期刊
IEEE ACCESS
卷 6, 期 -, 页码 20868-20880出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2018.2824559
关键词
Electric vehicles; battery management system; state estimation; genetic algorithm
资金
- Foundation of State Key Laboratory of Automotive Simulation and Control [20161106]
- National Natural Science Foundation of China [51607030]
- Fundamental Research Funds for the Central Universities [N160304001]
Online estimation of the state of power (SoP) of lithium-ion batteries is crucial for both battery management system and energy management system in electric vehicles. In this paper, the approach of online estimating the SoP is investigated with a concern of the impact of the imprecise state of charge (SoC). First, the characteristics of lithium batteries under different state of health (SoH) conditions are experimented based on a typical vehicle driving cycle; then the SOP estimation algorithm using genetic algorithm (GA) is proposed to deal with the long time-scale estimation for power management application, on top of that, the sensitivity coefficient (delta) of the SoP estimation to the SoC precision is analyzed and the correlations of delta with the varying SoH, estimation time-scale are established. Finally, the presented algorithm is evaluated by a simulation study. The proposed GA-based estimation method can improve the SoP estimation accuracy by up to 7.2% in certain cases compared with the traditional Taylor method.
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