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Collaborative state estimation of lithium-ion battery based on multi-time scale low-pass filter forgetting factor recursive least squares - double extended Kalman filtering algorithm

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INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS 2022年第6期50卷 2108-2127页

作者： Long, Tao Wang, Shunli Cao, Wen Ren, Pu He, Mingfang Fernandez, Carlos Southwest Univ Sci & Technol Sch Informat Engn Mianyang 621010 Sichuan Peoples R China Robert Gordon Univ Sch Pharm & Life Sci Aberdeen Scotland

For the lithium battery management system and real-time safety monitoring, two issues are of great significance, namely, the ability to accurately update the model parameters in real time and to accurately estimate the state of charge and health. In this context, this thesis adopts the second-order RC equivalent circuit model and the forgetting factor recursive least squares - double extended Kalman filtering (FFRLS-DEKF) algorithm with multi-time scales and low-pass filter. forgetting factor recursive least squares is applied to conduct online parameter identification, and the traditional double extended Kalman filtering algorithm is optimized to evaluate the state of charge and model parameters in the micro-scale and macro-scale. In this way, the error caused by two different characteristics is reduced, and a low-pass filter is added to optimize the fluctuation problem of the estimated value of the model parameters. According to the experiment results, the maximum error between the model simulation value and the actual value of the terminal voltage is 0.0459 V. If the initial value of the state of charge deviates from the actual value, the maximum errors under BBDST and HPPC conditions record 0.0235 and 0.0048, respectively, the forgetting factor recursive least squares - double extended Kalman filtering algorithm with multi-time scales and low-pass filter is able to track the true value within 40 s. Furthermore, the lithium-ion battery state of health both reaches 98% under the two conditions. In summary, the experimental analysis shows that the algorithm helps reduce the influence of initial values on the results, thereby reducing error accumulation and improving the robustness.

关键词： collaborative state estimation double extended Kalman forgetting factor recursive least squares low pass filter multi-time scale second-order RC model

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Online Parameter Identification and State of Charge Estimation of Lithium-Ion Batteries Based on forgetting factor recursive least squares and Nonlinear Kalman Filter

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ENERGIES 2018年第1期11卷 3页

作者： Xia, Bizhong Lao, Zizhou Zhang, Ruifeng Tian, Yong Chen, Guanghao Sun, Zhen Wang, Wei Sun, Wei Lai, Yongzhi Wang, Mingwang Wang, Huawen Tsinghua Univ Grad Sch Shenzhen Shenzhen 518055 Peoples R China Sunwoda Elect Co Ltd Shenzhen 518108 Peoples R China

State of charge (SOC) estimation is the core of any battery management system. Most closed-loop SOC estimation algorithms are based on the equivalent circuit model with fixed parameters. However, the parameters of the equivalent circuit model will change as temperature or SOC changes, resulting in reduced SOC estimation accuracy. In this paper, two SOC estimation algorithms with online parameter identification are proposed to solve this problem based on forgetting factor recursive least squares (FFRLS) and nonlinear Kalman filter. The parameters of a Thevenin model are constantly updated by FFRLS. The nonlinear Kalman filter is used to perform the recursive operation to estimate SOC. Experiments in variable temperature environments verify the effectiveness of the proposed algorithms. A combination of four driving cycles is loaded on lithium-ion batteries to test the adaptability of the approaches to different working conditions. Under certain conditions, the average error of the SOC estimation dropped from 5.6% to 1.1% after adding the online parameters identification, showing that the estimation accuracy of proposed algorithms is greatly improved. Besides, simulated measurement noise is added to the test data to prove the robustness of the algorithms.

关键词： forgetting factor recursive least squares nonlinear Kalman filter state of charge estimation online parameter identification lithium-ion battery variable temperature

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State of energy estimation for lithium-ion batteries using adaptive fuzzy control and forgetting factor recursive least squares combined with AEKF considering temperature

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JOURNAL OF ENERGY STORAGE 2023年 70卷

作者： Liu, Donglei Wang, Shunli Fan, Yongcun Liang, Yawen Fernandez, Carlos Stroe, Daniel-Ioan Southwest Univ Sci & Technol Sch Informat Engn Mianyang 621010 Peoples R China Sichuan Univ Coll Elect Engn Chengdu 610065 Peoples R China Aalborg Univ Dept Energy Technol Pontoppidanstr 111 DK-9220 Aalborg Denmark Robert Gordon Univ Sch Pharm & Life Sci Aberdeen AB10 7GJ Scotland

