A data-fusion framework for lithium battery health condition Estimation Based on differential thermal voltammetry

Battery state foretasting and health management are significant tasks for ensuring safety and stability of battery systems. Accurate state estimation can not only provide valuable parameters for energy management but also may prolong battery usage lifespan. Comprehensive theoretical analysis and practical application, differential thermal voltammetry analysis method has great potentials in actual operation. This paper proposes a closed-loop battery capacity estimation framework, Gaussian process regression and multi-output Gaussian process regression for constructing battery dynamic state-space function, to improve the accuracy and robustness of battery SOH estimation. Firstly, a time-series model of battery capacity degradation is established as the state equation using Gaussian process regression. Secondly, two strong correlation indicators are treated as observed parameters to construct an observation equation through multi-output Gaussian process regression, where the health indicators are extracted from the partial smoothed curves by two filter methods. Thirdly, particle filter algorithm is employed to correct the prior estimated capacity and suppress noise perturbations for achieving closed-loop control. Additionally, the performances of particle filter algorithm with different particle sizes are discussed and analyzed from accuracy and computational time aspects. Verification of three types of batteries indicates that the proposed method has an excellent capability for battery capacity estimation.