Online Parameter Identification Research Articles

АНАЛІЗ ВПЛИВУ ІНДИВІДУАЛЬНИХ ХАРАКТЕРИСТИК КОМІРОК LiFePO4 НА ПАРАМЕТРИ РОБОТИ АКУМУЛЯТОРНИХ ЗБІРОК

The article analyzes the influence of the individual characteristics of lithium-iron-phosphate (LiFePO4) cells on the overall efficiency, stability and duration of operation of battery assemblies. The influence of such parameters as internal resistance, polarization resistance and capacity on the operation of battery assemblies under different load regimes and changes in temperature conditions was studied. It was found that these parameters can vary significantly between cells within the same assembly, which leads to an imbalance during the operation of the battery system. Such an imbalance causes an uneven distribution of charge between the cells, which reduces the overall performance of the system, accelerates the degradation of the cells and shortens their service life. To overcome these problems, the use of active balancing methods based on Unscented Kalman Filter (UKF) algorithms was proposed. The application of UKF allows for dynamic evaluation of the state of charge (SOC) of each cell and adjustment of system parameters in real time, which provides more accurate management of battery assemblies in conditions of variable external factors such as temperature and current loads. Special attention is paid to online identification of battery parameters, such as polarization resistance and capacity, which change over time due to cell aging. The conducted studies confirmed that constant monitoring of temperature regimes is critically important for maintaining stable operation of battery cells, preventing overheating and ensuring uniform load distribution. The performed simulation confirmed the effectiveness of the application of adaptive methods of balancing and temperature control, which allows to increase the reliability and overall performance of battery assemblies.

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

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

Enhanced Second-Order RC Equivalent Circuit Model with Hybrid Offline–Online Parameter Identification for Accurate SoC Estimation in Electric Vehicles under Varying Temperature Conditions

Accurate estimation of State-of-Charge (SoC) is essential for ensuring the safe and efficient operation of electric vehicles (EVs). Currently, second-order RC equivalent circuit models do not account for the influence of battery charging and discharging states on battery parameters. Additionally, offline parameter identification becomes inaccurate as the battery ages. Online identification requires real-time parameter updates during the SoC estimation process, which increases the computational complexity and reduces the computational efficiency of real vehicle Battery Management System (BMS) chips. To address these issues, this paper proposes a SoC estimation method that combines online and offline identification based on an optimized second-order RC equivalent circuit model, which distinguishes it from existing methods in the field. On the basis of the traditional second-order RC model, the Ohmic resistance (R0), polarization resistance (R1), polarization capacitance (C1), diffusion resistance (R2), and diffusion capacitance (C2) during the charging and discharging processes are discussed separately. R0, which does not change frequently, is identified offline, while R1, R2, C1, and C2, which dynamically change with time and current, are identified online. To thoroughly verify the feasibility of the proposed method, we construct an SoC estimation test bench, which allows us to adjust the battery’s surface temperature in real time using a temperature control chamber. Experimental validation under Federal Urban Driving Schedule (FUDS) (−10 °C to 45 °C, 80% battery capacity) and Dynamic Stress Test (DST) (−10 °C to 45 °C, 8% battery capacity) conditions demonstrate that our method improves SoC estimation accuracy by 16.28% under FUDS and 28.2% under DST compared to the improved GRU-based transfer learning method, while maintaining system SoC estimation efficiency.

State of Charge Estimation in Batteries for Electric Vehicle Based on Levenberg–Marquardt Algorithm and Kalman Filter

A new optimization method for estimating the State of Charge (SOC) of battery charge state is proposed. This method incorporates the Levenberg–Marquardt Algorithm (LMA) for online parameter identification and the Extended Kalman Filter (EKF) for SOC. On the one hand, the LMA efficiently alleviates the ’Data saturation’ problem experienced by least squares methods by dynamically adjusting weights of data. On the other hand, the EKF improves the robustness and adaptability of SOC estimation. Simulation results under Hybrid Pulse Power Characteristic (HPPC) conditions demonstrate that this new approach offers superior performance in SOC estimation in batteries for electric vehicles compared to existing methods, with better tracking of the true SOC curve, reduced estimation error, and improved convergence.

