Voltage Estimation Error Research Articles

State-of-Charge Estimation of Ultracapacitor Based on H Infinity Filter Considering Variable Temperature

Abstract The performance and service life of ultracapacitors are highly dependent on accurate modeling and state-of-charge (SOC) estimating. To overcome the model parameter errors caused by the various temperatures and different SOC intervals, the H infinity filter (HIF) is employed to estimate the ultracapacitor SOC based on a variable temperature model. For the application of the HIF method, the Thevenin model is first developed with a small terminal voltage estimation error. Then, the model parameters are optimally identified using the ant colony optimization (ACO) algorithm. Next, a variable temperature model is established to improve the adaptability of the ultracapacitor model, and the HIF is utilized for the ultracapacitor SOC estimation. Finally, to verify the performance of the variable temperature model and the proposed SOC estimation method, a series of experiments are conducted. The analysis results illustrate that the mean absolute error (MAE) of the SOC estimation values based on the variable temperature model is decreased by 39.62% compared to the one based on the nonvariable temperature model. Meanwhile, the proposed state estimation scheme based on the variable temperature model is accurate with estimation values maximum error (ME) and root-mean-squared error (RMSE) less than 0.80% and 0.60%, respectively. The HIF-based SOC estimation method also shows a good robustness with a short convergence time within 90.00 s when the SOC initial error is set to 0.20.

Centralized controller design for voltage estimation error constrained in islanded DC-microgrids: Kalman Filtering Method

The traditional model predictive control (MPC) design strategy heavily depends on the discritization of the non-linear model of DC/DC power converters to approximate the direct current (DC) microgrid system model. The system model does not includes the whole information of the physical implementation of the system. Assumptions have been made to add the model and sensor uncertainties in the system model to form a model with fully physical information that enables its solution through the linear kalman filtering method ( LKFM ). Due to the unbounded of the DC bus voltage estimation error, the instability of the LKFM during transient response increases exponentially. To standardize the MPC model and ensure bounded the DC bus voltage estimation error, a centralized controller design for DC bus voltage estimation error constrained is proposed in this paper. The proposed system model is constructed from DC/DC power converters and then translated into a deterministic model. Through measurements and time updates, the LKFM has been employed to estimate the DC bus voltage. The linear quadratic regulator is used for stabilizing the DC bus voltage estimation and current sharing error between DC/DC power converters. During a transient response, an integral action is utilized to close the loop and eliminate an error in DC bus voltage. A feedback control rule of the DC bus voltage estimation and a Riccati equation have been used to develop the proposed controller. Finally, the proposed approach was validated using Simulink, the Hardware-in-the-loop laboratory, and compared with conventional control approaches. Moreover, the LKFM's stability was examined using exponential stability criteria.

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.

Estimating SOC and SOH of lithium battery based on nano material

Nano-carbon materials are widely used and studied in a new generation of energy storage systems. Lithium batteries are widely used in transportation, power networks, and mobile devices. This paper mainly studies the estimation methods of SOC and SOH of lithium batteries based on nano materials. The SOC state equation and output equation of the lithium battery are established by the ampere integration method, and the parameters in the system are observed to achieve the purpose of observing the charging state of the lithium battery online. Using the internal resistance and SOC of the cell model as state parameters, the nonlinear transfer of equal and covariance is handled. The working voltage was modified using the lithium battery model, and different control methods were implemented for different SOC situations. According to the experimental data, the voltage estimation error is less than 2.1%, which can meet the actual use requirements. It was found that there are different types of lithium-ion batteries, and the model obtained by training samples of lithium-ion batteries reduces the prediction accuracy when predicting other lithium-ion batteries.

Development of wide area monitoring system for smart grid application

PMU can directly measure positive sequence voltage, phase and system frequency. In this paper, the design and implementation for optimum placement of PMU in power system network (PSN) has been performed using 5 different intelligent approaches at an emulation platform. Different case studies based on IEEE 7, 14 and 30 bus system have been performed and analyzed. In the studies, PMU device is used for the measurement of voltage and current magnitude as well as its phase and its performance has been compared with measured real signals of PSN. PMU measurement gives the accurate results and reliability to PSN. But PMUs are not economical, so PSN operator needs to install a minimum number of PMU in PSN so that system should be fully observable in a real-time scenario. In this paper for optimal placement of PMU, five different intelligent methods have been analyzed for three different bus systems and obtained results are compared. For the further validation of selected PMUs for the PSN, a state estimation using WLS algorithm has been performed using conventional data and PMU data on IEEE14 and IEEE30 bus systems. The obtained results for voltage estimation error and phase estimation error with and without PMU data are compared.

