Dynamic Stress Test Research Articles

Study on Dynamic Performance of Standard Sand Based on KTL Dynamic Triaxial Apparatus

The determination of dynamic properties is of great scientific significance to the constitutive modeling and parameter determination of large deformation for sand liquefaction. For Fujian standard sand, a new high-precision KTL bidirectional dynamic triaxial test system was used to carry out liquefaction tests under equal consolidation and different equal amplitude cyclic stresses. The dynamic characteristics of Fujian standard sand, such as dynamic stress and dynamic strain test results, pore water pressure model, effective stress path, and stress-strain relationship curve, were studied. The main results are as follows: the whole vibration process of the sample could be basically divided into four stages: the greater the dynamic stress is, the easier the sample is to liquefy. With the increasing vibration times, the samples under different dynamic stress amplitudes meet the qualitative analysis of the first three stages of liquefaction; that is, the pore water pressure increases rapidly at the beginning of vibration, and the growth is stable in the middle stage. When the effective pore water pressure increases to 60 kPa, it begins to increase sharply and finally reaches the effective confining pressure, and then, the soil sample liquefies. The axial strain begins to accumulate in the compression direction and gradually increases. After liquefaction, the strain accumulates in the tensile direction in equal amplitude. During the whole vibration process, the axial strain of the sample can develop greatly in the tensile and compressive direction. Under the action of different cyclic stresses, the stress path of the soil sample tends to zero, and the rate of effective stress state varies greatly, indicating that the dynamic stress has a great influence on the dynamic liquefaction process of Fujian standard sand. In the dynamic triaxial test of standard sand, the difference in dynamic stress amplitude will lead to great changes in the stress-strain relationship curve. But it has little effect on the analysis of liquefaction dynamic characteristics of sand.

Computationally Efficient State-of-Charge Estimation in Li-Ion Batteries Using Enhanced Dual-Kalman Filter

This paper proposes a state-of-charge estimation technique to meet highly dynamic power requirements in electric vehicles. When the power going in/out the battery is highly dynamic, the statistics of the measurement noise are expected to deviate and maybe change over time from the expected laboratory specified values. Therefore, we propose to integrate adaptive noise identification with the dual-Kalman filter to obtain a robust and computationally-efficient estimation. The proposed technique is verified at the pack and cell levels using a 3.6 V lithium-ion battery cell and a 12.8 V lithium-ion battery pack. Standardized electric vehicle tests are conducted and used to validate the proposed technique, such as dynamic stress test, urban dynamometer driving schedule, and constant-current discharge tests at different temperatures. Results demonstrate a sustained improvement in the estimation accuracy and a high robustness due to immunity to changes in the statistics of the process and measurement noise sequences using the proposed technique.

Fracture Mechanism Analysis and Design Optimization of a Wheelset Lifting Mechanism Based on Experiments and Simulations

In this study, material and dynamic stress experiments are combined with finite element (FE) simulations to reveal the fracture mechanism of the wheelset lifting apparatus, and a structural design optimization scheme based on the double-layer Kriging surrogate model is proposed. The fracture mechanism of the wheelset lifting apparatus is first clarified through the material analysis of macro/micro and dynamic stress tests. Static strength and modal analyses are then performed to perfect the mechanism analysis in terms of structural performance. An efficient, robust, fatigue design optimization method based on the double-layer Kriging surrogate model and improved non-dominated sorting genetic algorithm II (NSGA-II) is finally proposed to improve the original design scheme. For the wheelset lifting mechanism’s fracture, the crack source is found on the transition fillet surface of the lifting lug and lifting ring, where the fracture has the characteristics of two-way, multisource, high-cycle, low-stress fatigue. It is further revealed that the vibration fatigue occurring at the point of maximum stress is the main cause of the fracture. The effectiveness of the proposed design optimization method is validated via comparative analysis.