As the main energy storage component of electric vehicles (EV), lithium-ion battery state estimation is an essential part of the battery management system (BMS). State of Energy (SOE) is one of the important state parameters, and its accurate estimation effectively reduces the potential safety hazards in the use of lithium-ion batteries, improves the efficiency of energy utilization, and alleviates the mileage anxiety of drivers. To solve the problem that the prediction of SOE of lithium-ion batteries is greatly influenced by temperature, a novel method called adaptive fuzzy control forgetting factor recursive least squares-Adaptive extended Kalman filtering (AFCFFRLS-AEKF) is formed. A fuzzy logic controller is designed for adaptive adjustment of the online parameter recognition forgetting factor with the change of working conditions. To solve the problem that the open-circuit voltage (OCV) changes with the influence of temperature in the variable temperature range, the regression analysis method is used in modeling to realize the regression analysis of OCV in a wide temperature range. Estimation accuracy is verified under two working conditions. The error of the estimation considering the temperature effect converges within 1 %, which achieves higher estimation accuracy and stronger robustness.

关键词： forgetting factor recursive least squares Adaptive fuzzy control Adaptive extended Kalman filtering Lithium-ion batteries State of energy

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Hardware-in-the-Loop simulation for online identification of lithium-ion battery model parameters and state of charge estimation

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RESULTS IN ENGINEERING 2025年 25卷

作者： Le, Quoc Dan Lam, Quoc Minh Huynh, Minh Nhat Nguyen, Huy Hung Duong, Van Tu Vietnam Acad Sci & Technol Inst Appl Mech & Informat Hanoi Vietnam Sai Gon Univ Fac Elect & Telecommun Ho Chi Minh City Vietnam Ho Chi Minh City Univ Technol HCMUT Fac Mech Engn Key Lab Digital Control & Syst Engn DCSELab 268 Ly Thuong Kiet StDist 10 Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Ho Chi Minh City Vietnam

Accurately estimating the state of charge (SOC) of Lithium batteries remains a major challenge due to their nonlinear behavior, especially when the initial SOC value significantly deviates from the actual one. To address this issue, this study introduces a new algorithm to estimate the battery SOC based on adaptive unscented Kalman filter (AUKF). The modified AUKF (mAUKF) begins by constructing a second-order Thevenin model of the batteries through the forgetting factor recursive least squares (FFRLS) method, and then implements the novel approach for SOC estimation. The proposed method retains the strengths of the AUKF while dynamically adjusting the process and measurement noise covariance to improve SOC estimation robustness and accuracy. The effectiveness of the method is validated using both simulation and experimental results. Additionally, to avoid the limitations of pure simulation or experimentation in validating when there are novel SOC estimation algorithms, a hardware-in-the-loop (HIL) test bench is also introduced. The final results demonstrate that the mAUKF algorithm achieves faster convergence rates of about 45% and 49% when handling large initial errors and transient responses compared to the original methods, in simulation and experiment cases, respectively.

关键词： State-of-charge Adaptive unscented Kalman filter Hardware-in-the-loop forgetting factor recursive least squares battery management systems

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Improved forgetting factor recursive least square and adaptive square root unscented Kalman filtering methods for online model parameter identification and joint estimation of state of charge and state of energy of lithium-ion batteries

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IONICS 2023年第12期29卷 5295-5314页

作者： Zhu, Tao Wang, Shunli Fan, Yongcun Zhou, Heng Zhou, Yifei Fernandez, Carlos Southwest Univ Sci & Technol Sch Informat Engn Mianyang 621010 Peoples R China Sichuan Univ Sch Elect Engn Chengdu 610065 Peoples R China Robert Gordon Univ Sch Pharm & Life Sci Aberdeen Scotland

The estimation of the state of charge (SOC) and state of energy (SOE) of lithium-ion batteries is very important for the battery management system (BMS) and the analysis of the causes of equipment failures. Aiming at many problems such as the changes in the parameters of the lithium battery model and the accurate estimation of the SOC and SOE, this paper proposes a joint algorithm of forgetting factor recursive least square (FFRLS) and adaptive square root unscented Kalman filter (ASRUKF) based on the second-order RC equivalent circuit model. In this paper, the joint FFRLS-ASRUKF algorithm is used to perform simulation experiments under three different working conditions of HPPC, DST, and BBDST at different temperatures of 25, 15, and 5 & DEG;C. And a current & PLUSMN;1 A offset is added as a disturbance to verify the robustness of ASRUKF. The results show that under HPPC working condition, the RMSE, MAE, and MAPE estimated by ASRUKF for SOC and SOE of lithium-ion batteries at three temperatures do not exceed 0.0016, 0.0012, and 0.43%, respectively. Under DST working condition, ASRUKF estimates that RMSE, MAE, and MAPE of SOC and SOE of lithium-ion batteries at three different temperatures do not exceed 0.0013, 0.0009, and 0.70% respectively. Under BBDST operating conditions, ASRUKF estimates that the RMSE, MAE, and MAPE of the SOC and SOE of lithium-ion batteries at three different temperatures do not exceed 0.0016, 0.0009, and 0.71% respectively. After adding the current offset, ASRUKF can still accurately estimate the SOC and SOE of lithium-ion batteries.

