From Grayscale Image to Battery Aging Awareness—A New Battery Capacity Estimation Model With Computer Vision Approach

Accurate detection of capacity degradation is critical to the safe and efficient utilization of battery systems. Many data-driven capacity estimators were proposed based on emerging intelligent algorithms, but their accuracy depends on the data of complete charged/discharged process and complex algorithm structures. This article developed a computer vision (CV)-based method, constructing battery multidimensional aging features as the key image to estimate capacity using specific charging data segment. Specifically, the designed image-aging recognition method is used to extract multidimensional aging features from the partial charging current sequence and then establish map inputs for a computer vision model that recognizes the constructed feature maps. Consequently, the mapping relationship between the charging information and capacity degradation can be obtained as the 2-D grayscale images that contain massive extracted features in their small size hence greatly simplify the network structure in CV model so as to improve estimation accuracy and efficiency significantly. More importantly, since the model input is a specific charging current segment rather than the data of complete charging process, the model applicability to the random and incomplete charging process of electric vehicles can be greatly improved. Battery cycling data from different types of Li-ion cells were utilized for performance verification. Compared with the conventional estimation methods proposed previously, the proposed method demonstrates the great superiority in terms of the model applicability, estimation accuracy, and computational efficiency for online capacity estimation in actual battery usage.

I. Introduction

With the rapid development and the continuous popularization of electronic equipment, battery energy storage system, as the main energy provider, is becoming more and more indispensable [1]. Particularly, lithium-ion batteries have been widely used in electronic appliances, electric vehicles (EVs), and grid energy storage systems due to high energy density, outstanding using safety, and low manufacturing cost [2]. As the number of charge/discharge cycles rises in such applications, battery capacity deterioration is unavoidable owing to the expansion of the solid electrolyte interface layer and the loss of available lithium inventory, which invariably leads to system failure and safety concerns [3]. Therefore, it is significant to detect the capacity degradation of batteries accurately for ensuring their safe and reliable operation. State of health (SOH) represents the aging condition of a battery and is defined as the ratio of cell capacity at recent charge/discharge cycle to the initial capacity [4]. However, since it is difficult to directly measure cell capacity while the battery system is in operation, the estimating procedure can only be accomplished using a few fundamental quantifiable signal characteristics. For this issue, a variety of capacity estimation methods are proposed, which can be roughly divided into two categories, namely model-based methods and data-driven methods [5].