Startup Process Optimization of Hydropower Unit Based on Stress Measurement of Francis Turbine Runner

The energy efficiency characteristics of the turbine, cavitation performance of the runner, and operation stability of the unit are the three major concerns of a hydropower field, the latter two factors leading to the units’ cavitation and stability problems under some operation conditions and fatigue failure of the runner blades, which are the main causes of structural and mechanical failure for the hydropower units. In this article, three conventional unit startup processes are introduced, and combined with the optimal impulse control strategy, an optimized unit startup method is proposed. As the optimal guide vane opening adjustment setting value is obtained by solving the planned startup control strategy, 3D modeling and CFD simulation are carried out, then the equivalent stress characteristics are obtained by finite element calculation when the unit is running from startup to no load at a rated speed on three different startup modes. At last, the dynamic stress tests of the runner blade are performed at the hydropower station by using wireless acquisition and transmission technology, and the peak value and characteristics of the dynamic and static stresses of the blade under different startup processes are obtained. In this, the validity of the model analysis is proved by the comparison between the simulation calculation and the measured results, and the effectiveness of the startup control strategy is proved by combining the runner blade dynamic stress simulation, calculation, and the field dynamic stress test results. The optimization method of the startup process proposed in the article can alleviate the abnormal vibration of the unit and provide a basis for long-term stable operation of the hydropower unit.

A State-of-Charge Estimation Method Based on Multi-Algorithm Fusion

Lithium-ion power batteries are widely used in the electric vehicle (EV) industry due to their high working voltage, high energy density, long cycle life, low self-discharge rate, and environmental protection. A multi-algorithm fusion method is proposed in this paper to estimate the battery state of charge (SOC), establishing the Thevenin model and collecting the terminal voltage residuals when the extended Kalman filter (EKF), adaptive extended Kalman filter (AEKF), and H infinite filter (HIF) estimate the SOC separately. The residuals are fused by Bayesian probability and the weight is obtained, and then the SOC estimated value of the fusion algorithm is obtained from the weight. A comparative analysis of the estimation accuracy of a single algorithm and a fusion algorithm under two different working conditions is made. Experimental results show that the fusion algorithm is more robust in the whole process of SOC estimation, and its estimation accuracy is better than the EKF algorithm. The estimation result for the fusion algorithm under a Dynamic Stress Test (DST) is better than that under a Hybrid Pulse Power Characterization (HPPC) test. With the emergence of cloud batteries, the fusion algorithm is expected to realize real vehicle online application.

A fractional-order model of lithium-ion batteries and multi-domain parameter identification method

A multi-domain parameter identification method for a fractional-order model of lithium-ion batteries is presented. The fractional-order model is studied, and twenty-five identification parameters are determined. An intelligent optimization method named the genetic algorithm-particle swarm optimization algorithm is used to identify the parameters. Based on electrochemical impedance spectroscopy in the frequency domain and the terminal voltage of the dynamic stress test in the time domain, a multi-domain identification method is proposed. In synthetic experiment, the proposed genetic algorithm-particle swarm optimization algorithm has higher accuracy and faster convergence speed than the traditional optimization methods, and the proposed multi-domain identification method has more accurate parameter identification results than the frequency-domain identification and time-domain identification. In experiment on lithium-ion batteries, the model parameters are identified by electrochemical impedance spectroscopy and dynamic stress test data, and the parameter identification results are verified by verification test data. The results demonstrate that the genetic algorithm-particle swarm optimization algorithm and multi-domain identification method can be used as robust and reliable tools for parameter identification of lithium-ion batteries. A MATLAB application with the proposed method is also published on the community MATLAB website, providing researchers with a more convenient and effective tool.

Simultaneously estimating two battery states by combining a long short-term memory network with an adaptive unscented Kalman filter

Accurate state of charge (SOC) and state of energy (SOE) estimations for lithium-ion batteries (LIBs) are of great significance in battery management system (BMS). Especially, the two battery states estimation with SOC and SOE at the same time, can promote the battery life and ensure the system reliability of LIBs. In this work, a novel long short-term memory network combined with an adaptive unscented Kalman filter (LSTM-AUKF) method is proposed to estimate SOC and SOE simultaneously. The proposed LSTM-AUKF method is validated with several dynamic driving schedules (dynamic stress test, US06 test, and federal urban driving schedule) under different temperatures and different initial errors. Experimental results reveal that the proposed method can effectively co-estimate the SOC and SOE for the LIBs with high accuracy and low complexity. The recorded root means square error (RMSE) and mean absolute error (MAE) of SOC are controlled within 0.43% and 0.41% respectively. Meanwhile, the RMSE and MAE of SOE estimation are less than 0.46% and 0.44%, respectively. Furthermore, the proposed LSTM-AUKF has been compared with single LSTM, LSTM combined with an unscented Kalman filter and other methods for SOC and SOE estimation, the results indicate that the proposed method has excellent performance in reducing computation complexity and enhancing estimation accuracy.

