Accurate Real Time On-Line Estimation of State-of-Health and Remaining Useful Life of Li ion Batteries

Abstract

Inaccurate state-of-health (SoH) estimation of battery can lead to over-discharge as the actual depth of discharge will be deeper, or a more-than-necessary number of charges as the calculated SoC will be underestimated, depending on whether the inaccuracy in the maximum stored charge is over or under estimated. Both can lead to increased degradation of a battery. Inaccurate SoH can also lead to the continuous use of battery below 80% actual SoH that could lead to catastrophic failures. Therefore, an accurate and rapid on-line SoH estimation method for lithium ion batteries, under different operating conditions such as varying ambient temperatures and discharge rates, is important. This work develops a method for this purpose, and the method combines the electrochemistry-based electrical model and semi-empirical capacity fading model on a discharge curve of a lithium-ion battery for the estimation of its maximum stored charge capacity, and thus its state of health. The method developed produces a close form that relates SoH with the number of charge-discharge cycles as well as operating temperatures and currents, and its inverse application allows us to estimate the remaining useful life of lithium ion batteries (LiB) for a given SoH threshold level. The estimation time is less than 5 s as the combined model is a closed-form model, and hence it is suitable for real time and on-line applications.

An Experimental Study on Prototype Lithium–Sulfur Cells for Aging Analysis and State-of-Health Estimation

“…This outcome demonstrate that the proposed estimation technique is relatively robust against the measurement noise error in UAH calculation and it can work under real working condition with RMSE around 5% (3.3% + 1.5% = 4.8%). Although, this level of accuracy is not perfect however, it is comparable with similar studies in the literature where an estimator is designed for Li-ion batteries [50], [51]. Figure 11: Sensitivity of estimation RMSE to UAH measurement error 5.…”

A Review of Lithium-Ion Battery State of Health Estimation and Prediction Methods

“…To ensure the performance requirements of lithium-ion batteries, IEEE standard 1188.1996 clearly states that when the battery SOH is less than 80%, it indicates that the end of life has been reached and the battery needs to be replaced [32][33][34].…”

An improved feedforward-long short-term memory modeling method for the whole-life-cycle state of charge prediction of lithium-ion batteries considering current-voltage-temperature variation

“…The state prediction of lithium-ion batteries can be realized by the characteristic analysis, which is useful for the effective energy supply process throughout the whole life-cycle aging process. On the discharge curves, both the state of health (SOH) and remaining useful life (RUL) are estimated accurately in real-time by combining the electrochemistry-based electrical and semi-empirical capacity fading models [44]. Based on the extended state observer of a generalized proportional-integral type, a reliable SOC prediction is observed for lithium-ion batteries [45].…”