A partial charging curve-based data-fusion-model method for capacity estimation of Li-Ion battery

摘要

Accurate capacity estimation is crucial and challenging for guaranteeing safety and durability of Li-ion batteries. This work proposes a data-fusion-model method to estimate battery capacity using the partial charging curve. First, during the charging process, two representative battery ageing features are extracted from the partial incremental capacity curve smoothed by Locally Weighted Scatterplot Smoothing (LOWESS). Second, the dual Gaussian process regressions (GPRs) are employed to establish a data-driven battery ageing state-space representation, which takes battery capacity as the state variable and takes two ageing features as the input variables. Third, combining with the GPR-based battery state-space representation, Particle Filter (PF) is introduced to suppress the measurement noises and a Gaussian Process Particle Filter (GPPF) is built to estimate battery capacity. Meanwhile, the output capacity is fed back to the GPPF to update the battery ageing state-space representation. Finally, ageing experiments are conducted to validate the effectiveness of the proposed method. Meanwhile, GPR is implemented with the cycle and two ageing features for capacity estimation as a contrast. The results show that the proposed GPPF method can provide more accurate and robustness battery estimation results than GPR.