Real-Time State-of-Health Estimation of Lithium-Ion Batteries Based on the Equivalent Internal Resistance

Tan, Xiaojun; Tan, Yuqing; Zhan, Di; Yu, Ze; Fan, Yuqian; Qiu, Jianzhi; Li, Jun

doi:10.1109/access.2020.2979570

Cited by 74 publications

(28 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A mathematical model or a state space model is usually first developed to characterize the electrochemical mechanisms or electrical behavior properties. A nonlinear state estimator or adaptive filter technique, such as extended Kalman filter (EKF) or particle filter (PF), is utilized to update the parameters of the developed model [4][5][23][24][25][26][27][28][29][30][31][32][33].…”

Section: A Model Based Methodsmentioning

confidence: 99%

Lithium-Ion Battery Ageing Behavior Pattern Characterization and State-of-Health Estimation Using Data-Driven Method

Xia

Qahouq

2021

IEEE Access

View full text Add to dashboard Cite

This paper presents a study on Lithium-ion battery aging behaviors/patterns and related Stateof-Health (SOH) indicators before presenting the development of data-driven based SOH estimators. Battery charge/discharge cycling experiments are conducted in order to obtain needed data for this work. The battery ageing behavior patterns until the battery cell reaches highly deteriorated health conditions are investigated and characterized in this paper by analyzing the aggregated battery ageing data. The observed battery ageing behavior patterns include: (1) the rate at which the battery voltage decreases during discharging increases as the battery ages, (2) the speed at which the battery terminal voltage increases during Constant Current (CC) charging increases as the battery's health deteriorates, (3) the time period for CC charging operation decreases as the battery ages, (4) the rate at which the battery current decreases during Constant Voltage (CV) charging increases as the battery ages, and (5) the speed at which the battery temperature drops during CV charging increases as the battery ages. Corresponding SOH indicators are developed to quantify these battery ageing behavior patterns for the development of SOH estimators. Deep Neural Network (DNN) is utilized to extract and model the nonlinear and complex correlation between the defined SOH indicators and SOH values of the Lithium-ion battery. Multiple DNN-based SOH estimators are developed in this paper. The SOH estimation results from different DNN-based SOH estimators indicate that the diversity of SOH indicators used for the development of SOH estimator can substantially improve the estimation performance.

show abstract

Section: A Model Based Methodsmentioning

confidence: 99%

Lithium-Ion Battery Ageing Behavior Pattern Characterization and State-of-Health Estimation Using Data-Driven Method

Xia

Qahouq

2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…KPSS Processes 2021, 9, 795 6 of 16 test is to remove the intercept term and trend term from the sequence to be tested to construct the statistic LM. The inspection process is shown in Equation (5).…”

Section: Second-order Stationarity Testmentioning

confidence: 99%

“…The estimation methods of battery SOH include empirical-based methods [1][2][3][4], modelbased methods [5,6], and data-driven methods [7][8][9][10]. Empirical-based methods include the cycle number method, ampere-hour method, weighted ampere-hour method, and event-oriented aging accumulation method.…”

Section: Introductionmentioning

confidence: 99%

A Novel Autoregressive Rainflow—Integrated Moving Average Modeling Method for the Accurate State of Health Prediction of Lithium-Ion Batteries

Huang

Wang

et al. 2021

Processes

View full text Add to dashboard Cite

The accurate estimation and prediction of lithium-ion battery state of health are one of the important core technologies of the battery management system, and are also the key to extending battery life. However, it is difficult to track state of health in real-time to predict and improve accuracy. This article selects the ternary lithium-ion battery as the research object. Based on the cycle method and data-driven idea, the improved rain flow counting algorithm is combined with the autoregressive integrated moving average model prediction model to propose a new prediction for the battery state of health method. Experiments are carried out with dynamic stress test and cycle conditions, and a confidence interval method is proposed to fit the error range. Compared with the actual value, the method proposed in this paper has a maximum error of 5.3160% under dynamic stress test conditions, a maximum error of 5.4517% when the state of charge of the cyclic conditions is used as a sample, and a maximum error of 0.7949% when the state of health under cyclic conditions is used as a sample.

