batteries and to optimize their energy and capacity utilization, it is indispensable to estimate their state-of-charge (SoC). This study aimed to develop a novel estimation approach based on the grey model (GM) and genetic algorithms without the need of a high-fidelity battery model demanding high computation power. A SoC analytical model was established using the grey system theory based on a limited amount of incomplete data in contrast with conventional methods. The model was further improved by applying a sliding window mechanism to adjust the model parameters according to the evolving operating status and conditions. In addition, the genetic algorithms were introduced to identify the optimal adjustment coefficient λ in a traditional grey model (1, 1) model to further improve the source estimation accuracy. For experimental verification, two types of lithium-ion batteries were used as the device-under-test that underwent typical passenger car driving cycles. The proposed SoC estimation method were verified under diverse battery discharging conditions and it demonstrated superior accuracy and repeatability compared to the benchmarking GM method.

State of Charge Estimation of Lithium-Ion Batteries Over Wide Temperature Range Using Unscented Kalman Filter

With the development of electric vehicles in recent years, lithium-ion batteries have been widely used. Accurate state of charge (SOC) estimation plays an important role in the safety of electric vehicles. Since the temperature has the significant influence on charge and discharge performance of the battery, it is critical to achieve accurate SOC estimation over the wide temperature range. In this paper, a polymer ternary lithium-ion battery is focused, and a Thevenin equivalent circuit model with temperature compensation is established. The validity of the established battery model was verified by the dynamic stress test. On this basis, the ternary lithium-ion battery SOC was estimated using the unscented Kalman filter (UKF). The New European Driving Cycle is used to verify the effectiveness of the proposed algorithm. The simulation and experimental results show that the established Thevenin equivalent circuit model with temperature compensation can accurately represent the battery dynamics. Based on this model, the SOC was estimated using the UKF and the maximum errors are within 3%. Therefore, the proposed SOC estimation method is verified to be effective and robust.

A Novel Online SOC Estimation Method for the Power Lithium Battery Pack Based on the Unscented Kalman Filter

The SOC (State Of Charge) estimation is a core aspect for the associated BMS (Battery Management System) equipment of the lithium battery pack, in which the KF (Kalman Filter) -based methods have been extensively used but suffers from the linearization accuracy drawbacks. A novel UKF (Unscented Kalman Filter) estimation method battery model is proposed for the SOC estimation of the lithium battery pack, in which the linearization treatment is not required and fewer Sigma data points are used, reducing the computational requirement of the SOC estimation. The UKF method improves the SOC covariance properties for the lithium battery pack, the estimation performance of which has been validated by the experimental results. The proposed SOC estimation method has a RMSE (Root Mean Square Error) value of 1.42%, playing an important role in the popularization and application of the lithium battery pack.

Design of a Bidirectional Energy Storage System for a Vanadium Redox Flow Battery in a Microgrid with SOC Estimation

This paper used a Vanadium Redox flow Battery (VRB) as the storage battery and designed a two-stage topology of a VRB energy storage system in which a phase-shifted full bridge dc-dc converter and three-phase inverter were used, considering the low terminal voltage of the VRB. Following this, a model of the VRB was simplified, according to the operational characteristics of the VRB in this designed topology of a VRB energy storage system (ESS). By using the simplified equivalent model of the VRB, the control parameters of the ESS were designed. For effectively estimating the state of charge (SOC) of the VRB, a traditional method for providing the SOC estimation was simplified, and a simple and effective SOC estimation method was proposed in this paper. Finally, to illustrate the proper design of the VRB ESS and the proposed SOC estimation method, a corresponding simulation was designed by Simulink. The test results have demonstrated that this proposed SOC estimation method is feasible and effective for indicating the SOC of a VRB and the proper design of this VRB ESS is very reasonable for VRB applications.

