Accuracy Of Parameter Identification Research Articles

Lithium-Ion Battery SOC Estimation Based on Adaptive Forgetting Factor Least Squares Online Identification and Unscented Kalman Filter

In order to improve the estimation accuracy of the battery state of charge (SOC) based on the equivalent circuit model, a lithium-ion battery SOC estimation method based on adaptive forgetting factor least squares and unscented Kalman filtering is proposed. The Thevenin equivalent circuit model of the battery is established. Through the simulated annealing optimization algorithm, the forgetting factor is adaptively changed in real-time according to the model demand, and the SOC estimation is realized by combining the least-squares online identification of the adaptive forgetting factor and the unscented Kalman filter. The results show that the terminal voltage error identified by the adaptive forgetting factor least-squares online identification is extremely small; that is, the model parameter identification accuracy is high, and the joint algorithm with the unscented Kalman filter can also achieve a high-precision estimation of SOC.

Improved gray wolf particle filtering and high‐fidelity second‐order autoregressive equivalent modeling for intelligent state of charge prediction of lithium‐ion batteries

The rapid development of new energy vehicles puts forward higher requirements for the lithium-ion batteries model construction, high-efficiency condition monitoring and collaborative estimation. An improved high-fidelity second-order autoregressive model is proposed and constructed, and the autoregressive model is integrated with the second-order equivalent circuit model, which can achieve an accurate and reliable description of the batteries internal dynamic change process. To achieve the accurate expression of the battery's external characteristics and internal state, the forgetting factor is combined with the recursive least square algorithm to improve the parameter identification accuracy and optimality while reducing the space complexity of the algorithm. A novel gray wolf particle filtering algorithm is proposed, which eliminates the particles severe degradation in traditional algorithms and enhances the ability of particles to resist degradation. The algorithm superiority and generalization are verified under complex working conditions. The experimental results show that the accuracy of the high-fidelity second-order autoregressive model can reach 99%, which can well simulate the complex chemical reaction process inside the lithium-ion battery. Experimental simulation is performed under constant current conditions. Compared with the extended Kalman filter, unscented Kalman filter, and particle filter algorithms, the gray wolf particle filter algorithm has reduced the root mean square error by 3.39%, 0.90%, and 2.84%, and the mean absolute error has reduced by 1.95%, 0.51%, and 2.22%. Under dynamic stress test conditions, the root mean square error is reduced by 1.54%, 0.33%, and 0.78%, and the average absolute error is reduced by 1.4%, 0.22%, and 0.76%. In addition, when tested under different environmental conditions, although the improved algorithm has a relatively long running time, the estimation accuracy of the algorithm is greatly improved and the execution efficiency is high. The improved algorithm provides a theoretical basis for the reliability and stability of the onboard operation of lithium-ion batteries. Highlights A novel lithium-ion battery high-fidelity second-order autoregressive equivalent model is proposed. The gray wolf particle filter is employed to improve the observation accuracy. A recursive calculation method is derived by the time-based parameters.

Parameter identification of fractional-order time delay system based on Legendre wavelet

This paper proposes a parameter identification method of fractional-order time delay system based on Legendre wavelet. An integration operational matrix and delay operational matrix of a Legendre wavelet are constructed. The process is derived to convert a fractional-order time delay system based on a Legendre wavelet operational matrix to algebraic equation. The least squares method is used to simultaneously estimate the order, model parameters and time delay coefficient of a system. In addition, wavelet decomposition and reconstruction algorithm are used to pre-process the output signal, which separate the noise from signal. The proposed method overcomes the initial-state dependence and randomness of the identification result in the algebraic solution of the traditional operational matrix, and reduces the effect of noise on the accuracy of parameter identification. It is especially suitable for engineering application. The effectiveness is verified by numerical simulation and a temperature process control system.

Improved parameter identification algorithm for ship model based on nonlinear innovation decorated by sigmoid function

Abstract This paper investigates the problem of parameter identification for ship nonlinear Nomoto model with small test data, a nonlinear innovation-based identification algorithm is presented by embedding sigmoid function in the stochastic gradient algorithm. To demonstrate the validity of the algorithm, an identification test is carried out on the ship ‘SWAN’ with only 26 sets of test data. Furthermore, the identification effects of the least squares algorithm, original stochastic gradient algorithm and the improved stochastic gradient algorithm based on nonlinear innovation are compared. Generally, the stochastic gradient algorithm is not suitable for the condition of small test data. The simulation results indicate that the improved stochastic gradient algorithm with sigmoid function greatly increases its accuracy of parameter identification and has 14.2% up compared with the least squares algorithm. Then the effectiveness of the algorithm is verified by another identification test on the ship ‘Galaxy’, the accuracy of parameter identification can reach more than 95% which can be used in ship motion simulation and controller design. The proposed algorithm has advantages of the small test data, fast speed and high accuracy of identification, which can be extended to other parameter identification systems with less sample data.