An adaptive state of charge estimator for lithium‐ion batteries

AbstractThis study presents a data‐driven approach in conjunction with an adaptive extended Kalman filter (AEKF) to estimate lithium‐ion batteries' state of charge (SOC) online. The Thevenin battery model is used to evaluate the effects using battery voltage and current. The advantages of the Lagrange multiplier method are utilized to model the lithium‐ion battery. The Lagrange multiplier method continuously decreases the model error to adjust the Kalman gain of AEKF for accurate SOC estimation. Various current profiles such as hybrid pulse test, dynamic stress test, and Beijing dynamic stress test are used to verify the proposed approach's adaptability, robustness, and accuracy. It is observed that the proposed approach outperforms other methodologies (recursive least square–AEKF and forgetting factor recursive least square–AEKF) due to its high accuracy (mean average error of 0.32%). Additionally, the proposed approach exhibits robustness and high convergence speed despite deliberate erroneous initialization of parameters, thus indicating its applicability in online SOC estimation applications.

Structure Strength Dynamic Strain Measurement Acquisition System Based on Data Fusion

Strain measurement technology plays an irreplaceable role in measuring the force change characteristics of mechanical components and monitoring important engineering structures. In structural health monitoring, dynamic stress and strain testing is the main way to determine its overall fatigue, and excessive stress will seriously affect the strength of the structure, causing the structure to deform or even break, which will affect the reliability of the experiment and the use of workpieces, so it is of great significance to carry out dynamic stress and strain tests on the structure. Resistance strain measurement technology is widely used in the civil engineering industry, but the principles of modern physics technology are unknown to many practitioners. Data fusion technology or information fusion technology is also called multisensor fusion technology. Using computer technology to automatically analyze and integrate the data processing process of the detection data from multiple sensors according to time sequence and certain criteria, it can complete the required decision-making and judgment.

A computationally efficient adaptive online state-of-charge observer for Lithium-ion battery for electric vehicle

Due to the large number of cells installed in electric vehicle (EV), its battery management system (BMS) requires efficient state of charge (SOC) estimation algorithm. Since there is an impetus to reduce the computational burden (while retaining an acceptable accuracy), this paper proposes a simple and fast online adaptive observer for SOC estimation of Lithium-ion battery. The observer has several attractive features: first, its stability is proven by Lyapunov approach where asymptotic error convergence is guaranteed. Second, the computational requirements are low since it contains a few simple recursive equations without matrix inversion. Third, it is adaptive and achieves simultaneous online estimation of SOC and most of the battery parameters. The practical implementation using a 3 Ah battery proves the effectiveness of the proposed observer under dynamic stress test (DST). The testing with real EV profiles (supplemental federal test procedure which is known as US06 and federal urban driving schedule FUDS) is also performed to show the reliability. It is confirmed that the computation time of the proposed algorithm is reduced by approximately 2.5 times in compared to the extended Kalman filter-recursive least square (EKF-RLS) method. Despite the reduction in computation time, the errors are comparable to the latter. The low computational cost is significant when considering the need to accurately estimate the SOC of a large number of cells in a battery pack of an EV.

A novel feedback correction-adaptive Kalman filtering method for the whole-life-cycle state of charge and closed-circuit voltage prediction of lithium-ion batteries based on the second-order electrical equivalent circuit model

Accurate state of charge (SOC) and closed-circuit voltage (CCV) prediction is essential for lithium-ion batteries and their model performance. In this study, a novel feedback correction-adaptive Kalman filtering (FC-AKF) method is proposed for the online battery state co-prediction, which is adaptive to the whole-life-cycle of the lithium-ion battery based on the improved second-order equivalent circuit model (SO-ECM). For the feedback correction strategy, the optimized iterative state initialization is conducted using the uncertainty covariance matrix of the prior three-time points with the convergence of the updating process. The experimental results show that the SOC prediction error of the proposed FC-AKF method is 0.0099% and 0.975% compared with the ampere-hour integral method under the dynamic stress test (DST) and the Beijing bus dynamic stress test (BBDST) working conditions, respectively. Also, the CCV traction by the SO-ECM is 0.80 V and has fast initial convergence and quick prediction error reduction characteristics. The constructed iterative calculation model promotes the accurate SOC and CCV co-prediction effect, improving the safety and longevity of lithium-ion batteries with high precision and fast convergence advantages.

