A Novel Health Factor to Predict the Battery’s State-of-Health Using a Support Vector Machine Approach

Abstract

The maximum available capacity is an important indicator for determining the State-of-Health (SOH) of a lithium-ion battery. Upon analyzing the experimental results of the cycle life and open circuit voltage tests, a novel health factor which can be used to characterize the maximum available capacity was proposed to predict the battery’s SOH. The health factor proposed contains the features extracted from the terminal voltage drop during the battery rest. In real applications, obtaining such health factor has the following advantages. The battery only needs to have a rest after it is charged or discharged, it is easy to implement. Charging or discharging a battery to a specific voltage rather than a specific state of charge which is difficult to obtain the accurate value, so the health factor has high accuracy. The health factor is not dependent on the cycle number of the cycle life test of the battery and it is less dependent on charging or discharging current rate, as a result, the working conditions have less effect on the health factor. Further, the paper adopted a support vector machine approach to connect the healthy factor to the maximum available battery capacity of the battery. The experimental results show that the proposed method can predict the SOH of the battery well.

Online State-of-Health Estimation for Second-Use Lithium-Ion Batteries Based on Weighted Least Squares Support Vector Machine

“…With continuous improvement, SVMs can be applied to solve regression problems. Practical application has shown that SVMs exhibit good performance in regression problems, especially with dealing with high-dimensional function approximation problems [6].…”

“…An online stateof-health (SOH) estimation is the most important deciding factor for ensuring the safety of the second-use retired batteries. The SOH is a battery age metric that reflects the ability of a battery to store and deliver energy relative to its initial condition [6]. It can be expressed as:…”

Electric Vehicle Lithium-ion Battery Ageing Analysis under Dynamic Condition: A Machine Learning Approach

“…Real-time analysis is lacking. Terminal voltage drop plays a significant role in SOH estimation [36]. As battery ages, cycle no.…”

A State of Health Estimation Method for Lithium-Ion Batteries Based on Voltage Relaxation Model

“…In addition to the aforementioned SOH estimation methods based on the battery capacity and impedance estimation, which generally need to be implemented under a certain dynamic working condition, a novel SOH estimation method which utilizes the resting process after charging or discharging, that is, the relaxation voltage process, has been proposed in recent years. Various researchers have proved that the relaxation process has relationships with the SOH of battery, and the terminal voltage of the battery during relaxation process, namely the relaxation voltage, could be used to estimate the battery SOH [25][26][27][28][29]. Baghdadi et al [25] measured the relaxation voltage after fully charged and 30 min of rest and found a linear dependence between that value and the capacity value.…”

“…Similarly, He et al [26] and Qin et al [27] used the relaxation voltage value after fully charged and studied the relationship between the value and capacity of the battery after different aging cycles. Kai et al [28] utilized a period time of the relaxation voltage, that is, the relaxation voltage curve as the research subject. The relationship between the curve and the capacity after aging was studied and a support vector machine method was used to select characteristics of the curve as the health factor denoting the battery SOH.…”