Voltage Error Research Articles

State of charge estimation for lithium-ion batteries based on fractional order multiscale algorithm

The accuracy of state of charge (SoC) plays a critical role in lithium-ion batteries management system, whereas it gradually reduces as ohmic internal resistance growing and maximum available capacity decreasing with battery aging, which is still an open and challenging problem in SoC estimation. To tackle this issue, a multi-scale estimation algorithm is proposed by incorporating fractional order adaptive extended Kalman filter and variable forgetting factor recursive least square (FOAEKF-VFFRLS). Firstly, in order to describe the physicochemical reactions inside battery accurately, a fractional order model of lithium-ion battery with its discrete form is introduced, and the resistor-capacitor (RC) values as well as the order of capacitors are obtained by employing ant colony algorithm (ACA). Secondly, for enhancing SoC estimation accuracy, a fractional order adaptive extended Kalman filter algorithm (FOAEKF) is proposed to continuously modify the noise statistical characteristics while utilizing observation data information. Thirdly, to predict ohmic internal resistance and maximum available capacity for battery, a variable forgetting factor recursive least square algorithm (VFFRLS) is established based on the fact that forgetting factor determined by individual data error would be suddenly changes in the case of inaccurate or perturbed data. Besides, to further improve the accuracy of SoC estimation, a multi-scale estimation approach is proposed in light of the slow variation of battery internal resistance and the fast change of SoC. Finally, through comparative experiments, it is proved that the proposed approach has comprehensive strengths. First, the minimum root mean square error (RMSE) of SoC estimation error is controlled within 0.52 %. Second, terminal voltage error is kept small while limiting SoC errors within narrow ranges. Third, favorable robustness in terms of SoC estimation is achieved despite SoC initial value is mis-initialized.

Precise and fast safety risk classification of lithium-ion batteries based on machine learning methodology

How to establish a fast and accurate model with physics implications still remains an unsolved headache for the community. Herein, we establish a battery safety risk classification modeling framework based on a machine-learning algorithm that can accurately and rapidly classify the potential safety risk level. The model can identify defective cells, cells with internal short circuits (ISCs), and cells with possible thermal runaway (TR) using a small portion of one cycle data. About 3 × 105 training samples are generated, covering a wide spectrum of State of Charge (SOC), short circuit resistance, and charging/discharging rate (C-rate)) ranges based on established electrochemo-mechanical model, serving as training and prediction datasets for the machine learning (ML) algorithm. Classifiers in the ML model demonstrates satisfactory performance and robustness. We further prove that only 5 min is needed for the model to classify the cells with over 95% accuracy with a voltage error<0.1 mV if the ISC resistance is smaller than 102Ω. This work highlights the promising future of combining physics-based numerical modeling and data-driven algorithm to provide satisfactory battery safety identification with limited data produced in a short cycling time.

Rotor Winding Short-Circuit-Fault Protection Method for VSPSGM Combining the Stator and Rotor Currents

Rotor winding short circuit faults are common faults for variable-speed pumped-storage generator-motors (VSPSGM). At present, the exciting rotor fault protection of VSPSGM is simple and has low sensitivity. It can only act when the instantaneous value of the rotor phase current reaches three times the rated current. Therefore, it is difficult to cover some rotor winding short-circuit faults with weak fault characteristics. It is urgent to study a novel rotor winding short-circuit-fault protection method for VSPSGM. In this paper, a protection method that combines the stator and rotor currents with different frequencies is proposed. The characteristics of the stator and rotor currents before and after the fault is analyzed by using Clark transformation. On this basis, a specific protection criterion is constructed based on the discrete integral operation, which is easy to implement and not affected by the change of rotor speed. Then, the calculation method of the protection setting is proposed, considering the effect of unbalanced voltage and sensor measurement error. Simulation results show that the proposed method can reliably realize the protection of rotor winding faults. It has faster protection action speed than other methods in the same field. The protection coverage rate is over 90%.