Co-estimation of parameters and state of charge for lithium-ion battery

• A recursive Bayesian algorithm is proposed for the online identification of battery parameters. • An online joint estimation algorithm of parameters and state of charge is proposed for the battery system. • Different initial SOC values and measurement noise covariance are applied to verify the robustness of the joint estimation method. • The proposed algorithm is validated under two dynamic current cycles. Accurate and reliable estimation of battery SOC is critical to enhance its service life and safety, and the accuracy of model parameter identification also directly affects the result of SOC estimation. This paper focuses on the parameter identification and SOC estimation of the dual-polarization. A co-estimation of recursive Bayesian algorithm and adaptive extended Kalman filter algorithm (RB-AEKF) is utilized to dynamically identify the model parameters and estimate the battery SOC. Firstly, the RB algorithm is used to identify model parameters through real-time current and voltage measurement data. The predicted voltage of the dual-polarization model is basically the same as the actual voltage, which better reflects the dynamic characteristics of the battery and the accuracy of the identification algorithm. In addition, adaptive noise variance updating algorithm is added to the extended Kalman filter to improve the accuracy of SOC estimation. Furthermore, a dataset consisting of data from a dynamic stress test (DST) and a federal urban driving schedule (FUDS) is used to verify the proposed method. The SOC estimafte error based on RB-AEKF stays within 2% and root mean square error is 0.01085 under FUDS test. Finally, we conduct the robustness analysis, and the results show that the algorithm has satisfactory robustness against inaccurate initial SOC and different measurement noise covariance.

Dynamic stress analysis of a metro bogie due to wheel out-of-roundness based on multibody dynamics algorithm

In metro vehicle systems, the dynamic stress and vibration of the bogie frame have a significant impact on the safety and reliability of vehicles. This study presents a fracture failure analysis of the bogie frame for the metro vehicle. The analysis consists of a bogie frame dynamic stress test, a wheel out-of-roundness (OOR) measurement and a vibration transmissibility test of the vehicle which is also used for operational modal analysis of the bogie frame. The field test result shows that the bending of cross beam combined with torsion of side beams(49.5 Hz) which are aroused by the wheel OOR mainly contribute to the fatigue failure of the bogie frame. To investigate the effect of wheel OOR on the fatigue failure of the bogie frame, the multibody system (MBS) model of the metro vehicle was developed. The MBS model accounts for the flexibility of the bogie frame and the nonlinearity of wheel-rail contact. Furthermore, the modal stress recovery method was adopted to study the dynamic responses of the bogie frame. The measured wheel OOR data is used to validate the efficiency of the model and investigate the characteristics of dynamic stress in time histories as well as frequency spectra. Moreover, the dynamic stress of fracture site in the bogie frame with consideration of ideal polygonal wear in radial deviation including different orders and variable amplitudes was investigated. The phase shift of wheel OOR between different wheels proves to be influential in the bogie frame vibration and thus the characteristics of dynamic stress change accordingly. The recommended limit values of wheel OOR amplitude are shown based on numerical results.

Research on Co-Estimation Algorithm of SOC and SOH for Lithium-Ion Batteries in Electric Vehicles

The accurate estimation of the state of charge (SOC) and state of health (SOH) is of great significance to energy management and safety in electric vehicles. To achieve a good trade-off between real-time capability and estimation accuracy, a collaborative estimation algorithm for SOC and SOH is presented based on the Thevenin equivalent circuit model, which combines the recursive least squares method with a forgetting factor and the extended Kalman filter. First, the parameter identification accuracy is studied under a dynamic stress test (DST) and the federal urban driving schedule (FUDS) test at different ambient temperatures (0 °C, 25 °C, and 45 °C). Secondly, the FUDS test is used to verify the SOC estimation accuracy. Thirdly, two batteries with different aging degrees are used to validate the proposed SOH estimation algorithm. Subsequently, the accuracy of the SOC estimation algorithm is studied, considering the influence of updating the SOH. The proposed SOC estimation algorithm can achieve good performance at different ambient temperatures (0 °C, 25 °C, and 45 °C), with a maximum error of less than 2.3%. The maximum error for the SOH is less than 4.3% for two aged batteries at 25 °C, and it can be reduced to 1.4% after optimization. Furthermore, calibrating the capacity as the SOH changes can effectively improve the SOC estimation accuracy over the whole battery life.

