Battery State of Health Estimation and Incremental Capacity Analysis under General Charging Profiles Using Neural Networks

Abstract: Incremental capacity analysis (ICA) and differential voltage analysis (DVA) are two effective approaches for battery degradation monitoring. One limiting factor for their real-world application is that they require constant-current charging profiles. This research removes this limitation and proposes an approach that enables ICA/DVA-based degradation monitoring under general charging profiles. A novel concept of virtual incremental capacity (VIC) and virtual differential voltage (VDV) is proposed. Then, two related convolutional neural networks (CNNs), called U-Net and Conv-Net, are proposed to construct VIC/VDV curves and estimate the state of health (SOH) from general charging profiles across any state-of-charge (SOC) ranges that satisfy some constraints. Finally, for onboard implementations, two CNNs called Mobile U-Net and Mobile-Net are proposed as replacements for the U-Net and Conv-Net, respectively, to reduce the computational footprint and memory requirements. Using an extensive experimental dataset of battery modules, the proposed CNNs are demonstrated to provide accurate VIC/VDV curves and enable ICA/DVA-based battery degradation monitoring under various fast-charging protocols and different SOC ranges.