Deep learning method for online parameter identification of lithium-ion batteries using electrochemical synthetic data

Accurately sensing the internal state of lithium-ion batteries and identifying parameters is crucial for developing effective battery safety and health management strategies. With the advancement of artificial intelligence, the integration of deep learning (DL) and electrochemical techniques has ushered in new avenues for high-level battery management. However, gaining proprietary battery datasets for neural network (NN) training is difficult at limited data scenarios. To address this problem, a DL method based on synthetic data and convolutional neural network (CNN) is proposed to achieve online parameter identification. Firstly, an electrochemical model (EM) considering the impact of solid electrolyte interface (SEI) film growth on battery performance is established. Then, nine features strongly correlated with electrochemical parameters are extracted from charge and discharge segments for the construction of CNN. Based on offline identified parameters, substantial electrochemical parameter-feature datasets are further generated through polynomial fitting and model calculations, followed by the application of principal component analysis and density-based spatial clustering of applications with noise (PCA-DBSCAN) to filter synthesized data for network training. Finally, the developed online parameter identification method is experimentally validated using full life cycle data of three cobalt-acid lithium (LiCoO2) batteries, confirming reasonable accuracy of the identified battery electrochemical parameters.

A compensation method for PMSM sensorless control with parameter identification considering SMO observation error

AbstractSensorless control of permanent magnet synchronous motor (PMSM) can increase the reliability of electric actuators of more electrical aircraft. Numerical online parameter estimation method will enhance the performance for sensorless control methods based on sliding mode observer (SMO). However, numerical online parameter identification methods typically assume zero rotor position estimation error to ensure full rank of the identification equation set. This assumption actually leads to errors in parameter identification, resulting in steady‐state errors in rotor position estimation. This paper focuses on a compensation method for PMSM online parameter identification that takes into account rotor position estimation error. This method is used for sensorless control based on SMO, using model reference adaptive systems for online parameter identification, and no additional parameter adjustment required. Validation is conducted on a PMSM experimental platform with maximum load of 15kW. The experimental results of 1000 r/min steady state operation proved the effectiveness of the method, and the dynamic experiments with 5 and 15kW loads proved that the method has no significant adverse effect on the system dynamics.

Model-Free Composite Disturbance Rejection Control for Dynamic Wireless Charging System Based on Online Parameter Identification

Model-Free Composite Disturbance Rejection Control for Dynamic Wireless Charging System Based on Online Parameter Identification

Online parameter identification based predictive pressure control for train electro-pneumatic braking systems with thermal effect

The electro-pneumatic braking system with ON/OFF solenoid valves has been widely used in trains due to its advantages and superiority. The undesirable impact of the thermal effect on the electro-pneumatic braking system leads to frequent valve switching, degradation of the pressure tracking performance and sometimes instability. This article presents an adaptive model predictive control approach to solve the pressure control problem under temperature uncertainty based on a switched unscented Kalman filter. First, a nonlinear switched dynamical model with the uncertain temperature parameter is derived for the electro-pneumatic braking system by comprehensively integrating its nonlinear, discontinuous dynamics and thermal effect. Using a switched unscented Kalman filter on the presented model of the system, the temperature parameter is accurately estimated to improve the model’s accuracy. Based on the corrected system model and the designed adaptive model predictive control method, the pressure tracking performance and the valves’ switchings of the electro-pneumatic braking system are improved, and the stability is guaranteed. The simulations and the experiments conducted for a braking system prototype confirm the performance validity of the proposed method.

Parameter Identification of Maritime Vessel Rudder PMSM Based on Extended Kalman Particle Filter Algorithm

To address the issue of system parameter variations during the operation of a maritime light vessel rudder permanent magnet synchronous motor (PMSM), an extended Kalman particle filter (EKPF) algorithm that combines a particle filter (PF) with an extended Kalman filter (EKF) is proposed in this paper. This approach enables the online identification of motor resistance and inductance. For highly nonlinear problems that are challenging for traditional methods such as Kalman filtering, this algorithm is typically a statistical and effective estimation method that usually yields good results. Firstly, a standard linear discrete parameter identification model is established for a PMSM. Secondly, the PF algorithm based on Bayesian state estimation as a foundation for subsequent research is derived. Thirdly, the advantages and limitations of the PF algorithm are analyzed, addressing issues such as sample degeneracy, by integrating it with the Kalman filtering algorithm. Specifically, the EKPF algorithm for online parameter identification is employed. Finally, the identification model within MATLAB/Simulink is constructed and the simulation studies are executed to ascertain the viability of our suggested algorithm. The outcomes from these simulations indicate that the proposed EKPF algorithm identifies resistance and inductance values both swiftly and precisely, markedly boosting the robustness and enhancing the control efficacy of the PMSM.