A new identification algorithm for ship model parameters based on sinusoidal function processing innovation

Objectives Due to the few data samples of ship test a, it is difficult to identify ship model parameters quickly and accurately. Methods On the basis of the original least-squaresystem identification algorithm, the innovation is processed by sinusoidal function, and a new method of parameter identification for ship model based on nonlinear sinusoidal function is proposed. Results The Yukun ship, a teaching and training ship of Dalian Maritime University, is taken as an example to carry out the identification experiment, In the case of only 26 sets of test data, the identification effect of the original least square method and improved least square algorithm is compared. The simulation results show that the parameter identification accuracy of the improved algorithm is improved by about 15%, and the effectiveness of the algorithm is verified by using the Yupeng ship. Conclusions This algorithm provides reference of parameter identification of ship model with few data samples.

Parameter identification of DC arc models using chaotic quantum cuckoo search

DC arc fault is difficult to be detected, which brings great challenge to the safety of DC distribution system. The investigation of arc fault detection based on simulation method is able to reduce the cost and contributes to the research in the system where it is not suitable to carry out experiments. Accurate arc model is the foundation of simulation research. To address the gaps existing in arc models and corresponding parameter identification methods, two novel arc models and a new optimization algorithm are presented in this paper. First of all, hook static model was proposed to describe the nonlinearity near the transition point in static curve of arc. Then, segmented noise model was developed to flexibly fit the diversiform shape of arc noise’s frequency spectrum (FS). Finally, in order to improve the accuracy of parameter identification of arc models, this paper introduced chaos mechanism into quantum cuckoo search (CQCS): using chaotic map to initialize the population and generate the random parameter p; using lifespan based chaotic local search to further update the position of best solution. Based on the data obtained from established experimental platform, the effectiveness of the proposed models and optimization algorithm is verified. Meanwhile, compared with the existing methodologies, the proposed models and optimization algorithm are of better performance.

Stability Analysis of Shunt Active Power Filter with Predictive Closed-Loop Control of Supply Current

This paper presents a shunt active power filter connected to the grid via an LCL coupling circuit with implemented closed-loop control. The proposed control system allows selective harmonic currents compensation up to the 50th harmonic with the utilization of a model-based predictive current controller. As the system is fully predictive, it provides high effectiveness of the harmonic reduction, which is proved by waveforms achieved in performed tests. On the other hand, the control system is prone to loss of stability. Therefore, the paper is focused on the stability analysis of the discussed control system with the additional outer control loop of the supply current with predictive control of this current. The conducted stability analysis encompasses the assessment of system stability as a function of the coupling circuit parameter identification accuracy, whose values are implemented in the current controller, as well as parameters such as the sampling frequency and proportional-integral (PI) controller coefficients. The obtained results show that the ranges of the LCL circuit parameter identification accuracy for which the system remains stable are relatively wide. However, the most effective compensation of the supply current distortion is achieved for the parameters identified correctly, and the greatest impact on the compensation quality has the value of L1 inductance.

Simultaneous identification of joint stiffness, kinematic and hand-eye parameters of measurement system integrated with serial robot and 3D camera

Purpose This paper aims to propose a measurement principle and a calibration method of measurement system integrated with serial robot and 3D camera to identify its parameters conveniently and achieve high measurement accuracy. Design/methodology/approach A stiffness and kinematic measurement principle of the integrated system is proposed, which considers the influence of robot weight and load weight on measurement accuracy. Then an error model is derived based on the principle that the coordinate of sphere center is invariant, which can simultaneously identify the parameters of joint stiffness, kinematic and hand-eye relationship. Further, considering the errors of the parameters to be calibrated and the measurement error of 3D camera, a method to generate calibration observation data is proposed to validate both calibration accuracy and parameter identification accuracy of calibration method. Findings Comparative simulations and experiments of conventional kinematic calibration method and the stiffness and kinematic calibration method proposed in this paper are conducted. The results of the simulations show that the proposed method is more accurate, and the identified values of angle parameters in modified Denavit and Hartenberg model are closer to their real values. Compared with the conventional calibration method in experiments, the proposed method decreases the maximum and mean errors by 19.9% and 13.4%, respectively. Originality/value A new measurement principle and a novel calibration method are proposed. The proposed method can simultaneously identify joint stiffness, kinematic and hand-eye parameters and obtain not only higher measurement accuracy but also higher parameter identification accuracy, which is suitable for on-site calibration.

