期刊
JOURNAL OF CLEANER PRODUCTION
卷 270, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2020.122508
关键词
State of charge estimation; Moving horizon estimation; Nonlinear optimization; Robust; Uncertainties
资金
- Beijing Institute of Technology Research Fund Program for Young Scholars [3030011181911]
- National Natural Science Foundation of China [51605020]
- National Key R&D Program of China [2017YFB0102600]
Accurate battery state information is essential for battery management system application and safety monitoring. However, it is a challenge task to obtain satisfied estimation results due to the uncertainties and inconsistencies of battery packs caused by aging. To solve this challenge, an optimization-based moving horizon estimation approach is presented in this paper for battery state and parameter online estimation. The dynamic battery parameters including open circuit voltage and internal resistance in equivalent circuit model are described by polynomial function of state of charge and input current for estimation algorithm design. And the intrinsic connection and difference between extended Kalman filter and moving horizon estimation algorithm are explicitly explained. Both of them are least square based estimation approach, and Kalman filter is a special form of moving horizon estimation, while moving horizon estimation relax Markov assumption compared with extended Kalman method. And then the optimization-based moving horizon estimation is designed for parameters and state of charge online assessment for battery dynamic system. To reduce computing time, the software framework CasADi is used for differential-algebraic calculation and nonlinear optimization. Three mismatch working conditions are studied for estimation performance validation, including mismatched initial guess values and battery dynamic characteristics difference caused by aging condition. The experimental results demonstrate that optimization-based moving horizon estimation performs better than Kalman filter-based approaches in terms of estimation precision, convergence time and robust. The proposed optimization-based moving horizon estimation is a promising approach for state of charge estimation in commercial battery management system applications. (C) 2020 Elsevier Ltd. All rights reserved.
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