SOC estimation of lithium battery based on online parameter identification and an improved particle filter algorithm

SOC estimation of lithium battery based on online parameter identification and an improved particle filter algorithm

An Equivalent Siding Mode Observer for Electric Vehicle Lithium Battery SOC Estimation

Accurate state of charge (SOC) estimation for lithium-ion batteries is essential to guarantee long-term stable operation of electric vehicles. In this study, an equivalent sliding mode observer (ESMO) is proposed to estimate the battery SOC. First, a sliding mode observer (SMO) was designed with Walcott-Zak structure to increase the sliding region. Next, a controlled equivalent function was used to replace sign function in the SMO, which can lessen chattering issue and increase system robustness. Furthermore, this study performs online parameter identification of a second-order resistance capacitor equivalent circuit model using the forgetting factor recursive least squares approach. Lastly, the experiments under dynamic current conditions were conducted to verify the proposed ESMO. The results show that the mean square error of the ESMO is decreased to 0.5%, which implies that the proposed ESMO can estimate the SOC with higher accuracy compared to the traditional SMO.

Research on Parameter Identification of Induction Motor Based on Model Reference Adaptive System

Abstract This paper proposes a novel online parameter identification method based on model-reference adaptive systems to achieve more precise control of induction motors. This method efficiently and accurately obtains the parameters of induction motors during operation, facilitating effective control. Experimental validation is conducted on a hardware-in-the-loop test platform, demonstrating that the proposed online parameter identification method for induction motors achieves accurate parameter estimation with the advantages of high identification speed and precision. Control experiments on induction motors using the identified parameters further validate the feasibility of the proposed method for online parameter identification.

A hybrid data-driven method optimized by physical rules for online state collaborative estimation of lithium-ion batteries

Accurate online estimation of state of charge (SOC) and state of health (SOH) is crucial for safe operation and reasonable planning of battery management system (BMS). However, battery internal states are unmeasurable directly and coupled with each other, which results in serious difficulties on multi-states accurate estimation. Considering SOC estimation is deeply influenced by SOH, this paper proposes a collaborative estimation method combined machine learning algorithm with simple physical rule. Integrated the merits of the above methods, it can not only improve the estimating accuracy but also low computational burden to a large extent. Firstly, SOH acts as input of SOC in collaboration, which means SOH should have been already known before SOC estimation operation. Thus, SOH is creatively predicted based on gate recurrent unit network in ahead of SOC estimation. Subsequently, the well-trained SOC data-driven model is combined with the ampere hour (Ah) integration, which act as observation and state equation respectively in the particle filtering algorithm to realize the final estimation of SOC. This way, the sole data-driven model is well improved under physics restriction without any other computational procedure, such as online parameter identification. Simultaneously, the predicted SOH is used to fill the Ah integration expression to realize the collaboration between SOC and SOH. What's more, the SOH is further corrected by the incremental capacity peak at the checkpoint, thereby further reducing the estimation deviation. The experimental result on battery shows that the SOH estimated error is below 0.3Ah (the rated capacity is 32.5Ah) and SOC error is less than 1.5 %. What's more, the generalization capacity of the proposed method is verified on different battery types based on public datasets. Obviously, the proposed method has fairly satisfactory property and performance and can totally support its promotion into state online evaluation of battery energy storage.

Revolutionizing motor dysfunction treatment: A novel closed-loop electrical stimulator guided by multiple motor tasks with predictive control

