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State of charge estimation method based on the extended kalman filter algorithm with consideration of time-varying battery parameters

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2020年第13期44卷 10538-10550页

作者： Luo, Yong Qi, Pengwei Kan, Yingzhe Huang, Jiayu Huang, Huan Luo, Jianwen Wang, Jianan Wei, Yongheng Xiao, Renjie Zhao, Shuang Chongqing Univ Technol Key Lab Adv Mfg Technol Automobile Parts Chongqing 400054 Peoples R China Chongqing Univ State Key Lab Mech Transmiss Chongqing Peoples R China

In developing battery management systems, estimating state-of-charge (SOC) is important yet challenging. Compared with traditional SOC estimation methods (eg, the ampere-hour integration method), extended kalman filter (EKF) algorithm does not depend on the initial value of SOC and has no accumulated error, which is suitable for the actual working condition of electric vehicles. EKF is a model-based algorithm;the accuracy of SOC estimated by this algorithm was greatly influenced by the accuracy of battery model and model parameters. The parameters of battery change with many factors and exhibit strong nonlinearity and time variance. Typical EKF algorithm approximates battery as a linear, time-invariant system;however, this approach introduces estimation errors. To minimize such errors, previous studies have focused on improving the accuracy of identifying battery parameters. Although studies on battery model with time-varying parameters have been carried out, few have studied the combination of time-varying battery parameters and EKF algorithm. A SOC estimation method that combines time-varying battery parameters with EKF algorithm is proposed to improve the accuracy of SOC estimation. Battery parameter data were obtained experimentally under different temperatures, SOC levels, and discharge rates. The results of parameter identification are made into a data table, and the battery parameters in the EKF system matrix are updated by looking up the data in the table. Simulation and experimental results shown that, average error of SOC estimated by the proposed algorithm is 2.39% under 0.9 C constant current discharge and 2.4% under 1.3 C, which is 1.91% and 2.35% lower than that of EKF algorithm with fixed battery parameters. Under intermittent discharge with constant current (1.1 C) and capacity (10%), the average error of SOC estimated by the proposed algorithm is 1.4%, which is 0.3% lower than that of EKF algorithm with fixed battery parameters. The average error of

关键词： battery management system extended kalman filter algorithm second-order RC equivalent circuit battery model state-of-charge

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An optimized quantum particle swarm optimization-extended kalman filter algorithm for the online state of charge estimation of high-capacity lithium-ion batteries under varying temperature conditions

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IONICS 2024年第10期30卷 6163-6177页

作者： Wu, Wenjie Wang, Shunli Liu, Donglei Fan, Yongcun Mo, Daijiang Fernandez, Carlos Southwest Univ Sci & Technol Sch Informat Engn Mianyang 621010 Peoples R China Inner Mongolia Univ Technol Coll Elect Power Hohhot 010080 Peoples R China Robert Gordon Univ Sch Pharm & Life Sci Aberdeen AB10 7GJ Scotland

The core focus of the battery management system (BMS) is accurate state of charge (SOC) estimation of the lithium-ion batteries. To solve the problem of improper selection of the noise covariance matrix in the extended kalman filter (EKF) algorithm, which in turn affects the actual operating effect and range of electric vehicles, this paper proposes the adaptive sine cosine-Levy flight-quantum particle swarm optimization (ASL-QPSO) algorithm to find the optimal noise covariance matrix. Firstly, this paper proposes the variable forgetting factor recursive least square (VFFRLS) algorithm to identify the parameters of the equivalent circuit model of the power lithium-ion batteries. Then, the obtained parameters are transmitted online by the EKF algorithm, based on which the local attraction factor is updated using the ASL-QPSO, which is used to select the appropriate noise covariance matrix. Finally, the optimized noise covariance matrix is obtained and used to achieve the accurate SOC estimation of the power lithium-ion batteries. Experimental results under different operating conditions and temperatures show that the maximum absolute error (MAX), mean absolute error (MAE), and root mean square error (RMSE) of the algorithm are less than 1.82%, 0.59%, and 0.72%, respectively. This demonstrates that the algorithm has superior convergence tuning and high robustness, presenting a novel optimization strategy for the SOC estimation of lithium-ion batteries.