Projection algorithm-based nonlinear observation of internal states in fuel delivery systems with gas diffusion

In this paper, a nonlinear observer design to estimate different gas species profiles and partial pressures in a fuel delivery system (FDS) with gas permeation is presented. Precise knowledge of critical internal states is of great importance to ensure reliable and efficient operation for FDS. First, a nonlinear isothermal dynamic model including fuel supply, hydrogen consumption, mixture recirculation and exhaust is formulated. Then a nonlinear observer based on projection algorithm is proposed, giving detailed information on internal conditions of FDS online. Without calculation of inverse matrix, the stack output voltage is treated as the only measurable observer input. The voltage estimation error is obtained from the difference between the prediction and the actual measurement, and nonlinear error injection matrix and projection term are employed as correction inputs. Finally, the proposed dynamic state observer is validated and compared with second order sliding mode (SOSM) observer on RT-LAB platform, the results indicate that the observer shows better real-time estimation performance and robustness. Furthermore, the observer demonstrates good robustness in terms of hydrogen partial pressures observation, but less robustness in nitrogen partial pressures estimation. Moreover, the sequence of key system parameters effecting the estimations are analyzed deeply and some conclusions are extracted.

Measuring battery discharge characteristics for accurate UAV endurance estimation

ABSTRACTThe increasing use of unmanned aerial vehicles in areas such as rescue, mapping, and transportation have made it necessary to study more accurate techniques for calculating flight time estimates. Such calculations require knowing the battery discharge profile. Simplified flight time calculation methods provide data with uncertainties as they are based solely on manufacturer datasheet information. This study presents a setup to measure the battery discharge curve using a LabVIEW interface with a low-cost acquisition system. The acquired data passes through a nonlinear optimisation algorithm to find the battery coefficients, which enables the more precise estimation of its range and endurance. The great advantage of this model is that it makes it possible to predict how the battery will discharge at different rates using just one experimental curve. The methodology was applied to three different batteries and the model was validated with different discharge rates in a controlled environment, which resulted in endurance lower than 3.0% for most conditions and voltage estimation error lower than 3.0% in operational voltage. The work also presented a methodology for estimating cruise time based on the current used during each flight stage.

Robust Estimation of Li-ion Battery SOC Based on Adaptive Mixed KF/HIFF Filter

The estimation of battery state of charge is the key technology and difficulty in the battery management system. model-based extended Kalman filtering (EKF) and H filtering (HIFF) have received attention for better estimation performance in recent years. In this paper, Panasonic NCR 18650GA battery was selected as the research object. first of all, the Thevenin model was established in the MATLAB / Simulink. After that, the bias compensated recursive least squares method was proposed by performing the deviation compensation on the basis of the recursive least squares method, which works well for parameter identification of data with colored noise. Then, an adaptive mixed EKF/HIFF algorithm was designed to estimate the SOC, combining the accuracy of EKF with the robustness of HIFF. And then the joint estimation of model parameters and battery status was realized. Finally, the robustness and accuracy of the proposed joint estimator has been verified by adding different colored noise to the experimental data. The result indicates that the estimation errors of voltage and SOC are less than 0.5% even if add a large colored noise to the experimental data, which makes the SOC estimation more accurate and reliable for the electric vehicles application.

Influence of Battery Parametric Uncertainties on the State-of-Charge Estimation of Lithium Titanate Oxide-Based Batteries

State of charge (SOC) is one of the most important parameters in battery management systems, as it indicates the available battery capacity at every moment. There are numerous battery model-based methods used for SOC estimation, the accuracy of which depends on the accuracy of the model considered to describe the battery dynamics. The SOC estimation method proposed in this paper is based on an Extended Kalman Filter (EKF) and nonlinear battery model which was parameterized using extended laboratory tests performed on several 13 Ah lithium titanate oxide (LTO)-based lithium-ion batteries. The developed SOC estimation algorithm was successfully verified for a step discharge profile at five different temperatures and considering various initial SOC initialization values, showing a maximum SOC estimation error of 1.16% and a maximum voltage estimation error of 44 mV. Furthermore, by carrying out a sensitivity analysis it was showed that the SOC and voltage estimation error are only slightly dependent on the variation of the battery model parameters with the SOC.

Hysteresis effect influence on electrochemical battery modeling

This paper studies the influence on the voltage estimation accuracy of a novel hysteresis effect model. The model is applied to six different battery technologies: Lead–acid AGM, Lead–acid gel, Nickel–cadmium, Nickel–metal hydride, Lithium-ion, Lithium-ion polymer. The proposed hysteresis electrical model estimates the variation of the parameters in a classical model when considering hysteresis effects. A general model is proposed and the characterization process is optimized so that it can be applied to all studied cells. The model is composed of three circuits: voltage–current circuit, Ah counting circuit and hysteresis electrical model circuit. All the parameters for all six technologies are calculated and compared. The model evaluation is carried out through 18 case studies. The results are compared in terms of increment of voltage estimation accuracy when considering hysteresis and influence of hysteresis on a certain technology. The model average voltage estimation error of all technologies is 0.48%. When hysteresis effect is included the average error reduces in 0.71%.