Coordinated Volt/Var Control of PV and EV Interfaced Active Distribution Networks Based on Dual-Stage Model Predictive Control

This article presents a dual-stage coordinated control approach for voltage regulation and congestion management of active distribution networks (ADN) in the presence of photovoltaic (PV) generators and electric vehicle stations (EVS). The proposed scheme operates on rule-based model predictive control (RBMPC) to optimally manage the settings of the regulating devices, i.e., on-load tap changer (OLTC), distribution static synchronous compensator (DSTATCOM), PV generators, and EV inverters that possess different temporal characteristics. The first (hourly time-scale) and the second (one-minute time-scale) stages of the dual-stage coordinated control mechanism are designed to correct the long-term and short-term voltage fluctuations, respectively. The objectives of the proposed approach are to minimize the number of OLTC operations (first stage) and changes in set-points of PV and EV inverters, and DSTATCOM (second stage) in addition to minimization of slack variables, energy loss, and voltage error at EV stations. In both the stages, predefined rules are set for MPC to optimize different objectives on the basis of the magnitudes of bus voltages. Simulations are performed on 33-bus and 38-bus distribution networks to test the efficacy of the control approach. It is observed that the proposed approach could mitigate the voltage variations as well as line congestion for different scenarios.

Primary-Side Linear Control for Constant Current/Voltage Charging of the Wireless Power Transfer System Based on the LCC-N Compensation Topology

This article proposes a primary-side linear control strategy to realize the constant current (CC)/voltage (CV) charging for the wireless power transfer system, based on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LCC-N</i> magnetic integration and non-compensation circuit. Different from the conventional compensation topology used in control strategies, this article combines magnetic integration structure on the primary side and noncompensation topology on the secondary side, which reduces the size of the additional compensation inductor and makes the secondary side more compact. Moreover, the primary-side linear control strategy is studied by using the improved load parameters identification method, based on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LCC-N</i> topology. First, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LCC-N</i> magnetic integration and noncompensation topology is introduced. An improved load parameters identification method considering the non-pure resistive input impedance characteristics of the rectifier is proposed to identify the current, voltage and equivalent resistance of the load, based on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LCC-N</i> topology. In the process of identification, the complicated relationship between the input impedance of the rectifier and the load is needless. Then, according to the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LCC-N</i> output characteristics and identified load parameters, the relationship between the input voltage and load current and voltage is simplified to linear through the linear superposition of fundamental wave and third harmonic. And a linear control strategy on the primary side is proposed to achieve CC/CV output. Finally, a 1 kW WPT prototype is built to verify the effectiveness of the proposed control strategy. The experimental results show that the maximum error of the output current is 1.8% during the CC charging, and the maximum error of the output voltage is 1.2% during the CV charging.

Model Predictive Voltage Control for SPMSM Drives With Parameter Robustness Optimization

In order to improve the parameter robustness of the model predictive voltage control (MPVC) for SPMSM drives, a robust MPVC scheme is proposed in this article. First, the parameter sensitivity of MPVC is analyzed. Then, according to the reference voltage equation, the reference voltage error model is presented. In this model, only inductance information is included; the resistance and the flux-linkage parameter are avoided. Furthermore, the inductance error extraction system is designed to obtain accurate inductance information. Then, according to the inductance error and reference voltage error model, the accurate reference voltage is obtained by compensating for the reference voltage error in real time. Finally, the proposed method is verified by experiments. Experimental results prove that the proposed method can effectively enhance the robustness of the system and eliminate the negative impact of parameter mismatch.

A novel pre-synchronization control strategy for microgrid connections based on improved droop control

Microgrid control strategies based on traditional droop control often exhibit output voltage and frequency return errors. As such, this study proposes a novel pre-synchronization control strategy to improve both the accuracy and stability of voltage and frequency, suppress harmonics generated by an inverter, and reduce the control errors. First, an improved droop control strategy was proposed to automatically adjust the microgrid output voltage and frequency in order to achieve power sharing and suppress voltage harmonics. A pre-synchronization controller was then used to compensate for the angular frequency during droop control, based on deviations in output voltage and frequency between the microgrid and power grid. This ensured stability and smooth switching between the two grids. Finally, a series of numerical simulations were conducted with the Matlab/Simulink software. The simulated results exhibited a frequency fluctuation range of 49.95–50.02 Hz, and 49.5–50.2 Hz for traditional droop control. In addition, a voltage deviation of only ±2.89% was observed from the nominal voltage (in both connected and islanded operation). The total harmonic distortion (THD) rate was 1.50% when the microgrid was connected to the power grid and 2.57% for traditional droop control. These results suggest the proposed control strategy to be highly effective and reliable.