Dynamic Parameter Identification of a Pointing Mechanism Considering the Joint Clearance

The clearance of the revolute joint influences the accuracy of dynamic parameter identification. In order to address this problem, a method for dynamic parameter identification of an X–Y pointing mechanism while considering the clearance of the revolute joint is proposed in this paper. Firstly, the nonlinear dynamic model of the pointing mechanism was established based on a modified contact model, which took the effect of the asperity of contact surface on joint clearance into consideration. Secondly, with the aim of achieving the anti-interference incentive trajectory, the trajectory was optimized according to the condition number of the observation matrix and the driving functions of activate joints that could be obtained. Thirdly, dynamic simulation was conducted through Adams software, and clearance was involved in the simulation model. Finally, the dynamic parameter identification of the pointing mechanism was conducted based on an artificial bee colony (ABC) algorithm. The identification result that considered joint clearance was compared with that which did not consider joint clearance. The results showed that the accuracy of the dynamic parameter identification was improved when the clearance was taken into consideration. This study provides a theoretical basis for the improvement of dynamic parameter identification accuracy.

Online full-parameter identification and SOC estimation of lithium-ion battery pack based on composite electrochemical - dual circuit polarization modeling

A new composite electrochemistry-dual circuit polarization model (E-DCP) is proposed by combining the advantages of various electrochemical empirical models in this paper. Then, the multi-innovation least squares (MILS) algorithm is used to perform online full parameter identification for the E-DCP model in order to improve data usage efficiency and parameter identification accuracy. In addition, on the basis of the E-DCP model, the MILS and the extended Kalman filter (EKF) are combined to enhance the state estimation accuracy of the battery management system (BMS). Finally, the model and the algorithm are both verified through urban dynamometer driving schedule (UDDS) and the complex charge-discharge loop test. The results indicate that the accuracy of E-DCP is relatively high under different working conditions, and the errors of state of charge (SOC) estimation after the combination of MILS and EKF are all within 2.2%. This lays a concrete foundation for practical use of the BMS in the future.

A parameter identification method of power grid load model based on edge computing in IoT environment

Load parameter identification is one of the keys and difficult points in power system analysis and research. With the rapid development of the grid scale, the load structure is becoming more and more complex, and the adaptability of the load model structure and typical parameters to the actual power grid has become increasingly prominent. In response to this problem, a parameter identification method for power grid load models in the internet of things (IoT) environment is proposed. Firstly, a load model is established under the power IoT “cloud-network-edge-terminal” architecture. Secondly, the genetic algorithm (GA) is used to identify the parameters of the dynamic load model in the edge layer. At the same time, without changing the structure of the load model, a load parameter security protection method based on edge computing is proposed to ensure the safety and effectiveness of the load parameter identification results. Finally, the effectiveness of the proposed load parameter identification method is tested by comparing response time, load parameter identification accuracy, and edge computing loss.

Human-Exoskeleton Coupling Dynamics of a Multi-Mode Therapeutic Exoskeleton for Upper Limb Rehabilitation Training

The purpose of this study is to establish the human-exoskeleton coupling (HEC) dynamic model of the upper limb exoskeleton, overcome the difficulties of dynamic modeling caused by the differences of individual and disease conditions and the complexity of musculoskeletal system, to achieve early intervention and optimal assistance for stroke patients. This paper proposes a method of HEC dynamics modeling, and analyzes the HEC dynamics in the patient-active training (PAT) and patient-passive training (PPT) mode, and designs a step-by-step dynamic parameter identification method suitable for the PAT and PPT modes. Comparing the HEC torques obtained by the dynamic model with the real torques measured by torque sensors, the root mean square error (RMSE) can be kept within 13% in both PAT and PPT modes. A calibration experiment was intended to further verify the accuracy of dynamic parameter identification. The theoretical torque of the load calculated by the dynamic model, is compared with the difference calculated by parameter identification. The trends and peaks of the two curves are similar, and there are also errors caused by experimental measurements. Furthermore, this paper proposes a prediction model of the patient’s height and weight and HEC dynamics parameters in the PPT mode. The RMSE of the elbow and shoulder joints of the prediction model is 9.5% and 13.3%. The proposed HEC dynamic model is helpful to provide different training effects in the PAT and PPT mode and optimal training and assistance for stroke patients.

