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On-board estimation of battery state of charge (SOC) plays a critical role in various functionalities performed by battery management systems (BMS) applicable to electric vehicles (EVs). The traditional approach of SOC estimation uses offline identification of battery model parameters as a function of SOC. It requires an update of SOC-dependent parameters in EVs run-time. Since battery dynamics or model parameters change as a function of state of health (SOH), identifying and updating these parameters online is a crucial challenge. Researchers have recently presented many techniques of online state estimation, but they are unsuitable for deployment due to constraints from the embedded point of view. This article presents a detailed investigation and analysis of battery model parameter sensitivity concerning the entire range of SOC and over the life cycle, followed by simplified model-based SOC estimation. First, the second-order equivalent circuit model with hysteresis is developed and validated. The sensitivity of model parameters is investigated using a state-of-the-art one-factor-at-a-time (OFAT) approach to classify parameters as high and low sensitive and to propose a simplified model considering the compromise between accuracy and embedded computations. The extended Kalman filter-based SOC estimation at different SOHs is designed. In the case of lithium-ion NCA battery, the proposed simplified model yields maximum SOC error of 2%, 1.47%, and 3.27% at SOH levels 92.12%, 89.36%, and 85.96%, respectively. Similarly, for lithium-ion LFP battery, the proposed simplified model yields a maximum SOC error of 1.5% when SOH is 100%, which demonstrates how a simplified model provides satisfactory results compared to traditional methods and is suitable for embedded deployment due to reduced computations in run-time. [ABSTRACT FROM AUTHOR]

The precise control of centrifugal hydrogen compressor (CHC) driven by high-speed permanent magnet synchronous motor (PMSM) is the key to the stable and efficient operation of hydrogen fuel cell (HFC). In this paper, an adaptive current deviation decoupling control (CDDC) strategy based on variable step size affine projection algorithm (VSS-APAs) is proposed to solve the high-speed PMSM control problem caused by current cross-coupling and parameter perturbation under complex operating conditions. Firstly, the step size is dynamically adjusted by VSS-APA through a normalized gradient descent to respond to system fluctuations, achieving rapid parameter identification during dynamic changes and accurate tracking in steady states. Secondly, stator inductance, magnetic flux linkage, stator resistance, and disturbance torque are identified in two-time-scale based on the characteristics of parameter changes, overcoming the rank deficiency in motor mathematical equations. Finally, utilizing online-identified motor parameters, the gains of the CDDC are adjusted, and compensation is applied for back electromotive force (BEMF) and disturbance torque, thereby ensuring the control accuracy of the motor under parameter perturbations and system disturbances. Experimental results demonstrate that the proposed method enhances the responsiveness and accuracy of the control strategy through efficient parameters identification. Consequently, it guarantees high-efficiency and stable performance of the CHC across different working conditions, markedly advancing the overall functionality and dependability of hydrogen energy system. • An adaptive current decoupling control scheme for high-speed permanent magnet synchronous motor is proposed. • A two-time-scale VSS-APAs for online identification of motor parameters is designed. • The effectiveness of this method is verified by experiments. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents a novel method for real-time identification of four parameters of the permanent magnet synchronous machines (PMSM) namely stator resistance, d-axis inductance, q-axis inductance and the rotor flux linkage. The proposed method is based on the utilization of the deep neural network to solve the problems of the existing model-based parameter estimation methods, which are caused by the non-linearity of the inverter and the inaccuracy of the measured rotor position. Extensive numerical simulations and experimental studies have been conducted to evaluate the robustness and the accuracy of the proposed online parameters identification solution, compared with the conventional methods such as recursive least square, extended Kalman filter and Adaline neural network.

In this study, an improved adaptive robust unscented Kalman Filter (ARUKF) is proposed for an accurate state-of-charge (SOC) estimation of battery management system (BMS) in electric vehicles (EV). The extended Kalman Filter (EKF) algorithm is first used to achieve online identification of the model parameters. Subsequently, the identified parameters obtained from the EKF are processed to obtain the SOC of the batteries using a multi-innovation adaptive robust unscented Kalman filter (MIARUKF), developed by the ARUKF based on the principle of multi-innovation. Co-estimation of parameters and SOC is ultimately achieved. The co-estimation algorithm EKF-MIARUKF uses a multi-timescale framework with model parameters estimated on a slow timescale and the SOC estimated on a fast timescale. The EKF-MIARUKF integrates the advantages of multiple Kalman filters and eliminates the disadvantages of a single Kalman filter. The proposed algorithm outperforms other algorithms in terms of accuracy because the average root mean square error (RMSE) and the mean absolute error (MAE) of the SOC estimation were the smallest under three dynamic conditions. [ABSTRACT FROM AUTHOR]

