State Of Health Indicator Research Articles

Battery State of Health Monitoring by Estimation of Side Reaction Current Density Via Retrospective-Cost Subsystem Identification

This paper introduces a new method to monitor battery state of health (SOH). In particular, the side reaction current density is estimated as a direct SOH indicator for the first time and its estimation is formulated as an inaccessible subsystem identification problem, where the battery health subsystem is treated as an inaccessible subsystem with the side reaction current density as the output. Inaccessibility in this context refers to the fact that the inputs and outputs of the subsystem are not measurable in situ. This subsystem is identified using retrospective-cost subsystem identification (RCSI) algorithm, and the output of the identified battery health subsystem provides an estimate for the side reaction current density. Using an example parameter set for a LiFePO4 battery, simulations are performed to obtain estimates under various current profiles. These simulations show promising results in identifying the battery health subsystem and estimating the side reaction current density with RCSI under ideal conditions. Robustness of the algorithm under nonideal conditions is analyzed. Estimation of the side reaction current density using RCSI is shown to be sensitive to nonideal conditions that cause errors in the measurement or estimation of the battery voltage. A method for quantitatively assessing the impact of nonideal conditions on the side reaction current estimation accuracy is provided. The proposed estimation technique, including the method for estimating the side reaction current density using RCSI and the framework analyzing its robustness, can also be applied to other parameter sets and other battery chemistries to monitor the SOH change resulting from any electrochemical-based degradation mechanism that consumes cyclable Li-ions.

Novel state-of-health diagnostic method for Li-ion battery in service

The development of improved State-of-Health (SoH) diagnostic methods is a current research topic for battery-powered applications. For instance, the current rapid development of Electric Vehicles (EV) creates a strong demand for an accurate and reliable on-board SoH indicator during operation. Such an indicator is a key parameter required to optimize battery energy management and to track the degradation of the system performance. The electrochemical impedance spectrum (EIS) of an electrochemical system is a powerful lab-based diagnostic technique, usually measured using a frequency response analyzer. In this paper, we present an innovative diagnostic technique based on analysis of free voltage and current signals to give a so called “quasi-electrochemical impedance spectrum” (QEIS) and demonstrate its application on a Li-ion battery during a real EV duty cycle. It is worth noting that in our technique no additional signal is applied to the cell, since the current flowing into cells during use on-board is directly processed in the data treatment step.Commercial batteries (1.4Ah cylindrical LiFePO4/graphite cell) were selected in this study to validate the diagnostic method in the framework of an applied case study related to an electric school bus demonstrator. In order to study the capability of QEIS measurements as a diagnostic tool for SoH of Li-ion cells, a test procedure including ageing phases has been defined to characterise Li-ion cells before and during ageing. Voltage and current signals were treated by Fast Fourier Transform (FFT) in order to determine the QEIS spectra of Li-ion cells under study. Then, SoH prediction algorithms have been obtained from a mathematical analysis of the impedance parameters sensitive to SoH.

Electrochemical Li-Ion Batteries Modelisation for EV

The future development of electric vehicles is linked with their batteries. In parallel of the actual research focused on the development of new materials and increase of batteries performances in terms of energy, power, cost, durability and weight, it is necessary to develop modeling tools. The simulations are helpful for improving the knowledge of both physical and chemical phenomena, optimize the battery design according with the user requirements, and reduce the test/validation phase. Mathematical models are also of crucial interest in the Battery Management System, in order to improve safety and performance during operation (in particular state of charge and state of health indicators).In the present study, the sensitivity and accuracy determination of basic parameters of the cell is first discussed. In Li-ion batteries the existence of the SEI layer and the formation of this layer during first cycles leads to unprecise knowledge of the initial insertion state of both electrodes [1] (and the faradaic balance between the two). Our work show that no heavy simulation models are needed to evaluate these parameters, provided that the thermodynamical insertion isotherms of the two electrodes are known. These can be determined at small current in a pouch cell configuration. Importantly, the shape of these isotherms has a strong influence on the accuracy of this strategy to evaluate the initial insertion states of both electrodes. The second part of this work revisits one of the most used porous electrodes based model to describe the behavior of lithium-ion batteries [2]. Numerous complex derivations [3] of this model are available in the literature, but relatively few works-to the best of our knowledge- analyze in details its predictive capability. To investigate the model, firstly, all the physical quantities are set in a dimensionless form, as commonly used in fluid mechanics: the parameters that act in the same or the opposite ways are regrouped and the total number of simulation parameter is greatly reduced. In a second phase, the influence of the parameter is discussed, and interpreted with the support of the limit cases. This analysis is based on galvanostatic and pulse/relaxation profiles (discharge voltage and concentration gradients) compared with tested commercial NMC/NCA LIB. The simulations are performed with the software Comsol® and the post-processing with MatLab®.