Novel method for power system synthesis load identification

Power load parameters are one of the most important parameters for power system model. The load has the characteristics of time-varying, diversity and non-linearity. Therefore, obtaining the load parameters accurately is crucial to the safe and stable operation of the power system. The research in this paper is based on the synthesis load model which takes into account the impedance of distribution network. The multi-fault curves identification method and chaos-whale algorithm are adopted to improve the accuracy and practicability of load parameter identification. First, the algorithm proposed in this paper applies chaos algorithm to whale algorithm, and uses the randomness and ergodicity of chaos algorithm to make up for the shortcomings of the whale algorithm that the solution accuracy is not high and it is easy to fall into local optimum. Secondly, the identification method of multi-fault curves is to select several different faults for parameter identification, and the average value of each group of results obtained is regarded as the final result. Then compared with single curve identification, the multi-fault curves method is more superior. Finally, the accuracy of the results is verified by other faults. Simulation results show that the method is reliable to a certain extent.

A Novel Dual Correction Extended Kalman Filtering Algorithm for The State of Charge Real-Time Estimation of Packing Lithium-Ion Batteries

This paper explores the state estimation method of lithium-ion battery pack through theoretical analysis and experimental research. Combining the advantages of the empirical models of various electrochemical models, a new type of composite electrochemistry-dual circuit polarization (E-DCP) model is proposed to better reflect the dynamic performance of the power lithium-ion battery under the conditions of meeting its safe and reliable energy supply requirements. Using the multi-innovation least squares (MILS) algorithm to identify the parameters in the E-DCP model online, so that it has the characteristics of high data utilization efficiency and high parameter identification accuracy. The battery charge and discharge efficiency function is introduced to dynamically modify the battery capacity, and the dynamic function is used to improve the Kalman gain in the extended Kalman filter (EKF), a new type of based on dynamic function improvement and combined with actual capacity correction (FC-DEKF) algorithm is applied to the estimation of battery pack operating characteristics, which solves the problem that the traditional EKF algorithm is difficult to estimate errors when the system input change rate is large. The experimental results of urban dynamometer driving schedule (UDDS) and complex charge-discharge cycle test show that the maximum error of terminal voltage does not exceed 0.04V, the accuracy is 99.05%, and the errors of MILS algorithm combined with FC-DEKF algorithm for SOC estimation are all within 1%. The proposed equivalent circuit modeling method and state estimation correction strategy provide a theoretical basis for the reliable application of high-power lithium-ion battery packs.

A Newton iterative optimization combined with window loop calculation algorithm for estimating accelerometer bias based on gravitational apparent motion with excitation of swinging motion.

The initial alignment method, including the identification of inertial device error parameters, has always been a key issue in an inertial navigation system (INS). This study focuses on the error caused by the random noise of inertial devices that can be compensated by the reconstruction of gravitational apparent motion in an inertial frame under the condition of swinging motion. Attitude angles and accelerometer bias can also be estimated. However, the analysis and simulation results indicate that the existing methods cannot estimate the gyroscope bias. The accelerometer and the gyroscope bias will change over a long time, which will lead to long-term parameter identification accuracy decline or even failure. In this paper, a parameter identification algorithm based on Newton iterative optimization combined with a window loop calculation is designed to solve these problems. Simulation and turntable tests indicate that the proposed new algorithm can fulfill the initial alignment of strapdown INS under the swinging condition and estimate accelerometer bias effectively. Moreover, the new algorithm improves data utilization, which also has better time sensitivity, and the calculated alignment errors can nearly approach zero.