Functional electrical stimulation (FES) has been demonstrated as a viable method for addressing motor dysfunction in individuals affected by stroke, spinal cord injury, and other etiologies. By eliciting muscle contractions to facilitate joint movements, FES plays a crucial role in fostering the restoration of motor function compromised nervous system. In response to the challenge of muscle fatigue associated with conventional FES protocols, a novel biofeedback electrical stimulator incorporating multi-motor tasks and predictive control algorithms has been developed to enable adaptive modulation of stimulation parameters. The study initially establishes a Hammerstein model for the stimulated muscle group, representing a time-varying relationship between the stimulation pulse width and the root mean square (RMS) of the surface electromyography (sEMG). An online parameter identification algorithm utilizing recursive least squares is employed to estimate the time-varying parameters of the Hammerstein model. Predictive control is then implemented through feedback corrections based on the comparison between predicted and actual outputs, guided by an optimization objective function. The integration of predictive control and roll optimization enables closed-loop control of muscle stimulation. The motor training tasks of elbow flexion and extension, wrist flexion and extension, and five-finger grasping were selected for experimental validation. The results indicate that the model parameters were accurately identified, with a RMS error of 3.83 % between actual and predicted values. Furthermore, the predictive control algorithm, based on the motor tasks, effectively adjusted the stimulus parameters to ensure that the stimulated muscle groups can achieve the desired sEMG characteristic trajectory. The biofeedback electrical stimulator that was developed has the potential to assist patients experiencing motor dysfunction in achieving the appropriate joint movements. This research provides a foundation for a novel intelligent electrical stimulation model.

Experimental Analysis of Smart Drilling for the Furniture Industry in the Era of Industry 4.0.

The fact is that hundreds of holes are drilled in the assembly process of furniture sets, so intelligent drilling is a key element in maximizing efficiency. Increasing the feed rate or the cutting speed in materials characterized by a higher machinability index is necessary. Smart drilling, that is, the real-time adjustment of the cutting parameters, requires the evolution of cutting process variables. In addition, it is necessary to control and adjust the processing parameters in real time. Machinability is one of the most important technological properties in the machining process, enabling the determination of the material's susceptibility to machining. One of the machinability indicators is the unit cutting resistance. This article proposes a method of material identification using the short-time Fourier transform in order to automatically adjust cutting parameters during drilling based on force signals, cutting torque and acceleration signals. In the tests, four types of wood-based materials were used as the processed material: medium-density fiberboard, chipboard, plywood board and high-pressure laminate. Holes with a diameter of 10 mm were drilled in the test materials, with variable feed rate, cutting speed and thickness of cutting layer. An innovative method for determining the value of unit cutting resistance was proposed. The results obtained were used to determine the machinability index. Based on the test results, it was shown that both the selected signal measures in the time and frequency domains and the unit cutting resistance are constant for a given material of a workpiece and do not depend on the drilling process parameters. In this article, the methodology is proposed, which can be used as an intelligent technique to support the drilling process to detect the material being machined using data from sensors installed on the machine tool. The work proposes the fundamentals for material identification based on the analysis of force signals and the magnitude of force derivatives. The proposed methodology shows effectiveness, which proves that it can be used in intelligent drilling processes. Hybrid wood-based material structures consisting of different materials are becoming more and more common in building structures for strength, economic and environmental reasons. Due to the difference in the machinability of interconnected materials, cutting parameters must be optimized in real time during machining. Currently, with the rapid development of Industry 4.0, the on-line identification of parameters is becoming necessary to improve the process flow in industrial reality. The proposed methodology can be used as an intelligent technique to support the drilling process in order to detect the material being processed using data from sensors installed on the machine tool.

A novel least squares support vector machine-particle filter algorithm to estimate the state of energy of lithium-ion battery under a wide temperature range

The state of energy (SOE) is a key indicator for lithium-ion battery management systems (BMS). Based on the second-order resistance-capacitance equivalent circuit model and online parameter identification using the dynamic weights particle swarm optimization (DWPSO) method, a least-squares support vector machine-particle filter (LSSVM-PF) algorithm is proposed to construct a particle filter to estimate the SOE of a lithium-ion battery, and then transfer the resulting estimation error together with the experimentally measured voltage and current values to a trained LSSVM model, and use the LSSVM model to optimize the SOE estimates obtained by the PF algorithm twice to improve the accuracy of SOE estimation for lithium-ion batteries. The feasibility of the proposed algorithm is verified using two complex operating conditions and at three different temperatures. The validation results show that the maximum error of SOE estimation of the proposed algorithm is 0.0284 for a wide temperature range under Beijing Bus Dynamic Stress Test (BBDST) condition, and 0.0226 for a wide temperature range under Dynamic Stress Test (DST) condition. The proposed algorithm significantly improves the accuracy of SOE estimation and provides a reference for fundamental applications of lithium-ion batteries.

