检索结果-内蒙古大学图书馆

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Simulation Research and Embedded Implementation of Model Based SOC Estimation for Lithium Battery 6

Simulation Research and Embedded Implementation of Model Bas...

引用

6th International Conference on Smart Grid and Electrical Automation (ICSGEA)

作者： Wu, Youyu Zhou, Dingyinan Wuhan Univ Technol Sch Informat Engn Wuhan 430070 Peoples R China

ISBN: (纸本)9781665432634

State of charge (SOC) estimation is one of the key technologies in battery management system of electric vehicle. In order to improve the estimation accuracy of SOC, this paper improves the extended kalman filter (EKF) algorithm;in the identification of model parameters, this paper uses the least square method based on the forgetting factor to construct a lithium battery time-varying parameter model;and then uses EKF algorithm to realize SOC online estimation. Matlab simulation results show that the improved algorithm is more accurate than the traditional EKF algorithm, and the estimation error is less than 2%. Finally, the algorithm is implemented on the vehicle embedded system by using the automatic code generation technology.

关键词： SOC estimation Time-varying battery model extended kalman filter algorithm Automatic code generation

来源：评论

学校读者我要写书评

暂无评论

An improved second-order electrical equivalent modeling method for the online high power Li-ion battery state of charge estimation 12

An improved second-order electrical equivalent modeling meth...

引用

12th IEEE Energy Conversion Congress and Exposition-Asia (ECCE-Asia)

作者： Deng, Dan Liu, Sheng-Yong Wang, Shun-Li Xia, Li-li Chen, Lei Guangxi Univ Sci & Technol Sch Elect & Informat Engn Liuzhou Peoples R China Guangxi Key Lab Auto Parts & Vehicle Technol Liuzhou Peoples R China Southwest Univ Sci & Technol Sch Informat Engn Mianyang Sichuan Peoples R China

ISBN: (纸本)9781728163444

The estimation of power battery state of charge and the selection of an effective equivalent model have always been the fi)cus and difficulty of current research. This article has improved and optimized the traditional 'rhevenin model, and the performance of the Li-ion-ion battery is tested through hybrid Pulse Power Characterization (HPPC) experiments. 'the off-line curve fitting identification method is used to identify the relevant parameters in the model to obtain the intrinsic relationship with the state of charge (SOC). An equivalent circuit model is established on the simulation platform to compare and verify actual data. The error of the verification result is controlled within 3.05%, which proves the effectiveness of the improved 'Ilievenin model. On the basis that the relevant parameters of the equivalent model have been identified, the recursive principle of the extended kalman filter algorithm (EKY) is studied, and the algorithm is applied to the Li-ion battery SOC estimation to obtain an accurate state estimation of the 1,i-ion battery. The algorithm is simulated and verified under working conditions, and the verification showed that its accuracy reached more than 98.376%, which ensured the reliability of the battery during use.

关键词： Li-ion battery improved Thevenin model parameter identification HPPC experiment extended kalman filter algorithm state of charge

来源：评论

学校读者我要写书评

暂无评论

Error Analysis of Model-based State-of-Charge Estimation for Lithium-Ion Batteries at Different Temperatures

引用

INTERNATIONAL JOURNAL OF ELECTROCHEMICAL SCIENCE 2020年第10期15卷 9981-10006页

作者： Ren, Zhong Du, Changqing Wang, Huawu Shao, Jianbo Wuhan Univ Technol Hubei Key Lab Adv Technol Automot Components Wuhan 430070 Peoples R China Wuhan Univ Technol Hubei Collaborat Innovat Ctr Automot Components T Wuhan 430070 Peoples R China Dongfeng Commercial Vehicle Co Ltd Wuhan 430070 Peoples R China

State-of-charge (SOC) estimation of lithium-ion batteries (LIBs) is one of the core functions of a battery management system (BMS). Until now, numerous approaches have been proposed to achieve high accuracy SOC estimation, among which the model-based SOC estimation algorithm is the most popular algorithm implementation in an actual BMS. Since SOC estimation accuracy is directly influenced by battery model accuracy, it is essential to quantitatively analyze the relationship between model and SOC estimation accuracy, as well as the error sources of the model error. In this article, first, the model accuracy and SOC estimation accuracy are comprehensively studied based on the first-order resistance-capacitor (RC) model and extended kalman filter (EKF) algorithm under the constant current discharge (CCD) test and Federal Urban Dynamic Schedule (FUDS) test at different test temperatures (0 degrees C, 10 degrees C, 20 degrees C, 30 degrees C, 40 degrees C, and 50 degrees C). Second, regression and correlation analysis is applied to quantitatively evaluate the relationship between the normalized root-mean-square error (RMSE) of model and SOC estimation error. Third, the impact of the SOC open-circuit-voltage (OCV) curve, Ohmic resistance, impedance, and sensor error on SOC estimation accuracy are systematically studied as well. The results show that there is a one-dimensional linear positive relationship between model and SOC estimation accuracy, and the specific quantitative relationship is given. Among the parameters of the battery model, the accuracy of the SOC-OCV curve has the greatest influence on model and SOC estimation error compared to Ohmic resistance and impedance. In addition, compared to the effect of current sensor error, the voltage sensor error has a more significant impact on model and SOC estimation error.

关键词： Lithium-Ion Battery State-of-Charge Estimation extended kalman filter algorithm Error Analysis Battery Management System

来源：评论

学校读者我要写书评

暂无评论

Design and Application of a Small ROV Control System Based on ArduSub System 2

Design and Application of a Small ROV Control System Based o...

引用

2nd IEEE International Conference on Civil Aviation Safety and Information Technology (ICCASIT)

作者： Zhao, Yuming He, Zhen Li, Gelun Wang, Yabiao Li, Zhigang Chinese Acad Sci Shenyang Inst Automat Shenyang 110169 Peoples R China Chinese Acad Sci Inst Robot Shenyang 110169 Peoples R China Chinese Acad Sci Inst Intelligent Mfg Shenyang 110169 Peoples R China

ISBN: (纸本)9781728199481

A small ROV was designed and developed for its wide application and low cost, ArduSub system was used to construct ROV control system to realize ROV motion and attitude control. Equipped with componets such as cameras, underwater lights, sonar and manipulator, ROV has the characteristics of flexible movmcnt, strong anti-current ability, and underwater observation and operation capabilities. Structure and control system composition of ROV and the attitude control algorithm used is introduced in the paper. Through the pool test, the results showed that the use of ArduSub system to build ROV control system makes the ROV has better motion performance and attitude maintain ability, shorten the ROV development cycle and reduce cost.

关键词： ArduSub Pixhawk ROV extended kalman filter algorithm Double closed-loop PID

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：