A novel equivalent modeling method combined with the splice‐electrochemical polarization model and prior generalized inverse least‐square parameter identification for UAV lithium‐ion batteries

AbstractThe accuracy of lithium‐ion battery state estimation is critical to the safety of unmanned aerial vehicles (UAVs). In this paper, aiming at the high‐fidelity modeling of the UAV lithium‐ion battery, a splice‐electrochemical polarization model (S‐EPM) for UAV lithium‐ion battery is constructed by combining the traditional electrochemical model with the equivalent circuit model, which greatly improved the accuracy of the battery modeling. In addition, a novel prior generalized inverse least‐squares algorithm is proposed. Also, based on this algorithm, the full‐parameter identification and multicondition error analysis of the S‐EPM are realized based on this algorithm. Finally, a targeted complex discharge rate test and a full‐function charge–discharge test were designed to further verify the applicability of the S‐EPM to complex conditions. The experimental results show that the voltage error of the model under each working condition is 5.50 and 3.0 mV, and the maximum percentage error ratio is 0.20% and 0.07%. This experiment can provide a theoretical basis for the combination of the electrochemical model and equivalent circuit model and the accurate estimation of internal state variables of lithium‐ion batteries.

SOC Estimation Based on Hysteresis Characteristics of Lithium Iron Phosphate Battery

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.

State of charge estimation based on active disturbance rejection control for power batteries in engine waste heat recovery system

The Coulomb counting (CC) method is the most widely accepted state of charge (SOC) estimation method in the industry. However, the cumulative error generated during operation is a huge challenge for the engineering application. A feedback regulation framework is designed to address this situation; however, the basic problem of this framework is that the error regulation occurs only after the error is generated. As a result, its initiative to suppress system disturbance is insufficient. To overcome this problem, an active disturbance rejection control–based-feedback-estimator is proposed in this paper. The regulation is designed based on the terminal voltage error between actual measurement and theoretical value. And active disturbance rejection control is employed to improve the estimation accuracy and adaptability. The power batteries’ SOC estimation in engine waste heat recovery system is studied in this paper. Compared with the traditional CC method and a proportional–integral (PI) feedback estimation method, the proposed active disturbance rejection control-feedback-estimator exhibits better estimation performance and adaptability to system disturbances and uncertainties. There are only three parameters [Formula: see text], [Formula: see text], and [Formula: see text] to tune, and estimation performance can be maintained without having to retune parameters when boundary conditions change, proving the effectiveness of the proposed active disturbance rejection control-feedback-estimator.

Prediction Errors Analysis and Correction on FCS-MPC for the Cascaded H-Bridge Multilevel Inverter

Predictive errors restrict high performance of finite control set model predictive control (FCS-MPC) in the cascaded H-bridge multilevel inverter.In order to solve this problem, the influence mechanisms of parameter mismatch, model discretization, and sampling error on voltage and current prediction errors are analyzed in this article. A hierarchical multifactorial error correction method is proposed. The known voltage prediction errors of the previous adjacent prediction periods are first fed back to calculate the voltage predictive error factors and self-correct the next voltage prediction errors. And then the current prediction errors are calibrated by the corrected voltage and the calculated current predictive error factor. This proposed method involves a simple structure and a good inhibitory ability on multifactorial prediction errors. Finally, experimental results in a three-phase five-level cascaded H-bridge multilevel inverter support the theoretical analysis and the proposed method.

An Efficient Model Predictive Torque Control for Induction Motors With Flexible Duty Ratio Optimization

Efforts to improve the drive performance of classical model predictive torque control (MPTC) for induction motors (IM) have continued for decades. Existing methods often suffer from intensive computations, poor robustness, and limited quality. To resolve these problems, this article proposes an efficient MPTC for IM with flexible duty ratio optimization. In the proposed strategy, candidate voltage vectors are restricted to be in a subset of an area enclosed by voltage limit hexagon, where all the possible two-vector combinations and part of three-vector combinations are included. To quickly determine the optimal input voltage, a tailored duty ratio optimization algorithm based on voltage error minimization is designed. Experiments clearly indicate that the proposed strategy can effectively improve the steady performance with respect to classic MPTC and some other typical methods. More importantly, the proposed strategy can be conveniently extended by increasing the number of three-vector combinations to achieve steady performance close to space vector modulation-based direct torque control (SVM-DTC). It is inspiring to see faster dynamic response together with less torque ripple than SVM-DTC is provided during a large transient due to the rigorously enhanced optimality of input voltage. Finally, the robustness of the proposed scheme is experimentally validated.