Electrolyte Modulators toward Polarization-Mitigated Lithium-Ion Batteries for Sustainable Electric Transportation.

Electric vehicles (EVs) are being adopted to replace combustion engine vehicles to reduce greenhouse gas emissions and mitigate climate change; developing batteries with high energy efficiency and long lifespan, in the context of carbon footprint and cost, are essential to ensure the successful transition. Herein, an electrolyte modulator that can effectively mitigate the polarization of lithium-ion batteries, leading to dramatically improved energy efficiency and lifespan, is reported. Under a dynamic stress test that mimics the operations of EVs, commercial pouch cells with a low concentration of electrolyte modulator (0.2 wt% of the electrolyte) exhibit enhanced energy efficiency (87.5% vs 80.4% for the first 500 testing cycles) and prolonged lifespan (fourfold improvement based on 70% energy-output retention). This work provides a simple yet effective strategy toward sustainable electrification of vehicles.

An Adaptive State of Charge Estimation Method of Lithium-ion Battery Based on Residual Constraint Fading Factor Unscented Kalman Filter

It is crucial to conduct highly accurate estimation of the state of charge (SOC) of lithium-ion batteries during the real-time monitoring and safety control. Based on residual constraint fading factor unscented Kalman filter, the paper proposes an SOC estimation method to improve the accuracy of online estimating SOC. A priori values of terminal voltage were fitted using cubic Hermite interpolation. In combination with the Thevenin equivalent circuit model, the method of adaptive forgetting factor recursive least squares is used to identify the model parameters. To address the problem of the UKF method strongly influenced by system noise and observation noise, the paper designs an improved method of residual constrained fading factor. Finally, the effectiveness of this method was verified by the test of Hybrid Pulse Power Characteristic and Beijing Bus Dynamic Stress Test. Results show that under HPPC conditions, compared with other methods, the algorithm in the paper estimates that the SOC error of the battery remains between -0.38% and 0.948%, reducing the absolute maximum error by 51.5% at least and the average error by 62.7% at least. Moreover, under the condition of Beijing Bus Dynamic Stress Test the algorithm estimates the SOC error of the battery stays between -0.811% and 0.526%, the SOC estimation errors are all within 0.2% after operation of ten seconds. Compared with other methods, the absolute maximum error can be reduced by 42.7% at least, the average error is reduced by 95% at least. Finally, the test proves that the method is of higher accuracy, better convergence and stronger robustness.

A Combined State of Charge Estimation Method for Lithium‐Ion Batteries Using Cubature Kalman Filter and Least Square with Gradient Correction

AbstractReliable state of charge (SOC) is the core of the energy storage system for electric vehicles. An efficient method of parameter identification and SOC estimation for lithium‐ion batteries (LIBs) using cubature Kalman filter (CKF) and least square with gradient correction is proposed. Firstly, the recursive gradient correction (RGC) strategy is introduced based on the recursive least square with forgetting factor (FFRLS) method, and a novel way is proposed for a simplified equivalent circuit model (ECM) based LIBs parameter identification using the combination of RGC and FFRLS method. Then, on the basis of CKF, the singular value decomposition (SVD) is used instead of the Cholesky decomposition to solve the non‐positive definiteness matrix for cubature transformation in the priori estimated covariance, thereby improving the effect of SOC estimation. Finally, the dynamic stress test (DST) and the federal urban driving schedule (FUDS) are used to verify the effect of the proposed method. The test results reflect that the combination of RGCFFRLS‐SVDCKF method and simplified ECM have advantages in terms of LIBs SOC estimation convergence ability and tracking accuracy, and the SOC estimation error can converge to 1% and 1.5% error boundary when the initial SOC are 0.5 and 0.8, respectively. In addition, the robustness of the proposed method is verified. Compared with the FFRLS‐EKF method, the proposed RGCFFRLS‐SVDCKF method has better precision and robustness to noise disturbance.

