Multi-model probabilities based state fusion estimation method of lithium-ion battery for electric vehicles: State-of-energy

State-of-energy (SoE) is an important index for batteries in electric vehicles and it provides the essential basis of energy application, load equilibrium and security of electricity. To improve the estimation accuracy and reliability of SoE, a novel multi-model fusion estimation approach is proposed against uncertain dynamic load and different temperatures. The main contributions of this work can be summarized as follows: (1) Through analyzing the impact on the estimation accuracy of SoE due to the complexity of models, the necessity of redundant modeling is elaborated. (2) Three equivalent circuit models are selected and their parameters are identified by genetic algorithm offline. Linear matrix inequality (LMI) based H-infinity state observer technique is applied to estimate SoEs on aforementioned models. (3) The concept of fusion estimation is introduced. The estimation results derived by different models are merged under certain weights which are determined by Bayes theorem. (4) Batteries are tested with dynamic load cycles under different temperatures to validate the effectiveness of this method. The results indicate the estimation accuracy and reliability on SoE are elevated after fusion.