关键词： Lithium-ion battery Second-order RC equivalent circuit model State of charge State of energy Adaptive square root unscented Kalman filter algorithm forgetting factor recursive least squares

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State of charge and online model parameters co-estimation for liquid metal batteries

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APPLIED ENERGY 2019年 250卷 677-684页

作者： Liu, Guoan Xu, Cheng Li, Haomiao Jiang, Kai Wang, Kangli Huazhong Univ Sci & Technol Sch Elect & Elect Engn State Key Lab Adv Electromagnet Engn & Technol Wuhan 430074 Hubei Peoples R China

Liquid metal battery (LMB) is a novel battery technology that shows great application potential in the electric energy storage system. For the utilization of battery systems, an accurate estimate of the state of charge (SOC) for LMBs is of great significance. However, there are still many challenges need to be addressed due to the relatively low voltage and flat open-circuit-voltage versus SOC curve of LMBs. In this work, a novel state and parameter co-estimator is developed to concurrently estimate the state and model parameters of a Thevenin model for LMBs. The adaptive unscented Kalman filter is employed for state estimation including the battery SOC, and the forgetting factor recursive least squares is applied for online parameter estimation, which increase the model fidelity and further enhance the accuracy and robustness of the SOC estimation. A comparison with other algorithms is made based on the experimental data from laboratory-made LMBs. The evaluation results show that the proposed co-estimator exhibits the smallest root mean square error of 0.21% and is robust to external disturbances.

关键词： Liquid metal battery State of charge Adapative unscented Kalman filter forgetting factor recursive least squares

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Short-Circuit Fault Detection and Quantitative Analysis Based on Mean-Difference Model With Variational Modal Decomposition

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JOURNAL OF ELECTROCHEMICAL ENERGY CONVERSION AND STORAGE 2024年第2期21卷 021007页

作者： Chang, Chun Wang, Zile Zhang, Zhen Jiang, Jiuchun He, Xing Tian, Aina Jiang, Yan Hubei Univ Technol Hubei Key Lab High Efficiency Utilizat Solar Energ Wuhan 430068 Peoples R China Sunwoda Elect Co Ltd Shenzhen 518108 Peoples R China Inst Prod Safety China Natl Inst Standardizat Beijing 100191 Peoples R China

Short-circuit failure is one of the triggers for thermal runaway of lithium-ion batteries, which can lead to serious safety issues. This paper attempts to estimate the short-circuit resistance of the cell using the mean difference model and relies on the estimated results to make a quantitative analysis of short-circuit fault. To achieve this goal, a combination of forgetting factor recursive least squares and extended Kalman filter is used to estimate the average open-circuit voltage within the battery pack. Subsequently, since both the open-circuit voltage (OCV) and intrinsic mode function (IMF0) components reflect the low-frequency characteristics of the battery voltage, we propose a new method based on the variational modal decomposition to extract the differential open-circuit voltage of the battery and finally make an estimate of the short-circuit resistance after obtaining OCV of the battery using the idea of the mean difference model (MDM). In addition, the effectiveness of the proposed method is verified under different degrees of short-circuit faults by connecting different resistors to the series battery pack.

关键词： short circuit mean-difference model forgetting factor recursive least squares extended Kalman filter variational modal decomposition batteries novel numerical and analytical simulations

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Generalized predictive control for fractional order dynamic model of solid oxide fuel cell output power

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JOURNAL OF POWER SOURCES 2010年第24期195卷 8097-8103页

作者： Deng, Zhonghua Cao, Hongliang Li, Xi Jiang, Jianhua Yang, Jie Qin, Yi Huazhong Univ Sci & Technol Dept Control Sci & Engn Key Lab Educ Minist Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Mat Sci & Engn State Key Lab Mat Proc & Die & Mould Technol Wuhan 430074 Peoples R China China Univ Geosci Sch Mech & Elect Informat Wuhan 430074 Peoples R China

