TY - GEN
T1 - On-line estimation of lithium polymer batteries state-of-charge using particle filter based data fusion with multi-models approach
AU - Zhou, Daming
AU - Ravey, Alexandre
AU - Gao, Fei
AU - Miraoui, Abdellatif
AU - Zhang, Ke
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/14
Y1 - 2015/12/14
N2 - 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.
AB - 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.
KW - multi-models data fusion
KW - particle filter
KW - state of charge
KW - weighted average
UR - http://www.scopus.com/inward/record.url?scp=84957681827&partnerID=8YFLogxK
U2 - 10.1109/IAS.2015.7356839
DO - 10.1109/IAS.2015.7356839
M3 - 会议稿件
AN - SCOPUS:84957681827
T3 - IEEE Industry Application Society - 51st Annual Meeting, IAS 2015, Conference Record
BT - IEEE Industry Application Society - 51st Annual Meeting, IAS 2015, Conference Record
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 51st Annual Meeting on IEEE Industry Application Society, IAS 2015
Y2 - 11 October 2015 through 22 October 2015
ER -