ObjectiveIn order to overcome the influence of the nonlinear time-varying characteristics of gas turbines on dynamic control and performance monitoring，this paper combines the time series memory and nonlinear relation expression ability of a long short-term memory neural network (LSTM) with the interval probability estimation ability of Gaussian process regression (GPR) to propose an online parameter identification algorithm for the key dynamic parameters of gas turbines based on an LSTM and GPR-based hybrid deep learning model (LSTM-GPR). MethodsFirst, the dynamic mechanism model of a gas turbine is established, and a large amount of training data is generated by taking fuel calorific value, compressor efficiency and load power moment as the parameters to be identified. Next, the parameter identification network model of LSTM-GPR is constructed, and the training data is input for network training and weight coefficient learning. Finally, the trained LSTM-GPR hybrid deep learning model is used to identify the dynamic operating parameters of the gas turbine model online, and the identification results are analyzed to verify the effectiveness of the proposed algorithm.ResultsThe simulation results show that the online identification results of the proposed LSTM-GPR hybrid model algorithm are accurate, with a recognition error of less than 1% and good real-time performance. Compared with the LSTM single model, the proposed algorithm can obtain a better mean estimation effect and provide a reliable confidence interval range. ConclusionsThe LSTM-GPR hybrid algorithm can be effectively applied to the online parameter identification of a gas turbine model, laying a foundation for its further application to the dynamic operation parameter identification of practical units.

Accurate estimation of PMSM parameters is beneficial to the high performance operation of PMSM. In order to prevent PSO from falling into the local optimal solution in the PMSM parameter identification process, so as to improve the accuracy of identification results, a modified fuzzy particle swarm optimization (MDFPSO) is proposed, which changes the speed of each particle from only affected by the optimal particle to affected by the surrounding particles, This improvement guarantees the identification accuracy of the algorithm, and introduce the convergence factor to ensure that the MDFPSO can converge. Simulation results show that the MDFPSO algorithm is effective in PMSM parameter identification.

State-of-charge (SoC) estimation is one of the core functions of battery energy management systems. An accurate SoC estimation can guarantee the safe and reliable operation of the batteries system. In order to overcome the practical problems of low accuracy, noise uncertainty, poor robustness, and adaptability in parameter identification and SoC estimation of lithium-ion batteries, this paper proposes a joint estimation method based on the adaptive extended Kalman filter (AEKF) algorithm and the adaptive unscented Kalman filter (AUKF) algorithm in multiple time scales for 18,650 ternary lithium-ion batteries. Based on the slowly varying characteristics of lithium-ion batteries' parameters and the quickly varying characteristics of the SoC parameter, firstly, the AEKF algorithm was used to online identify the parameters of the model of batteries with a macroscopic time scale. Secondly, the identified parameters were applied to the AUKF algorithm for SoC estimation of lithium-ion batteries with a microscopic time scale. Finally, the comparative simulation experiments were implemented, and the experimental results show the proposed joint algorithm has higher accuracy, adaptivity, robustness, and self-correction capability compared with the conventional algorithm. [ABSTRACT FROM AUTHOR]

Summary: Because of the common data redundancy phenomenon in the current least‐squares parameter identification algorithm and the complex offline parameter identification process, this research innovatively proposes a Limited Memory Multi‐Innovation Least Squares (LM‐MILS) ternary lithium‐ion battery (LIB) parameter identification algorithm that uses a limited set of data to estimate model parameters and attenuates the effects of old data. To improve the parameter fidelity of the equivalent circuit model (ECM) of the LIB, considering that the open‐circuit voltage of the lithium‐ion battery will gradually decrease with the self‐discharge when it is not in use, based on a large number of experiments, a model considering the self‐discharge of the LIB is constructed. The experimental results show that the self‐discharge effect‐2‐RC (SDE‐2‐RC) model can achieve higher accuracy in simulating the working state of the battery and the peak error of the simulated voltage is only 0.04342 V, and the accuracy can reach more than 98.966%. Using LM‐MILS and adaptive Kalman filtering algorithm (AEKF) for the state of charge (SOC) estimation, the results show that the algorithm has a fast convergence speed and strong tracking performance. The maximum SOC estimation errors in HPPC, DST, and BBDST three operating conditions are 0.00929, 0.01273, and 0.01002, respectively. The fluctuation range is small, and the maximum estimation error is less than 2%, which verifies that the improved parameter identification algorithm has good performance in improving the SOC estimation accuracy of LIB. [ABSTRACT FROM AUTHOR]

A novel online multiparameter identification method is proposed for interior permanent magnet (IPM) machines considering cross-coupling magnetic saturation. The identification method is based on the combination of small-signal impedance model and fundamental frequency signal model under dq-axes. To analyze the nonlinearity of IPM machines, cross-coupling magnetic saturation effect is introduced into online multiparameter identification for the first time. Based on this model, a phase compensation strategy and orthogonal decomposition in phasor domain is proposed to decouple the complicated mathematical model into four linear equations. By injecting voltage small-signals into dq-axes, multiparameter of IPM machine including adjusted apparent inductance, incremental self-inductance, incremental mutual-inductance can be identified online in a wide operation region. During this process, no additional computing equipment is required. The proposed method is carried out on a tested IPM machine and verified by the finite-element analysis. [ABSTRACT FROM AUTHOR]

Interior permanent magnet synchronous motor (IPMSM) is applied in automotive drive because of its high efficiency and high power density. The accurate torque control of motor, however, is still subject to further investigation and improvement. In order to improve the performance of IPMSM applied in electric vehicles, a feedback torque control method based on online parameter identification is developed in this research. In order to control the instantaneous electromagnetic torque accurately, stator resistance, rotor flux, and dq-axis inductance are identified simultaneously with a recursive least square algorithm. In this process, sinusoidal disturbance current is injected into d-axis to solve the rank-deficient problem. Furthermore, a simulation model is developed based on this torque control method. The simulation results show that the torque control method developed in this research can perform better than the torque control method based on fixed parameters. On the other hand, a test rig is developed in this research to verify the feasibility of torque control method and the effectiveness of simulation model. The measured results show that these parameters can be identified accurately with the online parameter identification method developed in this research, and the robustness of this online parameter identification method can be verified in the experiment. Furthermore, the torque control method is verified with this test rig, and the results show that the measured torque can accurately follow the torque command rapidly with little fluctuation, indicating that the method developed in this research has the features of high accuracy, high robustness and short response time.

