Hybrid Pulse Power Characteristic Test Research Articles

State-of-Charge Estimation of Nickel–Cadmium Batteries Based on Dynamic Modeling of Electrical Characteristics and Adaptive Untrace Kalman Filtering

With the increasing demand for intelligence and automation, and the continuous strengthening of safety and efficiency requirements, the disadvantages of traditional “blind use” of nickel–cadmium batteries have become increasingly prominent, and the lack of state-of-charge (SOC) estimation needs to be changed urgently. For this purpose, a dynamic model of nickel–cadmium battery is established, and an SOC estimation method of nickel–cadmium battery based on adaptive untraced Kalman filter is proposed. Firstly, the experimental platform was built, and the open-circuit voltage and polarization characteristics of nickel–cadmium batteries were analyzed. On this basis, an equivalent circuit model is constructed to reflect the characteristics of nickel–cadmium batteries, and the model parameters were identified by the hybrid pulse power characteristic test; Then, based on the dynamic model, the SOC of the nickel–cadmium battery was estimated by combining with the Sage–Husa adaptive untrace Kalman filtering algorithm. Finally, the SOC estimation effect was verified under two operating conditions: Hybrid pulse power characteristic (HPPC) and constant cyclic charging and discharging power. The experimental results show that the proposed estimation method is insensitive to the initial value of SOC, and can still converge to the real value even if there is 30% error in the initial value. The mean absolute error and root mean square deviation of the final SOC estimation results are both less than 1%. The dynamic model and the proposed SOC estimation method provide valuable reference for the operation control, maintenance, and replacement of nickel–cadmium batteries in the use process.

Effects of stack pressure on the equivalent circuit model of lithium-ion batteries

The stack-level mechanical pressure is inevitable in battery pack and it has a significant effect on battery characteristics. However, parameters variation of equivalent circuit models (ECMs) caused by stack pressure has seldom been considered. For the first time, the effect of stack pressure on ECM is investigated in detail with commercial Li[NiCoMn]O2 (NMC) cells and LiFePO4 (LFP) cells in this paper. The changes in model parameters and their influence on model accuracy are evaluated through a test procedure including capacity tests, a modified Hybrid Pulse Power Characteristic test, and the dynamic current test. The results of the comprehensive experiments show that all the parameters of ECM vary significantly with stack pressures. Voltage errors in model simulations suggest that the pressure-induced parameter differences reduce the model accuracy. To further demonstrate the effects, the SOC estimation is carried out, which indicate the negative impact of such parameter variations on model application. These results are instructive for the application of the ECM in battery management.

Optimization of hybrid pulse power characterization profile for equivalent circuit model parameter identification of Li-ion battery based on Taguchi method

The modeling of Li-ion batteries is crucial for their stable and efficient operation. The equivalent circuit model (ECM) is the most widely used battery model, for which parameter identification usually involves the hybrid pulse power characteristic (HPPC) test. However, since the HPPC test was designed to determine dynamic power capability of batteries, an investigation of how HPPC parameters affect ECM parameter identification would be reasonable. In this study, the Taguchi method was employed to investigate the effect of four HPPC parameters (positive and negative pulse height, length of pulse and length of relaxation) on ECM's performance under various application conditions (dynamic test, non-dynamic test and quasi-static test). The results show that all four parameters have an effect on ECM performance. For batteries operated only with the predictable charge and discharge patterns, the optimal combination of HPPC parameters values was determined based on Taguchi experiments. If batteries face unpredictable application scenarios, lower positive pulse heights, higher negative pulse heights, shorter pulse lengths and longer relaxation lengths within a certain range should be considered. Compared to established standard parameter values, this makes it possible to build a more accurate general model.

A framework for battery temperature estimation based on fractional electro-thermal coupling model

The temperature significantly affects the performance and safety of the battery. Therefore, this paper presents a novel framework for the estimation of the battery temperature based on a fractional-order electro-thermal coupling model. Due to the nonlinearity, coupling, and time varying parameters of lithium-ion batteries, the fractional-order equivalent circuit model is established using the fractional-order differential theory. Then it couples with the thermal model to establish the fractional order electro-thermal coupling model of lithium battery. Based on the model, the hybrid pulse power characteristic test method and the electro-thermal balance experiment are used to identify the parameters of the fractional order electro-thermal coupling model of lithium batteries. Finally, the fractional-order electro-thermal coupling model is used to estimate battery voltage, state of charge, and temperature under the conditions of the hybrid pulse power characteristic and the world transient vehicle cycle. The results show good consistency and responsiveness with the experiment under steady and dynamic conditions, and compared with the ETM, the characteristics of small error and low error rates of the F-ETM are introduced. Therefore, the fractional electrothermal coupling model of lithium batteries has good accuracy and applicability.

