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State of Charge Estimation in Electric Vehicles Using Improved Strong Tracking Kalman Filter Algorithm

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Abstract

Electric vehicles Battery management system observes the state of charge of Lithium Ion battery by controlling the parameters such as voltage, current and temperature prevents battery. The purpose of battery management system is used to estimate state of charge for vehicle navigation. This battery management system is useful from over charge and over discharge factors and it leads to cell balancing. An accurate measurement of state of charge estimation in battery management system is important in automotive batteries provides safety. The Kalman filter is proposed to realize the iterative mathematical calculations for estimating state of charge. Sigma point is sampled in the unscented transform using Kalman filter. The imaginary number is generated using this algorithm produces estimation failure. This paper presents a new algorithm, which is an updation of the Unscented Kalman Filter called as an improved unscented Kalman filter proposed which combines the decomposition for calculations and provides accurate estimation of state of charge. Adaptive noise covariance matching method is implemented. Finally, MATLAB simulations are performed to validate the effectiveness of this algorithm.

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The authors declare that data supporting the findings of this study are available within the article.

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  1. Department of Electronics and Communication Engineering, Thiagarajar College of Engineering, Madurai, 625015, India

    G. Ananthi

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  1. G. Ananthi
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Ananthi, G. State of Charge Estimation in Electric Vehicles Using Improved Strong Tracking Kalman Filter Algorithm. Wireless Pers Commun 128, 147–160 (2023). https://doi.org/10.1007/s11277-022-09946-x

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