Digital Voltage Sampling Scheme for Primary-Side Regulation Flyback Converter in CCM and DCM Modes

Primary-side regulation (PSR) flyback topology with discontinuous conduction modulation (DCM) mode has been widely used in low-power application due to its high stability, wide input voltage range, low cost, and low standby power. To improve the output power, continuous conduction modulation (CCM) mode is always introduced. However, in CCM mode, conventional PSR voltage-sampling method cannot realize high precision of output voltage or the voltage sampling method requires complex control of high cost. In this paper, a simple digital voltage sampling scheme with two reference voltages outputted from a digital-to-analog converter (DAC) is put forward to trace the “accurate-point” voltage for DCM and CCM modes. In CCM mode, the sampling voltage error of the “knee-point” voltage can be compensated just by inserting DCM switching cycle to obtain the “accurate-point” voltage. Voltage difference between DCM mode and CCM mode will be eliminated, and high precision is easily realized. The proposed method is verified in a 20V, 65W PSR flyback converter. The sampling method only requires a low speed DAC, three comparators and a digital controller. The output voltage precision is within 0.6% with universal input voltage.

Research on the traceability method based on half symmetric superposition

In order to achieve high-accuracy self-calibration and traceability of electromagnetic standard voltage transformers, a measurement method that can be used for voltage ratio standard error voltage coefficients is proposed. Using two auxiliary standard voltage transformers, the error variation of the voltage transformer under different voltages can be obtained through three-step measurement. Compared with the traditional error voltage coefficient measurement method and international comparison, the feasibility of the method and the rationality of the uncertainty evaluation of the measurement results are verified. The self-calibration ratio difference measurement uncertainty of the 110 kV standard voltage transformer is better than 5 × 10−6, and the angular difference measurement uncertainty is better than 5 µrad. The method can be used to trace the source of standard devices up to 1000 kV power frequency voltage ratio, which have characteristics such as high accuracy and good stability.

Modeling and Stability Analysis of Distributed Secondary Control Scheme for Stand-Alone DC Microgrid Applications

Stand-alone DC microgrids have multiple distributed generation (DG) sources that meet the required demand (load) by using droop control to achieve load (current) sharing between the DGs. The use of droop control leads to a voltage drop at the DC bus. This paper presents a distributed secondary control scheme to simultaneously ensure current sharing between the DGs and regulate the DC bus voltage. The proposed control scheme eliminates the voltage deviation and ensures balanced current sharing by combining the voltage and current errors in the designed secondary control loop. A new flight-based artificial bee colony optimization algorithm is proposed. This selects the parameters of the distributed secondary control scheme to achieve the objectives of the proposed controller. A state–space model of the DC microgrid is developed by using eigenvalue observation to test the impacts of the proposed optimized distributed secondary controller on the stability of the DC microgrid system. A real-time test system is developed in MATLAB/Simulink and used in a Speedgoat real-time simulator to verify the performance of the proposed control scheme for real-world applications. The results show the robustness of the proposed distributed secondary control scheme in achieving balance current sharing and voltage regulation in the DC microgrid with minimal oscillations and fast response time.

Elaborated EECM parameter identification reflecting adaptive weight for achieving a high‐accuracy SOC estimator

AbstractIn this paper, a combined method is proposed to improve the accuracy of an electrical equivalent circuit model (EECM) and state‐of‐charge (SOC) estimation in situations where a lithium‐ion battery is frequently charged and discharged. To calculate the terminal voltage in the dynamic region, where charging and discharging are frequently repeated at 0.1 s intervals, a specific region is determined and historical weight is selected, based on the amount of charge in that region, to reflect the model parameters. The historical weights are applied to the charging and discharging parameters to improve the estimated performance of the EECM in estimating the terminal voltage of the battery. Also, the improved EECM is applied to the extended Kalman filter to improve the performance of SOC estimation. A 21,700 cylindrical lithium nickel manganese cobalt oxide‐type battery was used for verification, and the parameters required for modeling were extracted through experiments that were conducted to determine electrical characteristics. This paper compared two models not considering the history of charging or discharging characteristics with a model considering history to verify the accuracy of voltage and SOC estimation results. The result shows that the voltage and SOC estimation errors of the proposed combined method are the lowest in the three cases.