Preface

The 2022 2nd International Conference on Energy, Power and Electrical Engineering (EPEE 2022), hosted by East China Jiaotong University and Anhui University, was successfully held on September 23rd-25th, 2022 in Hangzhou, China (virtual conference). EPEE 2022 promoted information exchange among scientists and engineers, as well as communication and collaboration between fundamental researchers and those engaged in the development of practical technology in areas of energy, power and electrical engineering.We were honored to have both Prof. Yinquan Yu from East China Jiaotong University, China and Prof. Cungang Hu from Anhui University, China to serve as our Conference Chairmen. Divided into three parts, the conference covered keynote speeches, oral presentations, and online Q&A discussion, attracting 200 individuals. Firstly, keynote speakers were each allocated 30-45 minutes to hold their speeches. Then in the oral presentations, the excellent papers we had selected were presented by their authors by turns.During the conference, seven sophisticated professors were invited to address their keynote speeches. Among them, Prof. Shubin Yan from Zhejiang University of Water Resources and Electric Power, China delivered a speech on the topic: Hydrogen Energy Development in the Context of Carbon Neutrality. He focused on the development of efficient and inexpensive catalysts and advanced battery materials, with the aim of establishing links with industries and construction industries that are difficult to decarbonize deeply, so as to enrich the application scenarios of hydrogen. Additionally, Prof. Zhigang Liu from Southwest Jiaotong University, China gave a report on the Analysis and Suppression of Harmonic Instability between EMUs and Traction Network in High-speed Railways. This presentation focused on the key issues such as small-signal impedance modelling, harmonic instability mechanism, and instability suppression strategy of EMUs converter and traction network. Last but not the least, Prof. Kai Wang from Qingdao University, China shared with us his study: Research on Life Prediction of Supercapacitor Based on Improved Long-term and Short-term Memory Neural Network, which applied different charge and discharge strategies based on different temperatures and voltages to perform a steady-state cycle life test and Hybrid Pulse Power Characteristic test on the supercapacitors to make the measured data more real and effective. Their brilliant speeches had triggered heated discussion. Besides, every participant praised this conference for disseminating useful and insightful knowledge.List of Committee member is available in this pdf.

A Numerical Study of Lithium-ion Battery Degradation Feature Extraction based on Transfer Function

Lithium-ion battery has been widely applied in various industrial fields including electrical vehicles (EVs), portable electronics and spacecraft. As the key part of the power supply system, the health state of the battery directly influence the reliability and safety of the host system. The degradation feature extraction approach is the vital part for battery state of health (SOH) estimation. The lithium-ion battery can be equalized as an electric circuit consists of a current source and several capacitances and resistances. These parameters varies based on the battery state of charge (SOC) and SOH. In other words, the battery performance degradation leads to the variation of the parameters in battery equivalent circuit model (ECM) which will directly influence the battery transfer function. Thus, this paper quantitative analysis of degradation characterization capability of parameters in battery equations. For the first-order ECM, the transfer function is established and the parameters are identified based on the battery hybrid pulse power characteristic test. Three different kinds of correlation analysis methods are applied in to evaluate the effectiveness of the parameters in transfer function for degradation representation.

A novel endurance prediction method of series connected lithium-ion batteries based on the voltage change rate and iterative calculation

High-power lithium-ion battery packs are widely used in large and medium-sized unmanned aerial vehicles and other fields, but there is a safety hazard problem with the application that needs to be solved. The generation mechanism and prevention measurement research is carried out on the battery management system for the unmanned aerial vehicles and the lithium-ion battery state monitoring. According to the group equivalent modeling demand of the battery packs, a new idea of compound equivalent circuit modeling is proposed and the model constructed to realize the accurate description of the working characteristics. In order to realize the high-precision state prediction, the improved unscented Kalman feedback correction mechanism is introduced, in which the simplified particle transforming is introduced and the voltage change rate is calculated to construct a new endurance prediction model. Considering the influence of the consistency difference between battery cells, a novel equilibrium state evaluation idea is applied, the calculation results of which are embedded in the equivalent modeling and iterative calculation to improve the prediction accuracy. The model parameters are identified by the Hybrid Pulse Power Characteristic test, in which the conclusion is that the mean value of the ohm internal resistance is 20.68 mΩ. The average internal resistance is 1.36 mΩ, and the mean capacitance value is 47747.9F. The state of charge prediction error is less than 2%, which provides a feasible way for the equivalent modeling, battery management system design and practical application of pack working lithium-ion batteries.

