Deep Learning Powered Online Battery Health Estimation Considering Multitimescale Aging Dynamics and Partial Charging Information

Online accurate battery state-of-health (SOH) estimation is crucial for ensuring safe and reliable operations of electric vehicles (EVs). Yet, such estimation problem remains a challenge in reality due to complex battery degradation behaviors and dynamic EV operations. This article proposes a novel deep learning-based framework, a bilateral-branched visual transformer with dilated self-attention (Bi-ViT-DSA), for online SOH estimation. The proposed framework considers partial charging segments during incomplete charging based on two mainstream charging modes, the multistage fast-charging (MSFC) and constant-current constant-voltage (CCCV) charging. To incorporate multitimescale battery aging dynamics into SOH estimation, a novel biparty input structure is developed to convey both inner cycle and intracycle degradation information from raw data. The proposed Bi-ViT-DSA is developed to learn multitimescale high-level latent features from the biparty input in parallel for SOH estimation. A dilated self-attention (DSA) mechanism is developed to reduce redundant operations in modeling. Computational studies are conducted on datasets of batteries under different chemistries and test conditions. Results validate the feasibility and robustness of the proposed method and its superior performance over a set of state-of-the-art benchmarks.