Adaptive Second-Order Sliding Mode Control of Buck Converters with Multi-Disturbances

In this paper, a novel adaptive second-order sliding mode (2-SM) control approach, based on online zero-crossing detection, was proposed to solve the problems of the chattering and fixed control gain for buck converters with multi-disturbances. In modeling, the possible parameter perturbations and external disturbances of the converter system were contained. Instead of the traditional first-order sliding mode (1-SM), the twisting algorithm with 2-SM was adopted for the controller design, which could overcome the chattering problem and realize control continuity. Meanwhile, a novel adaptive mechanism was introduced to replace the conventional fixed control gain by time-varying control gain, the idea of which is to calculate the number of the zero-crossing points of the sliding surface online. As a result, the control magnitude of the improved controller could be reduced to a minimal admissible level, and the steady error of the output voltage could converge to the expected value. Furthermore, the robust stability of the converter system with multi-disturbances wads investigated. Comparative simulations and experiments validated the advantages of this paper as offering better robustness and control performance.

A multi‐model real covariance‐based battery state‐of‐charge fusion estimation method for electric vehicles using ordered weighted averaging operator

Uncertainty prediction of lithium-ion battery state-of-charge (SOC) is key for electric vehicle battery management systems. Aiming at the shortcomings of a single equivalent circuit model (ECM) and traditional SOC fusion estimation algorithms, this paper proposes a new multi-model SOC fusion method. First, three sub-models are established. Second, an adaptive extended Kalman filter is applied to each sub-model in parallel to predict the battery terminal voltage and SOC simultaneously. Then, based on the ordered weighted averaging (OWA) operator theory, the real covariance matrix of the output voltage error of each model is obtained, and the weight factor of each sub-model is calculated using this matrix. Finally, the SOC estimation of each model is weighted and synthesized to realize the SOC fusion estimation. The experimental results show that the maximum absolute error of the multi-model SOC fusion estimation based on the OWA operator is close to the optimal value of a single model, whether it is the fusion of three ECMs or two ECMs with a degraded electrochemical model, and the multi-model SOC fusion estimation based on OWA operator has better robustness than the single model SOC estimation.

Primary Voltage and Frequency Regulation in Inverter Based Islanded Microgrids through a Model Predictive Control Approach

A frequency and voltage control strategy based on a decentralized and communication-less approach is proposed in this work and applied to Photovoltaic-Storage-Microturbine islanded Microgrids (MGs). The approach is based on the Model Predictive Control (MPC) technique. Thanks to the use of local measurements, each source can nullify the steady-state voltage and frequency errors by means of a dedicated MPC controller. Consequently, the proposed approach unifies the advantages of classic droop and master/slave controllers due to the absence of communication links among devices and due to the absence of a secondary centralized control loop.

Adaptive estimation of PEMFC stack model parameters - An experimental verification

The growing popularity of using proton-exchange membrane fuel cells (PEMFCs) stacks in stationary, portable, and transportation applications is driving researchers to develop proper dynamic models for PEMFCs. These models are used to accurately capture the electrical characteristics and runtime performance. This work proposes a well-known equivalent circuit model of a battery, to be modified and used as a model for a PEMFC stacks voltage-current characteristics. This model is modified by finding suitable functions to model the open circuit voltage and the series resistance, required to model the electrical performance of a 200-W PEMFC stack. The paper also shows that the existing adaptive parameters estimation (APE) technique for Li-ion battery parameters estimation is also able to estimate parameters of the PEMFC stack's model. The model parameters are estimated using the APE technique that requires only five experiments. The model is validated experimentally under different load conditions for a 200-W PEMFC stack supplied from a hydrogen cylinder (voltage error −0.2 V to 0.5 V), and a 30-W PEMFC stack supplied from a fuel stick (voltage error −0.2 V–0.4 V). The results show that the parameters estimation methodology works well across PEMFC stacks of different sizes with different input fuel intake configurations, with a minimal terminal voltage estimation error in the order of millivolts. Open circuit voltage measurements (OCV) show that the OCV curve starts at a little lower than 31 V, declines slowly to around 30 V for a normalized hydrogen flow rate of 0.6, after which there is a sudden linear decline in OCV was observed. Most of the data has absolute estimation error less than 0.1 V. In fact, the terminal voltage estimation error across all tests, with different current discharge profiles, lies between −0.2 and 0.2 V only. Also, 95.84% of the error samples lie between ±0.1% error.