Implementation of EKF combined with discrete wavelet transform-based MRA for improved SOC estimation for a Li-Ion cell

The electrification of the transport sector increases the demand for a battery management system that allows the Li-ion battery to work in a sophisticated way to reach its maximum. The main focus of the battery management system is the estimation of the battery’s state of charge (SOC) which is an indicator to determine the driving range of an electric vehicle. However, battery modeling has a direct influence on SOC estimation. In this article, the equivalent circuit model (ECM) and extended Kalman filter (EKF) algorithm are combined for SOC estimation of the Li-ion battery. Since the performance of Kalman filter algorithms strictly depends on noise covariance matrices, incorrect values reduce the accuracy and make divergence in state estimation. Therefore, it is motivated to analyze the impact of process noise and measurement noise covariance matrix values on SOC estimation using Kalman filter algorithms. Ten different combinations of noise covariance matrices are employed to analyze the effect of these matrices on SOC estimation. As a result, the tuning of the filter is critical for improvement in state estimation regardless of the type of the battery model and filtering technique. The matrix values are chosen to take into account the convergence rate and estimation error. To validate the effectiveness of the chosen noise covariance matrices, the simulation results of the ECM based EKF estimation method is superimposed with the discharge curves available in the data sheet. The simulation results show minimal estimation error and a good convergence rate for tuned EKF.

An adaptive Kalman filter algorithm is adopted to estimate the state of charge (SOC) of a lithium-ion battery for application in electric vehicles (EVs). Generally, the Kalman filter algorithm is selected to dynamically estimate the SOC. However, it easily causes divergence due to the uncertainty of the battery model and system noise. To obtain a better convergent and robust result, an adaptive Kalman filter algorithm that can greatly improve the dependence of the traditional filter algorithm on the battery model is employed. In this paper, the typical characteristics of the lithium-ion battery are analyzed by experiment, such as hysteresis, polarization, Coulomb efficiency, etc. In addition, an improved Thevenin battery model is achieved by adding an extra <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RC</i> branch to the Thevenin model, and model parameters are identified by using the extended Kalman filter (EKF) algorithm. Further, an adaptive EKF (AEKF) algorithm is adopted to the SOC estimation of the lithium-ion battery. Finally, the proposed method is evaluated by experiments with federal urban driving schedules. The proposed SOC estimation using AEKF is more accurate and reliable than that using EKF. The comparison shows that the maximum SOC estimation error decreases from 14.96% to 2.54% and that the mean SOC estimation error reduces from 3.19% to 1.06%.

A FPGA implementation for a model‐based state of charge (SOC) estimation is described in this paper. A Thevenin equivalent circuit model is designed for SOC estimation. The extended Kalman filter (EKF) is designed to complete the SOC estimation, and the error is within 1 % . The FPGA is chosen to achieve realtime SOC estimation. A fast matrix method is proposed to improve the calculation speed of the EKF in FPGA because the EKF algorithm requires many matrix operations. In addition, the embedded system based on the FPGA with a system on a programmable chip (SOPC) technique is built using the Qsys platform in Quartus II. Based on the embedded system, an online testing platform is established to monitor the terminal voltage and load current of the experimental battery in real time; experimental results show that the online SOC estimation is successful. The measurement results show that the FPGA embedded scheme of the EKF allows for successful implementation of the SOC estimation with accuracy and speed. The fast matrix method requires 0.00007 s to implement the SOC estimation and is four times faster than the conventional matrix method.

A comparative study on state-of-charge estimation for lithium-rich manganese-based battery based on Bayesian filtering and machine learning methods