Summary: Aiming at the problems of time‐varying battery parameters and inaccurate estimations of state of charge (SOC) and state of health (SOH), a joint estimation algorithm of SOC and SOH is proposed. A particle filter algorithm is used to identify the parameters online on the basis of a second‐order equivalent circuit model. The algorithm feasibility is verified through the terminal voltage estimation accuracy. Considering that an accurate SOH is one of the foundations to achieve an accurate SOC estimation, a dual particle filter is used to jointly estimate SOC and SOH. Under different test conditions, the effect of different initial values (initial SOC and capacity), temperatures, operation conditions, particle number, and model parameters on the estimation accuracy and robustness is compared and analyzed. The effectiveness of the proposed algorithm is validated by experimental data under different operation conditions. Experimental results show that the online particle filter algorithm can well predict the dynamic battery model parameters. The proposed algorithm has high robustness and a good tracking effect when estimating SOC with a mean absolute error of less than 1.3%, a root mean square error of less than 1%, and a tracking terminal voltage. [ABSTRACT FROM AUTHOR]

In order to improve the estimation accuracy of the state of charge (SOC) of lithium iron phosphate power batteries for vehicles, this paper studies the prominent hysteresis phenomenon in the relationship between the state of charge and the open circuit voltage (OCV) curve of the lithium iron phosphate battery. Through the hysteresis characteristic test of the battery, the corresponding SOC-OCV data when the battery is charged or discharged from different SOC states are analyzed. According to the approximation trend of the hysteresis main loop curve by the data points, a differential equation model for approximately solving the charge or discharge hysteresis small loop curve under any SOC state is established, and the adjustment parameters of the model are analyzed and debugged in sections. Then, based on the second-order Thevenin equivalent circuit model, the forgetting factor recursive least squares method is used to identify the model parameters online. When deriving the relationship between the OCV and SOC, according to the state of charge and discharge and the current SOC value, the approximate model of the real hysteresis small loop curve in the current state is solved in real time, and the extended Kalman recursion algorithm is substituted to correct the corresponding relationship between the OCV and SOC. Finally, the integrated forgetting factor recursive least squares online parameter identification and extended Kalman filter to correct the SOC-OCV hysteresis relationship in real time considering the hysteresis characteristics are used to complete the real-time estimation of the SOC of the lithium iron phosphate battery. The synthesis algorithm proposed in this paper and the Kalman filter algorithm without considering the hysteresis characteristics are compared and verified under the Dynamic Stress Test (DST) data. Based on the method proposed in this paper, the maximum error of terminal voltage is 0.86%, the average error of terminal voltage is 0.021%, the root mean square error (RMSE) of terminal voltage is 0.042%, the maximum error of SOC estimation is 1.22%, the average error of SOC estimation is 0.41%, the average error of SOC estimation is 0.41%, and the RMSE of SOC estimation is 0.57%. The results show that the comprehensive algorithm proposed in this paper has higher accuracy in both terminal voltage following and SOC estimation. [ABSTRACT FROM AUTHOR]

Model Predictive Control (MPC) based on Discrete Space Vector Modulation (DSVM) has the advantages of simple mathematical model and fast dynamic response. It is widely used in permanent magnet synchronous motor (PMSM). Additionally, the control performance of DSVM-MPC is influenced by the accuracy of motor parameters and the select speed of optimal voltage vector. In order to identify motor parameters accurately, model predictive control for PMSM based on discrete space vector modulation with recursive least squares (RLS) parameter identification is proposed in this paper. Additionally, a method to preselect candidate voltage vectors is proposed to select the optimal voltage vector more quickly. The simulation model of RLS-DSVM-MPC is established to simulate the influence of different parameters on PMSM performance. The simulation results show that model predictive control for PMSM based on discrete space vector modulation with RLS parameter identification has a better control performance than that of without RLS parameter identification. [ABSTRACT FROM AUTHOR]

The switched reluctance machine has been an attractive candidate for many applications owing to its simple design and low construction costs, without the use of permanent magnets. However, the double saliency of its stator and rotor poles results in noise-causing torque ripples. And although advanced torque ripple minimization control techniques exist, they rely on modeling the machine, which in turn requires specialized offline experimental setups or online (during operation) parameter identification techniques. To date, existing online techniques are iterative without proof of convergence, do not provide all model parameters, and/or rely on a priori information that can change after the machine is commissioned. In this work, an online parameter identification method is developed with a new empirical model of its flux linkage and electromagnetic torque, to provide a complete nonlinear model of the machine. With two seconds of data collected online, all electrical and mechanical parameters are identified using a non-iterative algorithm, and so it does not pose a risk of divergence. Therefore, parameter identification can be reliably and frequently carried out at different operating conditions as the machine ages for diagnostics. Also, the resulting model is designed to be used by advanced torque ripple minimization control techniques. The implementation procedure is detailed along with simulation results to demonstrate its efficacy.