关键词： Lithium-ion batteries Second-order RC equivalent circuit model State of charge extended kalman filter algorithm Adaptive sine cosine-Levy flight-quantum particle swarm optimization

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State of charge estimation of lithium-ion battery based on extended kalman filter algorithm

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FRONTIERS IN ENERGY RESEARCH 2023年 11卷

作者： Xie, Jiamiao Wei, Xingyu Bo, Xiqiao Zhang, Peng Chen, Pengyun Hao, Wenqian Yuan, Meini North Univ China Sch Aerosp Engn Taiyuan Shanxi Peoples R China North Univ China Coll Mechatron Engn Taiyuan Shanxi Peoples R China Guangdong Aerosp Res Acad Guangzhou Guangdong Peoples R China North Univ China Underground Target Damage Technol Natl Def Key Dis Taiyuan Shanxi Peoples R China

Due to excellent power and energy density, low self-discharge and long life, lithium-ion battery plays an important role in many fields. Directed against the complexity of above noises and the strong sensitivity of the common kalman filter algorithm to noises, the state of charge estimation of lithium-ion battery based on extended kalman filter algorithm is investigated in this paper. Based on the second-order resistor-capacitance equivalent circuit model, the battery model parameters are identified using the MATLAB/Simulink software. A battery parameter test platform is built to test the charge-discharge efficiency, open-circuit voltage and state of charge relationship curve, internal resistance and capacitance of the individual battery are tested. The simulation and experimental results of terminal voltage for lithium-ion battery is compared to verify the effectiveness of this method. In addition, the general applicability of state of charge estimation algorithm for the battery pack is explored. The ampere-hour integral method combined with the battery modeling is used to estimate the state of charge of lithium-ion battery. The comparison of extended kalman filter algorithm between experimental results and simulation estimated results is obtained to verify the accuracy. The extended kalman filter algorithm proposed in this study not only establishes the theoretical basis for the condition monitoring but also provides the safe guarantee for the engineering application of lithium-ion battery.

关键词： state of charge (SOC) second-order resistor-capacitance (RC) equivalent circuit model extended kalman filter algorithm lithium-ion battery MATLAB simulink

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State of Charge Estimation algorithm for Unmanned Aerial Vehicle Power-Type Lithium Battery Packs Based on the extended kalman filter

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ENERGIES 2019年第20期12卷 3960页

作者： Zhang, Haitao Zhou, Ming Lan, Xudong Tsinghua Univ Sch Aerosp Engn Beijing 100084 Peoples R China

The lack of endurance is an important reason restricting further development of unmanned aerial vehicles (UAVs). Accurately estimating the state of charge (SOC) of the Li-Po battery can maximize the battery energy utilization and improve the endurance of UAVs. In this paper, the main current methods for estimating the SOC of vehicles were explored and discussed to unveil their advantages and disadvantages. In addition, the extended kalman filter algorithm based on an equivalent circuit model was used to estimate SOC of power-type Li-Po batteries at different temperatures. The result showed that the closed-loop control method can effectively improve the battery life of small-sized electric UAVs.

关键词： unmanned aerial vehicle power-type Li-Po battery state of charge extended kalman filter algorithm

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Advanced localization techniques for autonomous road vehicles: Integrating ultrasonic and beacon-based systems

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2025年第2期22卷

作者： Nithilam, D. N. Paulchamy, B. Hindusthan Inst Technol Dept Elect & Commun Engn Coimbatore Tamil Nadu India

