Satellite Lithium-Ion Battery Remaining Cycle Life Prediction with Novel Indirect Health Indicator Extraction

Abstract

Prognostics and remaining useful life (RUL) estimation for lithium-ion batteries play an important role in intelligent battery management systems (BMS). The capacity is often used as the fade indicator for estimating the remaining cycle life of a lithium-ion battery. For spacecraft requiring high reliability and long lifetime, in-orbit RUL estimation and reliability verification on ground should be carefully addressed. However, it is quite challenging to monitor and estimate the capacity of a lithium-ion battery on-line in satellite applications. In this work, a novel health indicator (HI) is extracted from the operating parameters of a lithium-ion battery to quantify battery degradation. Moreover, the Grey Correlation Analysis (GCA) is utilized to evaluate the similarities between the extracted HI and the battery's capacity. The result illustrates the effectiveness of using this new HI for fading indication. Furthermore, we propose an optimized ensemble monotonic echo state networks (En_MONESN) algorithm, in which the monotonic constraint is introduced to improve the adaptivity of degradation trend estimation, and ensemble learning is integrated to achieve high stability and precision of RUL prediction. Experiments with actual testing data show the efficiency of our proposed method in RUL estimation and degradation modeling for the satellite lithium-ion battery application.

Indirect State-of-Health Estimation for Lithium-Ion Batteries under Randomized Use

“…For example, Saha et al [19] used the battery's impedance as the HI for the SOH of battery, and Tong et al [20] considered the open-circuit voltage, both under the circumstances of constant discharge. Moreover, Liu et al [21] discovered that the time interval of equal discharging voltage difference in each discharge cycle can be used as a HI to represent the capacity degradation; Li et al [22] used the temperature-change rate as the battery's HI. For the HIs extracted under a constant charge process, some contributions are worthy of attention.…”

“…From Figure 3, we can also observe that when the load was constant, the voltage trajectory decreased in a very clear way, from which indirect HIs could be easily extracted. For example, in [21], the time interval of an equal discharging voltage difference in each discharge cycle was decreased very clearly as the battery aged, which can be used as an indirect HI for SOH estimation. However, when the load is random, the voltage trajectory can be very irregular.…”

Prediction of Li-Ion Battery Discharge Characteristics at Different Temperatures and Discharge Rates Based on a Hybrid Soc Estimation Approach

“…C Id is the total capacity that can be drawn from the battery when it is discharged at a room temperature of 20°C at I d discharge rate (current). Usually a second-order polynomial is used to describe or model the temperature variations and a polynomial of order four is used for discharge rate variations [7]. i.…”

“…A generic datadriven, model-free approach that integrates an artificial neural network with a dual extended Kalman filter (DEKF) algorithm for lithium-ion battery health management has been dealt in [6]. In [7], Datong Liu et al proposed a complex Relevance Vector Machine (RVM) -Particle Filter (PF) approach in which Time interval between equal discharge voltage differences (TIEDVD) is used as health indicator. Since accuracy of prediction is important for satellite applications we planned to use a hybrid approach with UKF which is well known for non-linear system estimations and predictions.…”

Prognostics of Lithium-Ion Batteries Based on Battery Performance Analysis and Flexible Support Vector Regression

“…In light of this fact, environmental problems and global warming are becoming increasingly serious issues owing to the depletion of fossil fuels; therefore, there is an urgent need for new energy sources to be able to meet the daily requirements of energy across the globe [1]. As compared with other types of batteries, lithium-ion batteries have high energy density, a long lifetime, stable electrochemical properties, the ability to store electrical energy with low loss, and no memory effect [2]. Despite their overall advantages, their rated capacity will fade over repeated charge and discharge cycles [3].…”