On-line estimation of lithium polymer batteries state-of-charge using particle filter based data fusion with multi-models approach

In this paper, a robust model-based battery state of charge (SOC) estimating algorithm is proposed with a novel approach based on multi-models data fusion technique and particle filter (PF). The proposed method is particularly adapted for SOC estimation under conditions of sharp current variations and presence of measurement noise. In this innovative approach, multiple battery models have been used in order to accurately estimate a battery SOC. The measured battery terminal voltage is compared with the multiple battery models output to generate a residual, which is then used to calculate the weight of estimated value from each battery model. This weight, which represents the accuracy of observation equation of each battery model, is inversely proportional to the residual. The estimated SOC values from different models are then fused and the weights of estimated values from each battery model are adjusted dynamically using particle filter and weighted average methodology, in order to calculate the final SOC estimation of the battery. In addition to the simulation, the proposed method has been validated by experimental results. The results demonstrate that the proposed multi-models based algorithm can achieve better accuracy than single model-based methods.