A new state-of-charge estimation method for electric vehicle lithium-ion batteries based on multiple input parameter fitting model

Summary The estimation of state-of-charge (SOC) is crucial to determine the remaining capacity of the Lithium-Ion battery, and thus plays an important role in many electric vehicle control and energy storage management problems. The accuracy of the estimated SOC depends mostly on the accuracy of the battery model, which is mainly affected by factors like temperature, State of Health (SOH), and chemical reactions. Also many characteristic parameters of the battery cell, such as the output voltage, the internal resistance and so on, have close relations with SOC. Battery models are often identified by a large amount of experiments under different SOCs and temperatures. To resolve this difficulty and also improve modeling accuracy, a multiple input parameter fitting model of the Lithium-Ion battery and the factors that would affect the accuracy of the battery model are derived from the Nernst equation in this paper. Statistics theory is applied to obtain a more accurate battery model while using less measurement data. The relevant parameters can be calculated by data fitting through measurement on factors like continuously changing temperatures. From the obtained battery model, Extended Kalman Filter algorithm is applied to estimate the SOC. Finally, simulation and experimental results are given to illustrate the advantage of the proposed SOC estimation method. It is found that the proposed SOC estimation method always satisfies the precision requirement in the relevant Standards under different environmental temperatures. Particularly, the SOC estimation accuracy can be improved by 14% under low temperatures below 0 °C compared with existing methods. Copyright © 2017 John Wiley & Sons, Ltd.

Implementation of Unscented Kalman Filter-Based Online State-of-Charge Estimation in LiFePO4 Battery-Powered Electric Vehicle Applications

This paper proposes an online state-of-charge (SoC) estimation method based on a 2nd-order RC model. The open circuit voltage (OCV) of the battery is calculated through two adaptive filtering stage, which is then used to determine the SoC via a dynamic OCV–SoC curve. In the first stage a variable step-size least mean square (VSS-LMS) algorithm is employed to adaptively estimate the model parameters in real-time; in the second stage, an unscented Kalman filter (UKF) is used to estimate the OCV from the parameters. The proposed SoC estimation method with its simplified model was implemented in DSP system for real-time application. Plenty of experiments under different conditions were carried out to confirm the effectiveness and superiority of the proposed system.

Online state of charge estimation for the aerial lithium-ion battery packs based on the improved extended Kalman filter method

An effective method to estimate the integrated state of charge (SOC) value for the lithium-ion battery (LIB) pack is proposed, because of its capacity state estimation needs in the high-power energy supply applications, which is calculated by using the improved extended Kalman filter (EKF) method together with the one order equivalent circuit model (ECM) to evaluate its remaining available power state. It is realized by the comprehensive estimation together with the discharging and charging maintenance (DCM) process, implying an accurate remaining power estimation with low computational calculation demand. The battery maintenance and test system (BMTS) equipment for the aerial LIB pack is developed, which is based on the proposed SOC estimation method. Experimental results show that, it can estimate SOC value of the LIB pack effectively. The BMTS equipment has the advantages of high detection accuracy and stability and can guarantee its power-supply reliability. The SOC estimation method is realized on it, the results of which are compared with the conventional SOC estimation method. The estimation has been done with an accuracy rate of 95% and has an absolute root mean square error (RMSE) of 1.33% and an absolute maximum error of 4.95%. This novel method can provide reliable technical support for the LIB power supply application, which plays a core role in promoting its power supply applications.

A method for SOC estimation based on simplified mechanistic model for LiFePO4 battery

Accurate battery state of charge (SOC) estimation conduces to establishing an ideal charging and discharging strategy for battery management system (BMS). And it can also prevent serious damage to battery (pack) from over-charging or over-discharging. This paper firstly establishes a simplified mechanistic model for LiFePO4 battery. Parameter identification conditions are originally designed based on excitation response analysis and essential verifications in terms of model accuracy are conducted. The functional relationship between mechanistic parameters and battery SOC is then established. And lastly, SOC estimation method based on the proposed model is presented. The contributions of this paper are listed as follows: (i) the proposed mechanistic model with obtained parameters can accurately describe LiFePO4 battery behaviors and predict discharge capacity under different operating conditions, (ii) the proposed SOC estimation method based on the battery model has certain applicability and robustness. Analysis and assessment of accuracy of the proposed method indicate that SOC estimation accuracy is acceptable. With the improvement of model simulation, SOC estimation accuracy can be further refined.