State of Charge Estimation of Lithium-Ion Batteries Using a Discrete-Time Nonlinear Observer

In this paper, a novel approach using the discrete-time nonlinear observer (DNLO) is proposed for state-of-charge (SOC) estimation of lithium-ion batteries (LIBs). To design the DNLO for SOC estimation, the state equations based on a second-order resistor–capacitor equivalent circuit model are derived to simulate the dynamic behaviors of an LIB. Considering the hysteresis characteristic of the battery, the model parameters depend on the SOC and the direction of battery current simultaneously, and then, the exponential-function fitting method is adopted to identify the offline results of the parameters. The ninth-order polynomial function is adopted to represent the highly nonlinear relationship between the open-circuit voltage and the SOC. The Lyapunov stability theory is used to prove the convergence of the proposed DNLO. The performance of the proposed method is also verified by the experiments based on the hybrid pulse power characteristic test, which indicates that compared with the extended Kalman filter (EKF) and the discrete-time sliding mode observer (SMO) algorithms, the proposed observer has better performance in reducing the computation cost, improving the estimation accuracy and enhancing the convergence capability.

A Synthesized Diagnosis Approach for Lithium-ion Battery in Hybrid Electric Vehicle

A synthesized diagnosis approach for the lithium-ion battery is proposed with fully understanding of the internal failure mechanism, which is suitable for dynamic conditions such as hybrid electric vehicles. In order to detect and distinguish different fault modes and bridge external electrical parameters with internal chemical mechanisms, a serial of abusive experiments, including overcharge, over-discharge, and low-temperature operation, which commonly occur during battery applications, are arranged. Fault symptoms in the form of electrical parameter variation are extracted with reference performance tests, which consist of hybrid pulse power characteristic test, federal urban driving schedule test, and incremental capacity analysis. The proposed diagnosis approach not only indicates the occurrence of fault but provides a detailed description of symptoms and physical meaning for internal mechanisms as well.

A new method of modeling and state of charge estimation of the battery

Accurately estimating the State of Charge (SOC) of the battery is the basis of Battery Management System (BMS). This paper has introduced a new modeling and state estimation method for the lithium battery system, which utilizes the fractional order theories. Firstly, a fractional order model based on the PNGV (Partnership for a New Generation of Vehicle) model is proposed after analyzing the impedance characteristics of the lithium battery and compared with the integer order model. With the observability of the discrete non-linear model of the battery confirmed, the method of the state observer based on the extended fractional Kalman filter (EFKF) and the least square identification method of battery parameters are studied. Then, it has been applied successfully to estimate the battery SOC using the measured battery current and voltage. Finally, a standard HPPC (Hybrid Pulse Power Characteristic) test is used for parameter identification and several experimental validations are investigated on a ternary manganese-nickel-cobalt lithium battery pack with a nominal capacity of 24 Ah which consists of ten Sony commercial cells (US18650GR G7) in parallels. The results demonstrate the effectiveness of the fractional order model and the estimation method.

A novel model of the initial state of charge estimation for LiFePO4 batteries

The State-of-Charge (SOC) is an important performance parameter and evaluation index in rechargeable battery energy storage systems. Here, a novel smart estimation method based on coulomb counting is proposed in order to enhance estimation accuracy. Firstly, an enhanced model for the initial SOC (SOC0) estimation based on dynamic multi-parameter method is investigated, because SOC0 plays a key role in calculating real SOC in-time. All the design limits, such as voltage, temperature, are used as its constraints. And more importantly, the relaxation effect also is considered. The SOC0 with the main parameters satisfies Gauss function. Secondly, the quantitative relations of the energy efficiencies are measured and analyzed under the moderate discharging situation. The results show a negative quadratic correlation between energy efficiency and rate, and exhibit a reduction in energy efficiency as rate increases. Lastly, a test with several consecutive hybrid pulse power characteristic test profiles is carried. The experimental results indicate that the correction of SOC0 can efficiently limit the error below 4%, and also exhibit that considering on the effect of energy efficiency can further reduce its estimation error.