State of Charge (SOC) estimation plays a crucial role in managing the performance and longevity of electric vehicle (EV) batteries. Various estimation techniques, including various Kalman Filters (KF), have been employed for this purpose. The mathematical process and limits of different KF family algorithms are analysed. In this study, an impact of nonlinear KF parameters on SOC estimation accuracy for EV batteries is analyzed. Specifically, the performance of Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Particle Filter (PF) is examined. EKF is most preferred for SOC estimation whereas introducing sigma points in UKF is the basic difference between EKF and UKF. In comparison to EKF and UKF, PF is more flexible since it applies to nonlinear systems with non-Gaussian distributions. Utilising a sequence of noisy measurements, KF is a recursive method that approximates the state of a dynamic system. It requires accurate parameters such as process noise covariance (Q) and measurement noise covariance (R) to work effectively. In this work, the effect of these parameters on state of charge estimation using nonlinear KFs has been analysed under different settings of noise covariance matrices. Tuning these parameters is essential for optimizing the performance of the KF and ensuring accurate and reliable state estimation in real-world applications. To find parameter values that provide the best balance between accuracy, robustness, and computational efficiency, the tuning process involves experimentation, simulation, and validation using real-world data. According to the results observed, the filter parameters such as three diagonal elements of Q are selected as 2e-8, 0.005, 0.003 and R as 2e-6. By evaluating the state estimation accuracy under different driving scenarios, robustness and sensitivity of filtering technique in estimating SOC for EV batteries has been studied. This analysis contributes to the optimization of SOC estimation algorithms, enhancing the reliability and efficiency of EV battery management systems.

Nowadays, the development of technology which involves multimedia data is widely used to help better understanding in spreading information. Image is known as 2D signal which contain huge data especially a high resolution image. This paper shows the comparison of applying lossy and lossless compression on the image data. Image compression is necessary in reducing the size of image for storage or transmission purpose to support most of the application nowadays. The technique applied in this paper is the hybrid of Discrete Wavelet Transform (DWT) technique and Huffman coding technique which are classified as lossy and lossless compression, respectively. The performance of image compression are evaluated in terms of compression ratio, Mean Square Error (MSE), Power Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM) and computing time. Several types of evaluation can determine better technique to apply on specific type of application. The stand-alone of each DWT and Huffman technique are evaluated before applying hybrid of DWT and Huffman technique. After conducting a comprehensive observation, the hybrid technique can compress with ratio about 1:17 to 1:27 due to the support from DWT technique that apply filter concept. The MSE value is high with the average about 69 which contributes to low PSNR value with about 29 to 30 dB due to the relation of PSNR equation with MSE value. Besides, the SSIM value is 0.6 or about 40% far from the original image that affect the output image. Despite of that, the computing time is fast with about 3 to 4 seconds which has been improved from stand-alone Huffman technique. Therefore, hybrid compression is capable of supporting each other techniques in stand-alone technique.

This paper proposed a battery state of charge (SOC) estimation methodology utilizing the Extended Kalman Filter. First, Extended Kalman Filter for Li-ion battery SOC was mathematically designed. Next, simulation models were developed in MATLAB/Simulink, which indicated that the battery SOC estimation with Extended Kalman filter is much more accurate than that from Coulomb Counting method. This is coincident with the mathematical analysis. At the end, a test bench with Lithium-Ion batteries was set up to experimentally verify the theoretical analysis and simulation. Experimental results showed that the average SOC estimation error using Extended Kalman Filter is <;1%.

An accurate battery State of Charge (SOC) estimation has great significance in improving battery life and vehicle performance. An improved second-order battery model is proposed in this paper through quantities of LiFePO4 battery experiments. The parameters of the model were acquired by the HPPC composite pulse condition under different temperature, charging and discharging rates, SOC. Based on the model, battery SOC is estimated by Extended Kalman Filter (EKF). Comparison of three different pulse conditions shows that the average error of SOC estimation of this algorithm is about 4.2%. The improved model is able to reflect the dynamic performance of batteries suitably, and the SOC estimation algorithm is provided with higher accuracy and better dynamic adaptability. DOI: http://dx.doi.org/10.11591/telkomnika.v11i8.2629

