Voltage and temperature information ensembled probabilistic battery health evaluation via deep Gaussian mixture density network

Abstract

Accurate state-of-health (SOH) estimation of lithium-ion batteries is crucial for ensuring the safety and reliability of electric vehicles. In this paper, a data-driven method is proposed for realizing accurate and robust SOH estimation with uncertainty measures. First, a set of battery health features are proposed based on the original and differential capacity–voltage (Q–V) and temperature–voltage (T–V) curves for depicting the multi-timescale battery ageing behavior. Four metrics, including the mean, variance, Shannon entropy, and fuzzy entropy of the original and processed Q–V/T–V sequences, are considered. The proposed features demonstrate high correlations to battery SOH. Next, a deep learning-assisted Gaussian mixture density network is developed to fuse the proposed features and generate the conditional probability distribution of battery SOH. Finally, a comprehensive computational study is conducted based on three datasets, which contain batteries of different chemistries and operated under different conditions. Results verify that the proposed method can generate an accurate and robust SOH estimation as well as provide the probability measure. The proposed method also presents superior performance to a set of benchmarking methods.