期刊
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
卷 65, 期 6, 页码 4512-4522出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2015.2443975
关键词
Battery management systems; battery modeling; cell parameter variation; model predictive iterative; power estimation
资金
- Automotive Partnership Canada, Natural Sciences and Engineering Research Council of Canada
- Canada Excellence Research Chairs Program
- Fiat Chrysler Automobiles (FCA)
- FCA USA LLC
- FCA Canada Inc.
In this paper, a higher fidelity battery equivalent circuit model incorporating asymmetric parameter values is presented for use with battery state estimation ( BSE) algorithm development; particular focus is given to state-of-power ( SOP) or peak power availability reporting. A practical optimization-based method is presented for model parameterization fitting. Two novel model-based SOP algorithms are proposed to improve voltage-limit-based power output accuracy in larger time intervals. The first approach considers first-order extrapolation of resistor values and open-circuit voltage ( OCV) based on the instantaneous equivalent circuit model parameters of the cell. The second proposed approach, which is referred to as multistep model predictive iterative ( MMPI) method, incorporates the cell model in a model predictive fashion. Finally, a SOP verification methodology is presented that incorporates drive cycle data to realistically excite the battery model. Simulation results compare the proposed SOP algorithms to conventional approaches, where it is shown that higher accuracy can be achieved for larger time horizons.
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