Online SoC Estimation of Lithium-Ion Batteries Using a New Sigma Points Kalman Filter

The accurate state of charge (SoC) online estimation for lithium-ion batteries is a primary concern for predicting the remaining range in electric vehicles. The Sigma points Kalman Filter is an emerging SoC filtering technology. Firstly, the charge and discharge tests of the battery were carried out using the interval static method to obtain the accurate calibration of the SoC-OCV (open circuit voltage) relationship curve. Secondly, the recursive least squares method (RLS) was combined with the dynamic stress test (DST) to identify the parameters of the second-order equivalent circuit model (ECM) and establish a non-linear state-space model of the lithium-ion battery. Thirdly, based on proportional correction sampling and symmetric sampling Sigma points, an SoC estimation method combining unscented transformation and Stirling interpolation center difference was designed. Finally, a semi-physical simulation platform was built. The Federal Urban Driving Schedule and US06 Highway Driving Schedule operating conditions were used to verify the effectiveness of the proposed estimation method in the presence of initial SoC errors and compare with the EKF (extended Kalman filter), UKF (unscented Kalman filter) and CDKF (central difference Kalman filter) algorithms. The results showed that the new algorithm could ensure an SoC error within 2% under the two working conditions and quickly converge to the reference value when the initial SoC value was inaccurate, effectively improving the initial error correction ability.

Polymer/expanded graphite-based flexible phase change material with high thermal conductivity for battery thermal management

Phase change materials, as smart, ideal latent heat storage, and passive technology, have become the promising option for thermal management. However, the melting leakage, poor mechanical property, and intrinsic low thermal conductivity are long-standing bottlenecks for practical applications. In this study, a synergetic method is proposed to fabricate phase change composites with excellent shape stability, flexible property, and high thermal conductivity. Here, paraffin is used as thermal energy storage material, styrene-ethylene-propylene-styrene served as supporting material provides a cross-linked network to restrict paraffin molecular and endow the composite with thermal-induced flexibility, and expanded graphite with lamellar structure constructs an interconnected thermally network. The thermal conductivities of composites reach up to 2.671–10.019 W m−1 K−1 with EG loading of 5–30 wt%. Simultaneously, the phase transition enthalpy is measured as high as 155.4–211.9 kJ kg−1, indicating that the composites have good thermal properties. In addition, the composites exhibit superior thermal management behavior by controlling the operating temperature of battery to below 50 °C under normal discharge-charge and dynamic stress test cycles. Therefore, this work offers a convenient and efficient method to synthesize scalable form-stable composite with promising performance for battery thermal management and other advanced thermal management applications.

A State of Charge Estimation of The Lithium-Ion Batteries Based on Reduced-Order Unscented Kalman Filtering Method

State of Charge (SOC) represents the available battery capacity and is one of the most important states that need to be monitored to optimize the performance and extend the lifetime of lithium-ion batteries. SOC estimation is a challenging task hindered by considerable changes in battery characteristics over its lifetime due to aging and distinct nonlinear behavior. This paper compares two of the basic methods and algorithms for SOC estimation of lithium-ion batteries (LIBs) focusing on the description of the two techniques in a test experiment and the elaboration of their differences for use in battery management systems (BMS) applications. A Reduced-order unscented Kalman filter method is used for estimation and tracking to realizer real-time high-precision estimation of lithium-ion battery state of charge. Experimental tests are carried out with a lithium-ion battery cell for model and state estimation validations. Many researchers have proposed different methods of estimating SOC that raised the challenge of establishing a relationship between the accuracy and robustness of the method. The experimental results of the OCV-SOC estimation method, Hybrid Pulse Power Characterization (HPPC) test, and Beijing Bus Dynamic Stress Test (BBDST) working condition method are analyzed. The error of SOC estimation based on the established Thevenin RC modeling using the Reduced-order-unscented Kalman filter is less than 0.3%. The result from the use of the reduced-order unscented Kalman filtering algorithm proves that it has high accuracy in the state of charge estimation of the lithium-ion batteries. Keywords : lithium-ion battery, open-circuit voltage, state of charge estimation, battery management system, HPPC test, reduced-order unscented Kalman filter DOI: 10.7176/JETP/11-6-05 Publication date: December 30 th 2021