An Improved Battery On-line Parameter Identification and State-of-charge Determining Method

Abstract To improve the estimation accuracy of battery's inner state for battery management system, an improved online parameters identification algorithm for equivalent circuit battery model is researched. To reduce the computation cost, the existing methods regarded the open circuit voltage over time as a constant value. However, when the sampling intervals are bigger, the estimation error of the battery state-of-charge calculated by the traditional method can be reach to 10% or more. Compared with the existing battery model parameter identification method, this study proposes a new online estimation method and which can estimate the battery open-circuit voltage in different sampling intervals with high accuracy. The results of the experiment, which uses Federal Urban Driving Schedule test to verify the parameters identification approach, show the proposed approach can accurately identify the model parameters within 1% maximum terminal voltage estimation error, and the state-of-charge error which calculated by the open circuit voltage estimates can be efficiently reduced to an accepted level.

Research on an Online Identification Algorithm for a Thevenin Battery Model by an Experimental Approach

To improve the estimation accuracy of battery’s inner state for battery management system, an online parameters identification algorithm for Thevenin battery model is researched. The Thevenin model and parameters identification algorithm based on recursive least square adaptive filter algorithm was built with the Simulink/xPC Target. The results of hardware-in-loop experiment, which uses Federal Urban Driving Schedule test to verify the parameters identification approach, show the proposed approach can accurately identify the model parameters within 1% maximum terminal voltage estimation error, and the State of Charge error which calculated by the open circuit voltage estimates can be efficiently reduced to 4%.

Cell State-of-Charge Estimation for the Multi-cell Series-connected Battery Pack with Model bIas Correction Approach

Accurate estimations of cell state-of-charge (SoC) for multi-cell series-connected battery pack are remaining challenge due to the inconsistency characteristic inhabited in battery pack and the uncertain operating conditions in electric vehicles. This paper tries to add three contributions. (1) A data-driven filtering process is proposed to select one represented cell to typify the voltage behavior of battery pack. (2) An improved battery model considering model and parameter uncertainties is developed. (3) An adaptive SoC estimator has been developed, in which the SoC of each cell in battery pack can be accurately predicted. The SoC of battery pack can be located with the SoC values of each cell. It significantly improves the safety operation of battery. The result indicates that the estimation errors of voltage and SoC for all the LiPB cells are less than 3% even if given big erroneous initial state of estimator.

A data-driven adaptive state of charge and power capability joint estimator of lithium-ion polymer battery used in electric vehicles

An accurate SoC (state of charge) and SoP (state of power capability) joint estimator is the most significant techniques for electric vehicles. This paper makes two contributions to the existing literature. (1) A data-driven parameter identification method has been proposed for accurately capturing the real-time characteristic of the battery through the recursive least square algorithm, where the parameter of the battery model is updated with the real-time measurements of battery current and voltage at each sampling interval. (2) An adaptive extended Kalman filter algorithm based multi-state joint estimator has been developed in accordance with the relationship of the battery SoC and its power capability. Note that the SoC and SoP can be predicted accurately against the degradation and various operating environments of the battery through the data-driven parameter identification method. The robustness of the proposed data-driven joint estimator has been verified by different degradation states of lithium-ion polymer battery cells. The result indicates that the estimation errors of voltage and SoC are less than 1% even if given a large erroneous initial state of joint estimator, which makes the SoP estimate more accurate and reliable for the electric vehicles application.

A data-driven based adaptive state of charge estimator of lithium-ion polymer battery used in electric vehicles

An accurate State of Charge (SoC) estimation method is one of the most significant and difficult techniques to promote the commercialization of electric vehicles. The paper attempts to make three contributions. (1) Through the recursive least square algorithm based identification method, the parameter of the lumped parameter battery model can be updated at each sampling interval with the real-time measurement of battery current and voltage, which is called the data-driven method. Note that the battery model has been improved with a simple electrochemical equation for describing the open circuit voltage against different aging levels and SoC. (2) Through the real-time updating technique of model parameter, a data-driven based adaptive SoC estimator is established with an adaptive extended Kalman filter. It has the potential to overcome the estimation error against battery degradation and varied operating environments. (3) The approach has been verified by different loading profiles of various health states of Lithium-ion polymer battery (LiPB) cells. The results indicate that the maximum estimation errors of voltage and SoC are less than 1% and 1.5% respectively.