Permanent magnet synchronous motors due to its high efficiency and power density, reliable performance and simple construction, industrially used. One of the problems with these Motor need accurate information to control its speed and position. Recently, because of the difficulties of the speed sensors, speed estimation is used instead of measuring it. In this paper, the state dependent model reference adaptive controller based on pseudo linearization is used to control the permanent magnet synchronous motor that its parameters are determined based on Lyapunov theory. This controller show good results by production control law, despite changing circumstances and maintain system stability despite external disturbances. Also due to uncertainty in the estimation of the position and speed of motor parameters, high impact, online identification of these parameters is necessary. In this paper, adaptive augmented observer is used to estimate speed and identify motor parameters online. The main advantage of this estimator is that speed and load torque estimation and identification of parameters are simultaneously thus volume and time calculations are reduced. The simulation results show convenient tracking of desired speed with the load torque, changing the motor parameters and the speed reference.

Copyright of Electric Machines & Control / Dianji Yu Kongzhi Xuebao is the property of Electric Machines & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article presents a joint state-of-charge (SOC) and state-of-available-power (SOAP) estimation method based on online battery model parameter identification. First, the SOAP of lithium-ion batteries is analyzed thoroughly, and a safe operating area border-based (SOAB-based) SOAP estimation is proposed. Second, based on the adaptive battery-state estimator (ABSE) and improved ABSE, a joint SOC and SOAB-based SOAP estimation method is proposed. The joint estimation results show that the improved ABSE achieves higher accuracy than the ABSE at different battery aging states. The open-loop accuracy evaluation results show that the improved ABSE identifies the battery model parameters more accurately, and the ABSE algorithm error source lies in its identified Rp being much higher than the actual value when the battery is charged/discharged at a high current. The ABSE does not consider the influence of load current on the equivalent circuit model parameters, so it is not suitable for SOAP estimation in theory. The improved ABSE proposed by our team can eliminate this modeling error, identify the battery model parameters, and estimate the SOC and SOAB-based SOAP more accurately. This improved ABSE is an effective algorithm for estimating the battery state when the battery is charged/discharged with a high current. [ABSTRACT FROM AUTHOR]

We propose a hybrid strategy for modeling non-linear thermoacoustic phenomena, e.g. limit-cycle (LC) oscillations, in can-annular combustion systems. The suggested model structure comprises a compressible CFD simulation limited to the burner/flame zone of one single can, coupled to a low-order model (LOM) representing the remaining combustor. In order to employ the suggested strategy for modeling non-linear phenomena like LC oscillations, the LOM must capture non-linear flame dynamics in the cans, which are not resolved by CFD. Instead of identifying such non-linear flame models in preliminary simulations, we aim at learning the non-linear dynamics "on-the-fly", while simulating the self-excited system under consideration. Based on the observation of flame dynamics in the CFD domain, the parameters of the employed non-linear models are estimated during run time. The present study reveals that block-oriented models, which comprise a linear dynamic part followed by a static non-linear function, are well suited for this purpose. The proposed hybrid model is applied to a laminar can-annular combustor. Results agree well with the monolithic CFD simulation of the entire combustor, while the computational cost is drastically reduced. The employed flame models, whose parameters are identified during the simulation of the self-excited LC oscillation, represent well the relevant non-linear dynamics of the considered flame. [ABSTRACT FROM AUTHOR]

Online identification of a battery model can capture the parameter variations in real time, thus, providing an accurate model under various conditions, such as a wide temperature range, different aging degree in electric vehicle application. However, the measurement noise and sampling asynchronization of voltage and current are usually inevitable in the battery management system (BMS), which can lead to a large identification error. In this article, the mechanism of sampling asynchronization and measurement noise is analyzed first, and then the identification sensitivity analyses on noise and sampling asynchronization are carried out. Simulation results indicate that even a small fluctuation as small as 5 mV or the sampling latency between voltage and current within 10 mS can cause relatively large identification errors, especially for polarization parameters. In order to guarantee the online identification accuracy and robustness in the BMS application, an improved robust recursive least-squares (RLS) algorithm with adaptive outlier boundary is proposed to eliminate the input outlier caused by sampling latency and suppress the effect of measurement noise utilizing the bias compensation method. The experimental results demonstrate that the proposed method can provide sufficient accuracy and robustness under noise interference and sampling latency in BMS application. [ABSTRACT FROM AUTHOR]