An improved Cauchy robust correction-sage Husa extended Kalman filtering algorithm for high-precision SOC estimation of Lithium-ion batteries in new energy vehicles

The accurate estimation of battery State of Charge (SOC) is a key technology in the research of electric vehicle battery management systems. In order to solve the problem of inaccurate noise estimation in nonlinear systems, an improved Cauchy robust correction-Sage Husa extended Kalman filtering (CRC-SHEKF) algorithm is proposed for high-precision SOC estimation of lithium-ion batteries in new energy vehicles. Considering the polarization effect of the battery, the FFRLS algorithm is used for online parameter identification of the Dual Polarization model. Using robust data correction methods, the Cauchy robust function is simplified for real-time correction of the covariance matrix Q of system state noise and the covariance matrix R of the observed noise in the filtering process and combined with SHEKF for SOC estimation. The experimental results show that under different temperature conditions and complex working conditions, the proposed CRC-SHEKF algorithm has the minimum mean absolute error (MAE), root mean square error (RMSE), and maximum error (MAX). Under the condition of the Beijing bus dynamic stress test (BBDST) at 15 °C, the MAE, RMSE, and MAX of the CRC-SHEKF algorithm are 0.392 %, 0.716 %, and 0.945 %, with the computing time of only 4.839 s. The algorithm proposed in this article has high accuracy and robustness, and has practical application value, providing a reference for the application of lithium battery condition monitoring.

Model predictive control for Vienna rectifier with constant frequency based on inductance parameters online identification

When the finite control set model predictive control (FCS-MPC) is applied to the three-level converter, there are problems such as large current harmonics, high requirements for the computing efficiency of the microcontroller, complex multi-objective optimization and limited output vector switching. In addition,the mismatch of inductance parameter may directly affect the observation accuracy of FCS-MPC. To solve the above problem,a double vector model predictive control strategy with constant frequency strategy based on the inductance parameters on-line identification is presented.First, a single objective cost function based on the direct power is constructed by optimizing the redundant vector which is selected to balance the neutral-point potential, the design of weighting factor is avoided.At the same time, in order to effectively reduce the grid current ripple under single vector modulation, space vector pulse width modulation (SVPWM) is realized by combining with zero vector control mode. And, a parameter identification method based on model reference adaptive system(MARS) is proposed to overcome the effect of model parameter mismatch.Finally, the results show that the proposed F-MPCCF has good steady-state and dynamic performance from the static, transient and neutral-point potential control.

Battery multi-time scale fractional-order modeling method for state of charge estimation adaptive to full parameters updating

The fractional-order theory has been successfully applied to battery modeling and state of charge (SOC) estimation thanks to the rapid development of smart energy storage and electric vehicles. The fractional-order model (FOM) has high nonlinearity, which makes it difficult to identify the parameters of the FOM, especially the online identification of the order. Aiming at the problem of parameter identification and SOC estimation of the FOM of battery, a multi-time scale fractional-order modeling method is proposed in this paper. Then, a multi-time scale parameter identification strategy based on feature separation is proposed, and two sub-filters are used to complete the online identification of parameters. Finally, a fractional-order multi-innovation unscented Kalman filtering (FO-MI-UKF) algorithm is proposed for SOC estimation to utilize the value of historical information better. Under dynamic stress test (DST) and Beijing bus dynamic stress test conditions (BBDST), compared with the single-time scale parameter identification algorithm and the single-innovation SOC estimation algorithm, the root mean square error of the estimation results is reduced by 13.3 % and 8.7 %, respectively. The experimental results verify the effectiveness of the modeling method and provide a new idea for fractional-order modeling.

An observer‐based composite identifier for online estimation of the Thévenin equivalent parameters of a power system

AbstractWe consider a Thévenin equivalent circuit capturing the dynamics of a power grid as seen from the point of common coupling with a power electronic converter and propose a solution to the problem of online identification of the corresponding circuit parameters. For this purpose, we first derive a linear regression model in the conventional abc coordinates that, unfortunately, suffers from severe lack of identifiability properties. In spite of this fact, we design a bounded observer‐based composite identifier that, requiring only local measurements and knowledge of the grid frequency, ensures global boundedness of the parameters estimation error and convergence to a practically small residual set. An extension that guarantees exponential convergence of these errors to zero, in the case that the grid is dominantly inductive, is further provided. The performance of the proposed identifier, which subsumes a conventional gradient descent algorithm, is illustrated via detailed computer simulations.