期刊
APPLIED ENERGY
卷 149, 期 -, 页码 297-314出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2015.03.110
关键词
Lithium-ion battery; Remaining discharge energy; Model parameter prediction; Predictive-adaptive energy prediction; Electric vehicle
资金
- MOST (Ministry of Science and Technology) of China [2014DFG71590, 2011AA11A269]
- MOE (Ministry of Education) of China [2012DFA81190]
In order to estimate the remaining driving range (RDR) in electric vehicles, the remaining discharge energy (E-RDE) of the applied battery system needs to be precisely predicted. Strongly affected by the load profiles, the available E-RDE varies largely in real-world applications and requires specific determination. However, the commonly-used direct calculation (DC) method might result in certain energy prediction errors by relating the E-RDE directly to the current state of charge (SOC). To enhance the E-RDE accuracy, this paper presents a battery energy prediction (EP) method based on the predictive control theory, in which a coupled prediction of future battery state variation, battery model parameter change, and voltage response, is implemented on the E-RDE prediction horizon, and the E-RDE is subsequently accumulated and real-timely optimized. Three EP approaches with different model parameter updating routes are introduced, and the predictive-adaptive energy prediction (PAEP) method combining the real-time parameter identification and the future parameter prediction offers the best potential. Based on a large-format lithium-ion battery, the performance of different E-RDE calculation methods is compared under various dynamic profiles. Results imply that the EP methods provide much better accuracy than the traditional DC method, and the PAEP could reduce the E-RDE error by more than 90% and guarantee the relative energy prediction error under 2%, proving as a proper choice in online E-RDE prediction. The correlation of SOC estimation and E-RDE calculation is then discussed to illustrate the importance of an accurate E-RDE method in real-world applications. (C) 2015 Elsevier Ltd. All rights reserved.
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