Ship maneuverability prediction accuracy depends on the accuracy of ship motion model parameter identification. To solve the problem of parameter identification of nonlinear ship motion model, this paper proposes a online parameter identification algorithm of maximum likelihood multi-innovation recursive least squares (ML-MI-RLS) for ship motion model parameter identification. To solve the parameter drift phenomenon, the improved gray wolf optimization (IGWO) algorithm is proposed to optimize the parameter identification results. The combination of system identification and intelligent optimization algorithm not only solves the parameter drift problem of system identification, but also compensates for the lack of real-time performance of existing algorithms. The effectiveness of the ML-MI-RLS algorithm is verified by parameter identification simulations. The online identification performance of the algorithm is verified by varying the ship maneuverability parameters simulation. The proposed method is verified to have excellent performance by ship maneuverability prediction simulation. • This study combines system identification with intelligent algorithms to solve the ship parameter identification problem. • An ML-MI-RLS algorithm is proposed for ship motion model parameter identification. • An Improved Gray Wolf optimization algorithm is proposed to improve the parameter drift phenomenon. • Simulation data validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Summary: Accurate prediction of the state of health (SOH) of Li‐ion battery has an important role in the estimation of battery state of charge (SOC), which can not only improve the efficiency of battery usage but also ensure its safety performance. The battery capacity will decrease with the increase of charge and discharge times, while the internal resistance will become larger, which will affect battery management. The capacity attenuation characteristics of Li‐ion batteries are analyzed by aging experiment. Based on the equivalent circuit model and online parameter identification, a novel adaptive dual extended Kalman filter algorithm is proposed to consider the influence of the battery SOH on the estimation of the battery SOC, and the SOC and SOH of the Li‐ion battery are estimated collaboratively. The feasibility and accuracy of the model and algorithm are verified by experiments. The results show that the algorithm has good convergence and tracking. The maximum error in the estimation of the SOC is 2.03%, and the maximum error of the Ohmic resistance is 15.3%. It can better evaluate the SOH and SOC of Li‐ion battery and reduce the dependence on experimental data, providing a reference for the efficient management of Li‐ion batteries. [ABSTRACT FROM AUTHOR]

This article presents a set of algorithms for the estimation of state of charge, specifically deployed for lithium-ion batteries. These algorithms are based on appropriate battery models. These models can be developed having different levels of accuracy, also including the possibility to correctly represent the hysteresis voltage behaviour of the selected lithium cells. In addition, different identification methods of the battery model parameters may also be considered, considering tabulated parameters, calibrated in previous tests, or online parametrization tools. State of charge is then evaluated using non-linear Kalman filter techniques. Effectiveness of identification methods, also with the performance offered by Kalman filter itself, has been accurately evaluated through experimental tests. To verify the robustness of the proposed algorithms, some disturbances were introduced and evaluation was also conducted at different state of charge initial conditions and sampling times. [ABSTRACT FROM AUTHOR]

In order to improve the estimation accuracy of the state of charge (SOC) of lithium iron phosphate power batteries for vehicles, this paper studies the prominent hysteresis phenomenon in the relationship between the state of charge and the open circuit voltage (OCV) curve of the lithium iron phosphate battery. Through the hysteresis characteristic test of the battery, the corresponding SOC-OCV data when the battery is charged or discharged from different SOC states are analyzed. According to the approximation trend of the hysteresis main loop curve by the data points, a differential equation model for approximately solving the charge or discharge hysteresis small loop curve under any SOC state is established, and the adjustment parameters of the model are analyzed and debugged in sections. Then, based on the second-order Thevenin equivalent circuit model, the forgetting factor recursive least squares method is used to identify the model parameters online. When deriving the relationship between the OCV and SOC, according to the state of charge and discharge and the current SOC value, the approximate model of the real hysteresis small loop curve in the current state is solved in real time, and the extended Kalman recursion algorithm is substituted to correct the corresponding relationship between the OCV and SOC. Finally, the integrated forgetting factor recursive least squares online parameter identification and extended Kalman filter to correct the SOC-OCV hysteresis relationship in real time considering the hysteresis characteristics are used to complete the real-time estimation of the SOC of the lithium iron phosphate battery. The synthesis algorithm proposed in this paper and the Kalman filter algorithm without considering the hysteresis characteristics are compared and verified under the Dynamic Stress Test (DST) data. Based on the method proposed in this paper, the maximum error of terminal voltage is 0.86%, the average error of terminal voltage is 0.021%, the root mean square error (RMSE) of terminal voltage is 0.042%, the maximum error of SOC estimation is 1.22%, the average error of SOC estimation is 0.41%, the average error of SOC estimation is 0.41%, and the RMSE of SOC estimation is 0.57%. The results show that the comprehensive algorithm proposed in this paper has higher accuracy in both terminal voltage following and SOC estimation.

Existing online motor parameter identification methods mostly depend on the fundamental frequency voltage equations, which lead to the unsatisfactory identification effect at low current and low speed operation. This article proposes a parameter identification method based on the high frequency (HF) equivalent impedance model of permanent magnet synchronous motor with HF signal injection at both the dq-axes. This method identifies the resistance and the dq-axis inductances offline and online, along with the flux linkage online. In order to improve the identification accuracy, the parameter sensitivity analysis-based algorithm is proposed to detect the resistance and the flux linkage. Meanwhile, the inverter nonlinearities and the HF influence on parameter identification are compensated effectively. In order not to affect the normal operation of the motor drive, the selection of the amplitude, and the frequency of the injected signal is investigated. The proposed method is validated on a 2.2-kW motor and confirmed by finite element analysis. The experimental results show the good identification effect in different operation conditions. [ABSTRACT FROM AUTHOR]