A support vector machine-based state-of-health estimation method for lithium-ion batteries under electric vehicle operation

Capacity and resistance are state-of-health (SOH) indicators that are essential to monitor during the application of batteries on board electric vehicles. For state-of-health determination in laboratory environment, standard battery performance tests are established and well-functioning. Since standard performance tests are not available on-board a vehicle, we are developing a method where those standard tests are applied virtually to a support vector machine-based battery model. This data-driven model is solely based on variables available during ordinary electric vehicle (EV) operation such as battery current, voltage and temperature. This article contributes with a thorough experimental validation of this method, as well as the introduction of new features – capacity estimation and temperature dependence. Typical EV battery usage data is generated and exposed to the suggested method in order to estimate capacity and resistance. These estimations are compared to direct measurements of the SOH indicators with standard tests. The obtained estimations of capacities and instantaneous resistances demonstrate good accuracy over a temperature and state-of-charge range typical for EV operating conditions and allow thus for online detection of battery degradation. The proposed method is also found to be suitable for on-board application in respect of processing power and memory restrictions.

Study of the Strain Evolution of Lithium-Ion Cells to Provide a State of Charge and State of Health Indicators

not Available.

Online parameter identification for real-time supercapacitor performance estimation in automotive applications

This paper focuses on synthesizing a real-time adaptive process for supercapacitor performance estimation using a dynamic model describing the SC behavior which can vary within each experiment. We develop a simple and linear-recursive model that proved its efficiency regarding the comparison between simulation results and real data from power cycling tests. Based on a recursive least squared algorithm with a time-variant forgetting factor, the on-line estimation of the dynamic supercapacitor-model parameters, mainly the internal resistance, served as a state of health indicator. Model shows very good performances since the maximum relative modeling error do not exceed 3%. Results from state of health indicator are compared to those issued from IEC standard and electrochemical impedance spectroscopy methods.

Towards Onboard Li-ion Battery State-of-health Diagnosis by a Virtual Sensor

The development of improved State-of-Health (SoH) diagnosis methods is a current research topic for battery-powered applications. For instance, Hybrid Electric Vehicles (HEV) are in rapid extension and create a strong demand for an accurate and reliable SoH indicator during operation. Such an indicator is a key parameter required to optimize battery energy management and to track the degradation of the system performances. Electrochemical impedance spectroscopy is one of the most promising methods for characterizing ageing effects of secondary batteries online because it can be sensitive to different ageing mechanisms. This paper presents a method to measure the electrochemical transfer function of the cell at different frequencies simultaneously with the data treatment of free current and voltage during operation. Impedance parameters were then estimated by a non linear least square algorithm. Based on this approach, an experimental investigation of ageing effects of a lithium ion battery was carried out. After 1400 HEV cycles, the battery showed a 7 % decreased capacity, and 8 % increased resistance at 0.5 Hz. Modelling results show that charge transfer resistance have a similar evolution in function of ageing time than the resistance of battery measured by electrochemical impedance spectroscopy. To our knowledge on the topic, this constitutes a substantial step towards SoH indicator that could be reliable and precise enough under realistic user conditions.

Evaluating Real-Life Performance of Lithium-Ion Battery Packs in Electric Vehicles

In regard to the increasing market launch of plug-in hybrid electric vehicles (PHEVs), understanding battery pack performance under electric vehicle (EV) operating conditions is essential. As lifetime still remains an issue for battery packs, it is a necessity to monitor the battery pack's state-of-health (SOH) on-board. Standard performance tests for health evaluation do not apply since operation interruptions and additional testing equipment are beyond question during ordinary EV usage. We suggest a novel methodology of performance estimation from real-life battery data. On the basis of battery pack data collected during PHEV operation, a support vector machine model is constructed that serves as source for performance evaluation figures. The SOH indicator "10 s discharge resistance" as known from hybrid pulse power characterization (HPPC) tests is chosen to exemplify how performance degradation can be followed over a year.

Evaluating Real-Life Performance of Lithium-Ion Battery Packs in Electric Vehicles

In regard to the increasing market launch of plug-in hybrid electric vehicles (PHEVs), understanding battery pack performance under electric vehicle (EV) operating conditions is essential. As lifetime still remains an issue for battery packs, it is a necessity to monitor the battery pack's state-of-health (SOH) on-board. Standard laboratory performance tests for health evaluation do not apply since operation interruptions and additional testing equipment are out of the question during ordinary EV usage. We suggest a novel methodology of performance estimation from real-life battery data. On the basis of battery pack data collected during PHEV operation, a support vector machine model capturing battery behavior characteristics is constructed. By virtually testing this battery model, access to standard performance evaluation figures can be gained. The SOH indicator “10 s discharge resistance” as known from hybrid pulse power characterization (HPPC) tests is chosen to exemplify how performance can be followed over a year.

Standby Power System VRLA Battery Reserve Life Estimation Scheme

This paper presents a valve-regulated lead acid (VRLA) battery reserve life estimation scheme. The scheme is adaptive in both type and frequency of involvement. The scheme is based on capacity trending with the support of a number of state-of-health (SOH) indicators. These SOH indicators include accumulated thermal stress, float voltage, and indicators acquired from the start-of-discharge (coup de fouet) region. An implementation test case is given, indicating that the scheme is capable of providing an accurate estimation of reserve life long before the end of life. Furthermore, the estimation accuracy improves as the end of battery life approaches.