show abstract

“…voltage (OCV) [20,21], electromotive force [22], internal resistance [23], and impedance spectroscopy [24]. Though generally simple and readily implementable, they have great strict requirements for experimental conditions and time.…”

mentioning

confidence: 99%

An Effective Method for Estimating State of Charge of Lithium-Ion Batteries Based on an Electrochemical Model and Nernst Equation

2020

View full text Add to dashboard Cite

Lithium-ion batteries are generally regarded as a leading candidate for energy storage systems. The safe and reliable operation of lithium-ion batteries depends largely on accurate estimation of the state of charge (SOC), which requires an accurate battery model. Bearing strong mechanisms, the electrochemical model (EM) can mimic the battery dynamics with high fidelity, and thus the EM-based methods can produce more reliable SOC estimates. This paper proposes a novel EM-based SOC estimation method for lithium-ion batteries from the electrochemical mechanism perspective. Firstly, a single particle model is employed to gain a direct insight into the electrochemical reactions inside the battery, and it is found that the model output voltage and SOC are strongly related to the lithium-ion concentrations of solid phases. A simple negative voltage feedback module is then applied to observe the voltage error between the cell referenced terminal voltage and the model output voltage. To eliminate the voltage error and achieve a precise estimate, a quantitative relationship between the voltage error and corrected amount of lithium-ion concentrations is deduced based on the Nernst equation. The performance of proposed method has been systemically evaluated under different operating conditions, including various charging and discharging current rates, erroneous initial SOCs, and cell aging levels. Although an erroneous initial SOC of 50% is applied to the proposed algorithm, promising estimates with the mean absolute errors of 0.22% and 1.35% can be still achieved under the constant and dynamic loading conditions, respectively. INDEX TERMS Lithium-ion battery, state of charge (SOC), electrochemical model, Nernst equation I. INTRODUCTION With numerous advantages, such as high energy density, low self-discharging rate, no memory effects, and long lifespan, and so on, lithium-ion batteries are generally regarded as a leading energy storage candidate for electric vehicles, renewable energy systems, portable electronic devices, and many other applications [1-3]. In most lithium-ion battery energy storage systems, a battery management system (BMS) is employed to manage the performance and maintain the longevity of battery cells [1, 4, 5]. The functions of a BMS include mainly cell voltage detection and balancing [6, 7], estimation of state of charge (SOC) [8-11] and state of health (SOH) [12-14], and thermal management [15-18]. Defined as the ratio of the residual charge stored in a battery to its maximum available capacity, the SOC is one of the primary and critical parameters for the development of battery management strategies [19]. Accurate SOC estimation plays a vital role in controlling the safe charging and discharging of the battery while guaranteeing its efficiency. However, the SOC cannot be measured directly and accurately by sensors like ordinary physical quantities in practices. Despite the huge research efforts, the battery SOC estimation still poses a significant challenge due to the complexity and nonlinearity of e...

show abstract

Real-Time State-of-Health Estimation of Lithium-Ion Batteries Based on the Equivalent Internal Resistance

Cited by 74 publications

References 42 publications

Lithium-Ion Battery Ageing Behavior Pattern Characterization and State-of-Health Estimation Using Data-Driven Method

Lithium-Ion Battery Ageing Behavior Pattern Characterization and State-of-Health Estimation Using Data-Driven Method

A Novel Autoregressive Rainflow—Integrated Moving Average Modeling Method for the Accurate State of Health Prediction of Lithium-Ion Batteries

An Effective Method for Estimating State of Charge of Lithium-Ion Batteries Based on an Electrochemical Model and Nernst Equation

Contact Info

Product

Resources

About