To meet practical usage requirements, lithium-ion batteries usually need to form a battery pack. However, due to production deviations and different usage environments, there are inconsistencies between batteries within the battery pack. This makes it challenging to estimate the state of charge (SOC) of the battery pack accurately. This article proposes a battery pack SOC estimation approach based on discharge stage division and fusion modeling. According to the battery discharge characteristics and SOC inconsistency, three stages are divided in the battery pack discharge process. In the first stage, the second-order RC model and extended Kalman filter (EKF) algorithm are employed for SOC estimation as the consistency between batteries is good. In the second stage, the representative battery and long short-term memory (LSTM) recurrent neural network (RNN) will be used to consider the impact of battery inconsistency on battery SOC estimation. An ampere-hour integration with a constraint factor for smoothing is applied to enhance the estimation accuracy of the LSTM network. In the third stage, EKF is utilized to estimate the SOC of all batteries as the inconsistency between batteries increases significantly and reaches a maximum at the end of the discharge. Finally, experiments and actual vehicle tests under the Worldwide Harmonized Light Vehicles Test Cycle (WLTC) working conditions are conducted to verify the effectiveness of proposed methods on the ternary lithium battery pack at <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0~^{\circ } $ </tex-math></inline-formula>C, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$25~^{\circ } $ </tex-math></inline-formula>C, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$45~^{\circ } $ </tex-math></inline-formula>C. Compared with other algorithms, the proposed method has good estimation performance, with a maximum RMSE of only 0.8%. The maximum prediction error under actual vehicle operating conditions is less than 1.2%.

State-of-Charge Estimation of VRLA Batteries Using Neural Networks and Extended Kalman Filter

Real-time state of charge estimation for electric vehicle power batteries using optimized filter

Lithium-ion (Li-ion) battery necessitates an accurate state of charge (SOC) estimation technique to control charging and discharging for the safety of electric vehicle (EV). SOC is an indicator to display the remaining capacity of the battery. To achieve an accurate SOC, model-based SOC estimation algorithm is developed in this paper. Due to the self-correction nature of Kalman filter (KF), KF family algorithm is most promising for SOC estimation when the system is running. As battery modelling has a direct impact on SOC estimation, an equivalent circuit model (ECM) is preferred due to its balance between accuracy and complexity. In this paper, a closed loop approach with the combination of ECM and extended Kalman filter (EKF) algorithm is implemented on Li-ion battery to estimate an accurate SOC. For ease of implementation, model parameters are considered as univariant of SOC. The obtained discharge characteristic curves at different C-rate are close matches with manufacturer’s catalogue. The simulation results show minimum estimation error and good convergence rate of EKF.KeywordsBattery management systemC-rateEquivalent circuit modelElectric Vehicle

With the application of high-capacity lithium iron phosphate (LiFePO4) batteries in electric vehicles and energy storage stations, it is essential to estimate battery real-time state for management in real operations. LiFePO4 batteries demonstrate differences in open circuit voltage (OCV) under different charge and discharge paths, indicating the hysteresis phenomenon of OCV, which is more evident under energy storage frequency regulation conditions. Previous battery models ignored the hysteresis characteristics in the energy storage frequency regulation conditions, causing low accuracy in the state of charge (SOC) estimation. To accurately estimate the SOC of LiFePO4 batteries, a hysteresis voltage reconstruction model is developed to analyze the hysteresis characteristics of LiFePO4 batteries under automotive dynamic conditions and energy storage frequency regulation conditions. The accuracy of the hysteresis model is compared with the basic first-order RC equivalent circuit model. Furthermore, the SOC estimation based on the extended Kalman filter (EKF) method is achieved. Results indicate that the hysteresis model exhibits better accuracy for the hysteresis features, with an error of less than 1.5%, which is more appropriate for SOC estimation under energy storage conditions.

Online state of charge estimation of LiFePO4 battery based on EKF-AUKF algorithm with reference compensation for estimation results

To accurately estimate the state of charge (SOC) of lithium-ion power batteries in the event of errors in the battery model or unknown external noise, an SOC estimation method based on the H-infinity filter (HIF) algorithm is proposed in this paper. Firstly, a fractional-order battery model based on a dual polarization equivalent circuit model is established. Then, the parameters of the fractional-order battery model are identified by the hybrid particle swarm optimization (HPSO) algorithm, based on a genetic crossover factor. Finally, the accuracy of the SOC estimation results of the lithium-ion batteries, using the HIF algorithm and extended Kalman filter (EKF) algorithm, are verified and compared under three conditions: uncertain measurement accuracy, uncertain SOC initial value, and uncertain application conditions. The simulation results show that the SOC estimation method based on HIF can ensure that the SOC estimation error value fluctuates within ±0.02 in any case, and is slightly affected by environmental and other factors. It provides a way to improve the accuracy of SOC estimation in a battery management system.