• Elements of system synchronization and adaptive control theories are used for system identification. • A sufficient convergence condition for the estimation function is proposed. • A technique which uses available initial estimations is proposed to improve the convergence speed. • The efficiency of the proposed method for online identification and damage detection of civil structures is shown. In this paper a new method for online parameter identification and damage detection in smart building structures that are subjected to arbitrary seismic excitation is proposed. It uses real-time measurements of a structure's motion to identify its unknown constant or piecewise constant parameters such as stiffness, damping and mass over the time. The method is based on elements of system synchronization and adaptive control theories. First, a computational system, called the virtual system , is defined. Next, by using properly designed controller and estimations for the unknown parameters , the state of the virtual system is forced to follow the measured motion of the real structure. The mentioned estimations are computed from a proposed update law which depends on the measured motion of the real structure and the virtual system 's state. A major theoretical novelty of this paper is a proposed convergence condition which is applicable in case of arbitrary external forces or ground acceleration. It is shown that upon the satisfaction of that condition, as the synchronization completes, the computed estimation function converges to the true value of the vector of unknown parameters. In addition, an important practical contribution presented in this study is the introduction of a technique called scale factors. It helps to use available initial guesses of the unknown parameters to improve the speed of online identification. Numerical examples show that the proposed method is promising and has a good performance in both online identification and online damage detection problems. [ABSTRACT FROM AUTHOR]

In the research, aiming at high positioning quality for LED chips under the variable system parameters, a real-time and robust visual positioning method is presented. At first, to solve the problems of delay in image system and measuring deviation in encoder, an adaptive dual rate Kalman filter technology is designed to estimate the accurate location of LED chip in real time. After sensitivity analysis, the changes of system parameters during manufacturing have a significant influence on estimation effect. Accordingly, an adaptive recursive least squares algorithm is integrated into the above estimation process to determine the system parameters timely and precisely. In the end, through experimental verification, the described method can guarantee the acceptable positioning effect and adapt to the variations of system parameters. • Compensation of image delay and encoder measurement deviation. • Sensitivity analysis of variable system parameters in LED chip manufacturing. • Fast convergence and precise identification of system parameters. • Precise positioning control and robustness to the variable system parameters. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The state of energy (SoE) is a critical indicator for energy management in supercapacitor (SC) energy storage systems. The estimation accuracy of the SoE relies on the model fidelity, which means that the model parameters are required to be identified online to mitigate the aging effect. However, since the SC model is naturally nonlinear and high dimensional, it is typically difficult to identify the model parameters online. To address this issue, in this paper, we propose a generalized extended state observer (GESO) for SC SoE estimation based on an online identified model. A nonlinear mathematical model for SC is established based on the three branch equivalent circuit models, where the model parameters are online estimated with a designed modified recursive least square. A GESO is designed to estimate the SoE of SC in real time. A laboratory test bed has been built to verify the effectiveness of the proposed method. The experiment results show that the proposed method provides a better SoE estimation accuracy than the existing methods.

Due to the low maintenance cost and high reliability, brushless doubly fed induction generator (BDFIG) has great potential in remote stand-alone applications. For good dynamic performance, the feedforward compensation is used in the BDFIG system. However, its performance highly depends on machine parameters and rotor angle, and thus complicated parameter measurements or estimations are required. To overcome this problem, this paper proposes an adaptive control system, in which the required feedforward parameters such as compensation coefficients and the transformation angle can be identified online by fully utilizing the control action of the inner loops. With the proposed system, precise machine parameters are needless and the encoder can also be canceled, and therefore the overall system becomes more cost-efficient and robust. Detailed analysis and designs of the adaptive control and identification are given in this paper. Experimental results from a BDFIG prototype prove the feasibility and effectiveness of the proposed system. [ABSTRACT FROM AUTHOR]

This paper proposes the variable regularized affine projection algorithms (VR-APAs) for the online multiparameter estimation of the interior permanent magnet synchronous motors (IPMSMs). Unlike the conventional APAs with a fixed regularization factor, the normalized gradient of the mean-square error is introduced in the proposed VR-APA to update the variable regularization which ensures a fast convergence rate, accurate estimation, and low steady-state error. Moreover, the proposed VR-APA does not require any accurate priori information of the motor parameters, making it highly feasible for the IPMSM. In order to accurately estimate the stator d–q axis inductances, stator resistance, flux linkage, and load torque, the two-time scale approach in the proposed VR-APAs is used due to the difference in the IPMSM dynamics. Of various applications of the proposed VR-APAs, such as condition monitoring, fault analysis, and controller design, these estimated multiparameters are updated online to the adaptive decoupling PI controllers to achieve the robustness against the parameter variations due to the temperature increase and load disturbances under various operating conditions (i.e., speed change, load change, and speed reversal). Finally, the comparative experimental verifications via a prototype IPMSM with TMS320F28335 DSP programmed by Code-Composer-Studio are conducted to confirm the effectiveness of the proposed VR-APAs. [ABSTRACT FROM AUTHOR]

Battery management systems (BMS) are widely used in energy storage systems and electric vehicles. The precise estimation of state-of-charge is a key factor affecting the performance of BMS. For the difficulty of calculating the charge status of storage lithium battery (e.g. poor estimation and reliability), this study presents the way of unscented Kalman particle filter (UPF) based on the online recursive least square method matched by dual polarisation battery model. The results show that, compared to the extended Kalman filter (EKF), extended Kalman particle filter (EKPF) and the unscented Kalman filter, the estimate accuracy was improved by 50.9, 33.4 and 19.6%, respectively. The online parameter identification assisted by UPF shows stronger inhibitory effect and the convergence stability. Besides, in comparison to the offline parameter identification with UPF, the estimated accuracy of 27.59% was improved by using the online parameter identification. [ABSTRACT FROM AUTHOR]

