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State of charge estimation of ultracapacitor based on forgetting factor recursive least square and extended Kalman filter algorithm at full temperature range

Jing Ren, Yonghong Xu, Hongguang Zhang, Fubin Yang, Yifang Yang, Xu Wang, Peng Jin, Denggao Huang

2022Heliyon23 citationsDOIOpen Access PDF

Abstract

values of ultracapacitor at full temperature range. Under the hybrid pulse power characterization working condition, the average mean absolute error between the estimated voltage and the actual voltage is about 0.0132 V. (2) EKF algorithm has a good adaptability to estimate SOC of ultracapacitor under different temperatures and working conditions. The SOC estimation error under different working conditions is low. From the perspective of mean square error, the estimation error at -20 °C is the lowest. (3) FFRLS and EKF joint estimation algorithm with good robustness and reliability can be used to estimate the SOC of ultracapacitor under different temperatures and working conditions. This study can provide a useful guidance for the parameter identification and SOC estimation of ultracapacitor for electric vehicle at different temperatures.

Topics & Concepts

Extended Kalman filterControl theory (sociology)Kalman filterState of chargeAlgorithmMean squared errorRecursive least squares filterRobustness (evolution)VoltageComputer scienceEngineeringMathematicsPower (physics)Battery (electricity)Adaptive filterElectrical engineeringStatisticsChemistryQuantum mechanicsPhysicsArtificial intelligenceGeneControl (management)BiochemistryAdvanced Battery Technologies ResearchElectric and Hybrid Vehicle TechnologiesElectric Vehicles and Infrastructure