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This paper presents a Li-ion cell model parameterization technique for hybrid electric and electric vehicle control applications. The proposed method is based on an equivalent electrical circuit (EEC) model of the Li-ion cell and combines the advantages of the two main strategies employed for cell model parameterization, namely, the offline and online procedures. Offline methods are based on the identification of relevant EEC parameter values using a limited set of test data specific to the target cell chemistry. Conversely, online techniques employ adaptive algorithms that update the cell model as it is being used. The novel method presented in this paper employs recurrent offline updates of the EEC parameterization set, and thus, it integrates the advantages of the offline approach, such as flexibility, reduced complexity, and improved run-time performance, with the main benefit of the online counterpart, which is the capacity to adapt the model parameterization to uncharacterized operating conditions. Based on an extensive set of experimental and simulation results obtained from tests specified in the IEC 62660-1 standard, it is shown that the proposed approach offers a significant accuracy improvement over simple offline methods, as well as enhanced runtime speed in comparison with commonly employed online strategies.
Date of Publication: Nov. 2012