Accurately estimating the online state-of-charge (SOC) of the battery is one of the crucial issues of the battery management system. In this paper, the gas–liquid dynamics (GLD) battery model with direct temperature input is selected to model Li(NiMnCo)O2 battery. The extended Kalman Filter (EKF) algorithm is elaborated to couple the offline model and online model to achieve the goal of quickly eliminating initial errors in the online SOC estimation. An implementation of the hybrid pulse power characterization test is performed to identify the offline parameters and determine the open-circuit voltage vs. SOC curve. Apart from the standard cycles including Constant Current cycle, Federal Urban Driving Schedule cycle, Urban Dynamometer Driving Schedule cycle and Dynamic Stress Test cycle, a combined cycle is constructed for experimental validation. Furthermore, the study of the effect of sampling time on estimation accuracy and the robustness analysis of the initial value are carried out. The results demonstrate that the proposed method realizes the accurate estimation of SOC with a maximum mean absolute error at 0.50% in five working conditions and shows strong robustness against the sparse sampling and input error.

To cope with the new transportation challenges and to ensure the safety and durability of electric vehicles and hybrid electric vehicles, high performance and reliable battery health management systems are required. The Battery State of Health (SOH) provides critical information about its performances, its lifetime and allows a better energy management in hybrid systems. Several research studies have provided different methods that estimate the battery SOH. Yet, not all these methods meet the requirement of automotive real-time applications. The real time estimation of battery SOH is important regarding battery fault diagnosis. Moreover, being able to estimate the SOH in real time ensure an accurate State of Charge and State of Power estimation for the battery, which are critical states in hybrid applications. This study provides a review of the main battery SOH estimation methods, enlightening their main advantages and pointing out their limitations in terms of real time automotive compatibility and especially hybrid electric applications. Experimental validation of an online and on-board suited SOH estimation method using model-based adaptive filtering is conducted to demonstrate its real-time feasibility and accuracy.

Battery management systems (BMS) are widely used in energy storage systems and electric vehicles. The precise estimation of state-of-charge is a key factor affecting the performance of BMS. For the difficulty of calculating the charge status of storage lithium battery (e.g. poor estimation and reliability), this study presents the way of unscented Kalman particle filter (UPF) based on the online recursive least square method matched by dual polarisation battery model. The results show that, compared to the extended Kalman filter (EKF), extended Kalman particle filter (EKPF) and the unscented Kalman filter, the estimate accuracy was improved by 50.9, 33.4 and 19.6%, respectively. The online parameter identification assisted by UPF shows stronger inhibitory effect and the convergence stability. Besides, in comparison to the offline parameter identification with UPF, the estimated accuracy of 27.59% was improved by using the online parameter identification.

Online/on-board diagnosis would help to improve fuel cell system durability and output power. Therefore, it is a feature the manufacturers may wish to provide for final users to increase the attractiveness of their product. This add-on requires suitable stack models, parametric identification tools and diagnostic algorithms to be run on low-cost embedded systems, ensuring a good trade-off between accuracy and computation time. In this paper, a computational approach for the impedance parameter identification of polymer electrolyte membrane fuel cell stack is proposed. The method is based on an evolutionary algorithm including sub-population and migration features, which improves the exploration capability of the search space. The goal of the evolutionary algorithm is to find the set of parameters that minimizes an objective function, representing the mismatch between two impedance plots in a normalized plane. The first plot is associated with experimental impedance and the second is computed on the basis of the identified parameters using a circuit model. Three kinds of impedance models, characterized by increasing computational complexity, are used, depending on the experimental data—a linear model made of resistors and capacitors, the Fouquet model and the Dhirde model. Preliminary analysis of the experimental impedance data may evidence correlations among parameters, which can be exploited to reduce the search space of an evolutionary algorithm. The computational approach is validated with literature data in a simulated environment and with experimental data. The results show good accuracy and a computational performance that fits well with the commercial embedded system hardware resources. The implementation of the approach on a low-cost off-the-shelf device achieves small computation times, confirming the suitability of such an approach to online/on-board applications. From a diagnostic perspective, the paper outlines a diagnostic approach based on the identified impedance parameters, on the basis of a small set of experimental data including fuel cell stack faulty conditions.

The state of charge (SOC) and the state of health (SOH) are the two most important indexes of batteries. However, they are not measurable with transducers and must be estimated with mathematical algorithms. A precise model and accurate available battery capacity are crucial to the estimation results. An improved speed adaptive velocity particle swarm optimization algorithm (SAVPSO) based on the Thevenin model is used for online parameter identification, which is used with an unscented Kalman filter (UKF) to estimate the SOC. In order to achieve the cyclic update of the SOH, the concept of degree of polarization (DOP) is proposed. The cyclic update of available capacity is thus obtainable to conversely promote the estimation accuracy of the SOC. The estimation experiments in the whole aging process of batteries show that the proposed method can enhance the SOC estimation accuracy in the full battery life cycle with the cyclic update of the SOH, even in cases of operating aged batteries and under complex operating conditions.