An adaptive generalized predictive control (GPC) system is presented for the management of output power of solid oxide fuel cells (SOFCs). The dynamics of SOFC output power are characterized by a fractional order model, which is more accurate than an integer order model to depict the dynamics;the fractional order dynamic model is taken as the controlled plant of the GPC system. The GPC algorithm adopts a linear approximation method that uses a linear predictive model to approximate locally and dynamically the nonlinear dynamics of SOFC output power at each sampling period. Moreover, the parameters of the predictive model are identified online to overcome the time-varying dynamics of SOFC output power via introducing a forgetting factor recursive least squares (FFRLS) algorithm. Finally, according to the future power outputs predicted by the predictive model, an optimal current control sequence is obtained by solving a multistage cost function. The results demonstrate that the dynamic responses of the GPC system are quick and smooth, and the change of the current control sequence is slow and smooth. The quick and smooth dynamics are important for satisfying the rapid load following of SOFC generating systems and for prolonging the lifetime of SOFC stack. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Solid oxide fuel cells Generalized predictive control Fractional order dynamic model forgetting factor recursive least squares

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Improved online parameter identification coupled with discrete wavelet transform for state-of-charge estimation under noise-riding discharging/ charging battery voltage

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ELECTROCHIMICA ACTA 2024年 508卷

作者： Tingbari, Vincent Masabiar Tagayi, Roland Kobla Kim, Jonghoon Chungnam Natl Univ Dept Elect Engn Energy Storage & Convers Lab Daejeon 34134 South Korea

With the expansion of the battery market and electric vehicles, improved battery-state estimation approaches are required for energy management to meet the requirements for safe usage. The accurate identification of battery parameters significantly influences the state-of-charge (SOC) computation algorithms. In addition, noise from charging and discharging voltages can influence the accuracy of the SOC estimation. This paper proposes a novel methodology combining a discrete wavelet transform (DWT) denoising procedure and an adaptive parameter identification. The DWT uses a multi-resolution analysis (MRA) to decompose noise-riding charging/discharge voltage signals, which are then rebuilt using the inverse DWT (IDWT) based on the rigsure adaptive threshold selection rule. In this study, a dual-resistor-capacitor lithium-ion battery model was examined. A formulation of the modified forgetting factor recursive least squares (MFFRLS) was presented and demonstrated robustness in overcoming data saturation while enhancing parameter tracking ability even under the effect of noise. To verify the effectiveness of the coupled methods for SOC estimation defined in this study, experimental data obtained from various working conditions were employed to estimate the SOC using the extended Kalman filter (EKF). A thorough analysis demonstrated that the proposed MFFRLS combined with the denoising method performed well in terms of SOC estimation accuracy and robustness.

关键词： Battery state-of-charge Parameter identification Lithium-ion battery forgetting factor recursive least squares Discrete wavelet transform

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Robust state of charge estimation for lithium-ion batteries in variable temperatures using truncated singular value decomposition Cubature Kalman Filter algorithm

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INTERNATIONAL JOURNAL OF ELECTROCHEMICAL SCIENCE 2024年第5期19卷

作者： Zhang, Tongrui Huang, Ce Zhu, Qiujin Dalian Maritime Univ Houston Int Inst Dalian 110626 Peoples R China Univ Houston Cullen Coll Engn Houston TX 77204 USA Harbin Univ Sci & Technol Elect Engn & Automat Coll Harbin 150080 Peoples R China

Advanced battery management systems play a significant role in the safe and efficient use of battery energy storage systems. While the accurate and reliable estimation of state of charge (SOC) is essential for improving the utilization efficiency of batteries and extending their service life. However, the working environment of battery energy storage systems is highly complex, and temperature changes can often affect the accuracy of modeling and estimation. The traditional methods of SOC estimation often ignore the changes in temperature of the whole system, thus resulting in the cumulative errors in SOC estimation. To address this problem, an improved method is proposed in this paper to estimate the SOC of lithium-ion batteries. It requires the use of Cubature Kalman Filter (CKF) algorithm that is based on Truncated Singular Value Decomposition (TSVD). Additionally, the forgetting factor recursive least squares (FFRLS) method is used to determine the parameters of the Thevenin equivalent model for lithium batteries. This algorithm is then validated through simulation analysis and comparison with the experimental data obtained under cyclic operating conditions at a temperature of 5 degrees C, 25 degrees C, and 40 degrees C, respectively. The results demonstrate that the proposed TSVD-CKF algorithm is accurate in reflecting the impact of ambient temperature on the model parameters and is applicable to SOC estimation in a wide range of temperatures, showing a strong robustness.

关键词： Energy storage systems SOC forgetting factor recursive least squares TSVD-CKF

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