In the realm of autonomous highway navigation, a critical initial task is accurately localizing the vehicle on the road. This necessitates the integration of data from various sensors alongside information gleaned from road maps to ensure precision and reliability. This research addresses the critical need for precise localization of autonomous road vehicles, essential for safe and efficient navigation in complex urban environments. Traditional GPS-based systems often face limitations in accuracy, especially in urban canyons and environments with tall buildings. Thus, alternative localization methodologies are essential to ensure reliable autonomous driving. Two novel localization methodologies are explored: an Ultrasonic Indoor Positioning System with a Single Beacon Range-Based Localization Approach. The Ultrasonic Indoor Positioning System involves deploying ultrasonic transmitters on vehicles and receivers at known locations, utilizing time-of-flight calculations for distance estimation. Calibration techniques and outlier rejection enhance system accuracy. Conversely, the single beacon method integrates beacon range measurements with vehicle speed and heading information using an extended kalman filter algorithm for iterative position estimation. Experimental evaluations demonstrate the effectiveness of both methodologies. The Ultrasonic Indoor Positioning System achieves position accuracies of 3-4 cm for static targets and 4-7 cm for moving targets, with a minimal error rate. In comparison, the single beacon approach achieves localization within approximately 30 cm of the true position, with a range measurement accuracy of 1-4 cm and a settling time of around 50 s. These results highlight the feasibility and reliability of employing these methodologies for autonomous road vehicle localization. The research underscores the significance of alternative localization techniques in enhancing the autonomy and safety of road vehicles. By leveraging ultrasonic and beaco

关键词： Autonomous highway navigation localization methodologies ultrasonic indoor positioning system single beacon range-based localization approach extended kalman filter algorithm

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Adaptive prescribed-time disturbance observer using nonsingular terminal sliding mode control: extended kalman filter and particle swarm optimization

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IET CONTROL THEORY AND APPLICATIONS 2020年第19期14卷 3301-3311页

作者： Vahidi-Moghaddam, Amin Rajaei, Arman Ayati, Moosa Vatankhah, Ramin Hairi-Yazdi, Mohammad Reza Michigan State Univ Dept Mech Engn E Lansing MI 48824 USA Shiraz Univ Sch Mech Engn Shiraz Iran Univ Tehran Sch Mech Engn Coll Engn Tehran Iran

In this study, adaptive prescribed finite-time stabilisation of uncertain single-input and single-output non-linear systems is considered in the presence of unknown states, unknown parameters, external load disturbance, and non-symmetric input saturation. A prescribed finite time disturbance observer is designed to approximate the unmeasured external disturbance. Also, a non-singular prescribed finite time terminal sliding mode control is proposed for the closed-loop control of the system with the non-symmetric input saturation. extended kalman filter algorithm is employed for the real-time estimations of the states and unknown parameters of the system. Moreover, a particle swarm optimisation algorithm is used to obtain the design parameters of the proposed disturbance observer and controller. To show the performance of designed control scheme, the proposed approach is employed to guarantee prescribed finite time stabilisation of non-linear vibration of a non-local strain gradient nanobeam. The Galerkin projection method is used to reduce the non-dimensional form of the governing non-linear partial differential equation of Euler-Bernoulli nanobeam to the ordinary differential equation. Finally, numerical simulations are performed to illustrate the effectiveness and performance of the developed adaptive control scheme for the vibration control of nanobeam in comparison with the conventional sliding mode control.

关键词： observers uncertain systems kalman filters Galerkin method control system synthesis differential equations adaptive control stability closed loop systems partial differential equations vibration control linear systems vibrations particle swarm optimisation variable structure systems nonlinear control systems uncertain nonlinear systems nonsingular terminal finite-time stabilisation uncertain single-input single-output unknown states unknown parameters external load disturbance nonsymmetric input saturation prescribed finite time disturbance observer approximate the unmeasured external disturbance nonsingular prescribed finite time terminal closed-loop control extended kalman filter algorithm real-time estimations particle swarm optimisation algorithm design parameters controller designed control scheme prescribed finite time stabilisation nonlinear vibration nonlocal strain gradient nanobeam nondimensional form governing nonlinear partial differential equation developed adaptive control scheme

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Model Predictive Torque Control for PMSM Driven by Three-Level Inverter Based on extended kalman filter Speed Observer

Model Predictive Torque Control for PMSM Driven by Three-Lev...