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

In this paper, an improved finite-control-set model predictive control method is proposed for active front-end rectifiers where the computational effort and parameter mismatch problems are taken into account simultaneously. Specifically, a desired voltage vector which only requires one exploration is directly selected by using a single cost function, and the process of selection of the desired voltage vector is optimized by using a sector distribution method. Meanwhile, a model reference adaptive system-based online parameter identification approach is presented to alleviate the parameter mismatch problem. The advantages of the proposed method summarized as follows: First, the proposed algorithm reduces the eight possible voltage vectors to one. The exhaustive exploration can be avoided while the control performance is not deteriorated. Second, the proposed controller can mitigate performance degradation caused by the model parameter mismatch. Simulation results under various parameters operating conditions are presented to demonstrate the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

The model parameters of the lithium-ion battery are of great importance to model-based battery state estimation methods. The fact that parameters change in different rates with operation temperature, state of charge (SOC), state of health (SOH) and other factors calls for an online parameter identification algorithm that can track different dynamic characters of the parameters. In this paper, a novel multiple forgetting factor recursive least square (MFFRLS) algorithm was proposed. Forgetting factors were assigned to each parameter, allowing the algorithm to capture the different dynamics of the parameters. Particle swarm optimization (PSO) was utilized to determine the optimal forgetting factors. A state of the art SOC estimator, known as the unscented Kalman filter (UKF), was combined with the online parameter identification to create an accurate estimation of SOC. The effectiveness of the proposed method was verified through a driving cycle under constant temperature and three different driving cycles under varied temperature. The single forgetting factor recursive least square (SFFRLS)-UKF and UKF with fixed parameter were also tested for comparison. The proposed MFFRLS-UKF method obtained an accurate estimation of SOC especially when the battery was running in an environment of changing temperature.

Induction motor parameters are essential for high-performance control. However, motor parameters vary because of winding temperature rise, skin effect, and flux saturation. Mismatched parameters will consequently lead to motor performance degradation. To provide accurate motor parameters, in this paper, a comprehensive review of offline and online identification methods is presented. In the implementation of offline identification, either a DC voltage or single-phase AC voltage signal is injected to keep the induction motor standstill, and the corresponding identification algorithms are discussed in the paper. Moreover, the online parameter identification methods are illustrated, including the recursive least square, model reference adaptive system, DC and high-frequency AC voltage injection, and observer-based techniques, etc. Simulations on selected identification techniques applied to an example induction motor are presented to demonstrate their performance and exemplify the parameter identification methods.

For model-based state of charge (SOC) estimation methods, the battery model parameters change with temperature, SOC, and so forth, causing the estimation error to increase. Constantly updating model parameters during battery operation, also known as online parameter identification, can effectively solve this problem. In this paper, a lithium-ion battery is modeled using the Thevenin model. A variable forgetting factor (VFF) strategy is introduced to improve forgetting factor recursive least squares (FFRLS) to variable forgetting factor recursive least squares (VFF-RLS). A novel method based on VFF-RLS for the online identification of the Thevenin model is proposed. Experiments verified that VFF-RLS gives more stable online parameter identification results than FFRLS. Combined with an unscented Kalman filter (UKF) algorithm, a joint algorithm named VFF-RLS-UKF is proposed for SOC estimation. In a variable-temperature environment, a battery SOC estimation experiment was performed using the joint algorithm. The average error of the SOC estimation was as low as 0.595% in some experiments. Experiments showed that VFF-RLS can effectively track the changes in model parameters. The joint algorithm improved the SOC estimation accuracy compared to the method with the fixed forgetting factor.

It is well known that predictive direct power control method can be influenced by the presence of modeling errors. Any small variations in model parameters will degrade the performance and stability of the control system. However, this value is difficult to be measured precisely in practical implementation. This paper proposes a predictive direct power control method for three-phase grid-connected converters with online parameter identification technique. Specifically, the proposed method alleviates the parameter mismatch problem by using the online parameter identification technique based on model reference adaptive system estimation theory. The proposed strategy not only identifies the parameter accurately, but also improves dynamic response without any additional sensors. The excellent steady-state and dynamic performance of the proposed method are confirmed through extensive simulations. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The electrical load is one of the most important parts of power systems. Parameter identification of load model is critical for power system stability analysis. The composite load model, which comprises a static load and a dynamic load, is one of the most widely used models in power system simulation and control. As the load characteristics could change considerably over time, online parameter identification is needed. However, because of the non‐linearity and complexity, online parameter identification for load models remains difficult to achieve, and there is rarely an effective solution. This study proposes a load model simplification and parameter identification method to solve this problem. First, the composite load model is preliminarily simplified by choosing the dominant parameters. Next, the load model is further simplified based on a second‐ordered state equation of the induction motor. Subsequently, a two‐step method for online parameter identification is presented. The first step is the electrical parameter identification based on the multi‐layer searching method proposed in this study. The second step is the mechanical parameter identification via the Newton method. Finally, an example for a typical case system is presented to demonstrate the effectiveness, efficiency and accuracy of the two‐step method. [ABSTRACT FROM AUTHOR]