Parameter identification and performance estimation for PV modules based on reduced forms model

Many methods are proposed for parameter identification and performance estimation of photovoltaic (PV) modules, including analytical methods, numerical methods, and optimization methods. In this paper, a hybrid method based on analysis expressions and optimization algorithms is proposed to determine the parameters of the single-diode model and the double-diode model for PV modules. The method is based on the reduced forms and combines the analytical method and the optimization method. By using three key points in I–V curves and making some approximations or simplifying assumptions without sacrificing accuracy, the dimensions of the search space are reduced to only one independent parameter, which reduces the computational complexity and cost. After identifying the parameters under certain conditions, the parameters under any operating conditions are estimated based on the dependence of parameters on environmental conditions. The proposed method is validated by massive experimental data of I–V curves under varying operating conditions. Comparing with other methods with high accuracy, the proposed method shows similar accuracy in parameter identification under certain conditions. Using the proposed method, the output performance estimation is found to be better than the results of other methods. Moreover, the influence of condition selection on the accuracy of output power performance estimation is investigated thoroughly.

基于IPSO-EKF算法的插秧机模型参数在线辨识

In order to improve the accuracy of rice transplanter model parameters, an online parameter identification algorithm for the rice transplanter model based on improved particle swarm optimization (IPSO) algorithm and extended Kalman filter (EKF) algorithm was proposed. The dynamic model of the rice transplanter was established to determine the model parameters of the rice transplanter. Aiming at the problem that the noise matrices in EKF algorithm were difficult to select and affected the best filtering effect, the proposed algorithm used the IPSO algorithm to optimize the noise matrices of the EKF algorithm in offline state. According to the actual vehicle tests, the IPSO-EKF was used to identify the cornering stiffness of the front and rear tires online, and the identified cornering stiffness value was substituted into the model to calculate the output data and was compared with the measured data. The simulation results showed that the accuracy of parameter identification for the rice transplanter model based on the IPSO-EKF algorithm was improved, and established an accurate rice transplanter model.

Modeling and Simulation of English Speech Rationality Optimization Recognition Based on Improved Particle Filter Algorithm

As one of the most important communication tools for human beings, English pronunciation not only conveys literal information but also conveys emotion through the change of tone. Based on the standard particle filtering algorithm, an improved auxiliary traceless particle filtering algorithm is proposed. In importance sampling, based on the latest observation information, the unscented Kalman filter method is used to calculate each particle estimate to improve the accuracy of particle nonlinear transformation estimation; during the resampling process, auxiliary factors are introduced to modify the particle weights to enrich the diversity of particles and weaken particle degradation. The improved particle filter algorithm was used for online parameter identification and compared with the standard particle filter algorithm, extended Kalman particle filter algorithm, and traceless particle filter algorithm for parameter identification accuracy and calculation efficiency. The topic model is used to extract the semantic space vector representation of English phonetic text and to sequentially predict the emotional information of different scales at the chapter level, paragraph level, and sentence level. The system has reasonable recognition ability for general speech, and the improved particle filter algorithm evaluation method is further used to optimize the defect of the English speech rationality and high recognition error rate Related experiments have verified the effectiveness of the method.

Parameter identification of a lithium‐ion battery based on the improved recursive least square algorithm

Accurate parameter identification of a lithium-ion battery is a critical basis in the battery management systems. Based on the analysis of the second-order RC equivalent circuit model, the parameter identification process using the recursive least squares (RLS) algorithm is discussed firstly. The reason for the RLS algorithm affecting the accuracy and rapidity of model parameter identification is pointed out. And an improved RLS algorithm is proposed, an inner loop with the estimated parameter vector updated multiple times is inserted into the conventional RLS algorithm, so that the identification results are improved. The test platform of a single lithium-ion battery is built. The experimental results show that the improved RLS algorithm has better tracking ability, smaller prediction error and has a moderate computational burden compared with the conventional RLS algorithm and a variable forgetting factor RLS algorithm.

Improved parameters identification and state of charge estimation for lithium-ion battery with real-time optimal forgetting factor

Accurate estimation of state of charge (SOC) is extremely essential for energy management of electric vehicles, and precise identification of model parameters will directly affect the results of SOC estimation. However, the traditional recursive least squares (RLS) method cannot accurately track the changes of model parameters in actual complex conditions. To solve these problems, a new parameters identification method combining real-time variable forgetting factor recursive least squares (VFFRLS) and adaptive extended Kalman filter (AEKF) is proposed, and the unscented Kalman filter (UKF) method is used to calculate SOC in real time. Through comparative verification and analysis, this method owns good accuracy of model parameters identification and robustness in three commonly used equivalent circuit models. Finally, experiments under dynamic stress test (DST) cycles show that the root mean square error of terminal voltage and SOC are 0.19% and 0.07% respectively in dual polarization model with VFFRLS, which proves that the proposed method can significantly improve the estimation accuracy of SOC.