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IEEE International Conference of Intelligent Robotics and Control Engineering (IRCE)

作者： Han, Xiao Teng, Qingfang Luo, Weiduo Lanzhou Jiaotong Univ Dept Automat & Elect Engn Lanzhou 730070 Gansu Peoples R China

ISBN: (纸本)9781538674161

Based on the extended kalman filter algorithm, a non sensor model predictive torque control strategy is proposed for the permanent magnet synchronous motor (PMSM) driven by three-level inverter. The method of MPTC is proposed in order to reduce the torque and flux ripple and improve the control performance of the system, the speed observer based on the extended kalman filter algorithm can realize the online observation of motor speed and enhance the robustness of the system. Compared with the traditional sliding mode speed observer, the observer is more simple and easier to implement. Also, the observer reduces the ripple of the motor speed in high frequency range in an efficient way. The control strategy in this paper can make PMSM MPTC system have better control performance, stronger robustness and good dynamic *** simulation results validate the feasibility and effectiveness of the proposed scheme.

关键词： permanent magnet synchronous motor (PMSM) Three-level inverter extended kalman filter algorithm model predictive torque control (MPTC)

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Identification of Hammerstein-Wiener ARMAX Systems Using extended kalman filter

Identification of Hammerstein-Wiener ARMAX Systems Using Ext...

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23rd Chinese Control and Decision Conference

作者： Mansouri, M. Tolouei, H. Shoorehdeli, M. Aliyari KN Toosi Univ Technol Control Dept Fac Elect Engn Intelligent Syst Lab ISLAB Tehran Iran KN Toosi Univ Technol Control Dept Fac Elect Engn Adv Proc Automat & Control Lab APAC Tehran Iran KN Toosi Univ Technol Mechatron Dept Fac Elect Engn Adv Proc Automat & Control Lab APAC Tehran Iran

ISBN: (纸本)9781424487363

In this study, extended kalman filter (EKF) algorithm is developed to estimate the parameters of Hammerstein-Wiener (H-W) ARMAX models. The basic idea is to estimate the original parameters of the identification model, which are appeared in the form of product terms, directly. While, other algorithms like extended Forgetting Factor Stochastic Gradient (EFG), extended Stochastic Gradient (ESG), Forgetting Factor Recursive Least Square (FFRLS) and kalman filter (KF), estimate parameters in the product form and they need another algorithms such averaging method (AVE method), singular value decomposition method (SVD method) to separate the parameters. So, the computational complexity of the proposed approach decreases. To show the efficiency of this method the results are compared with EFG and ESG method.

关键词： Hammerstein-Wiener model extended kalman filter algorithm extended Stochastic Gradient algorithm extended Forgetting Factor Stochastic Gradient algorithm

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State of charge estimation for Li-ion battery based on extended kalman filter 8th

State of charge estimation for Li-ion battery based on exten...

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8th International Conference on Applied Energy (ICAE)

作者： Li Zhi Zhang Peng Wang Zhifu Song Qiang Rong Yinan Collaborat Innovat Ctr Elect Vehicles Beijing Beijing 100081 Peoples R China Huanghe Jiaotong Univ Coll Vehicle Engn Wuzhi 454950 Henan Province Peoples R China

It is difficult to estimate Lithium-ion battery state of charge (SOC) accurately. By using extended kalman filter (EKF). the interference of system noise can be effectively reduced, which improved the estimation accuracy. First, the battery model was studied and a Thevenin model was established. Then the appropriate battery charge-and-discharge experiments were performed to identify the parameters of the model. Finally EKF applied to the model experiments show that EKF has high precision. (C) 2017 Published by Elsevier Ltd.

关键词： Lithium-ion battery Thevenin model extended kalman filter algorithm Battery state of charge

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State of Charge Estimation for Li-ion Battery Based on extended kalman filter

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Energy Procedia 2017年 105卷 3515-3520页

作者： Li Zhi Zhang Peng Wang Zhifu Song Qiang Rong Yinan Collaborative Innovation Center of Electric Vehicles in Beijing Beijing 100081 China College of Vehicle Engineering Huanghe Jiaotong University Wuzhi Henan Province 454950 China

It is difficult to estimate Lithium-ion battery state of charge (SOC) accurately. By using extended kalman filter (EKF).the interference of system noise can be effectively reduced, which improved the estimation accuracy. First, the battery model was studied and a Thevenin model was established. Then the appropriate battery charge-and-discharge experiments were performed to identify the parameters of the model. Finally EKF applied to the model experiments show that EKF has high precision.

关键词： Lithium-ion battery Thevenin model extended kalman filter algorithm Battery state of charge

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