State of charge

About: State of charge is a research topic. Over the lifetime, 12013 publications have been published within this topic receiving 201419 citations. The topic is also known as: SoC & SOC.

An improved adaptive unscented kalman filtering for state of charge online estimation of lithium-ion battery

Abstract: Precise state of charge (SOC) estimation is crucial to assure safe and reliable operation of lithium-ion battery in electric vehicles. Adaptive unscented Kalman filter (AUKF) has been intensively applied to estimate SOC due to its features of self-correction and high accuracy. Nevertheless, the estimation by traditional AUKF cannot proceed when error covariance matrix is non-positive definite, greatly influencing the stability of SOC estimation. To address this issue, an improved AUKF is proposed in this paper. Firstly, the forgetting factor recursive least square is employed to online identify parameters of electrical equivalent circuit model. With these identified parameters, an improved AUKF, whose Cholesky decomposition for error covariance matrix of tradition AUKF is replaced by singular value decomposition, is applied here to provide online accurate SOC estimation. The feasibility of the proposed method is verified by experimental data under Federal Urban Driving Schedule test. The validation results of robustness present that the algorithm has satisfactory robustness against inaccurate initial SOC. Moreover, through the comparison with traditional AUKF, it can be easily concluded that the proposed method can achieve precise and stable SOC estimation even though error covariance matrix is non-positive definite.

A Combined State of Charge Estimation Method for Lithium-Ion Batteries Used in a Wide Ambient Temperature Range

Abstract: Ambient temperature is a significant factor that influences the characteristics of lithium-ion batteries, which can produce adverse effects on state of charge (SOC) estimation. In this paper, an integrated SOC algorithm that combines an advanced ampere-hour counting (Adv Ah) method and multistate open-circuit voltage (multi OCV) method, denoted as “Adv Ah + multi OCV”, is proposed. Ah counting is a simple and general method for estimating SOC. However, the available capacity and coulombic efficiency in this method are influenced by the operating states of batteries, such as temperature and current, thereby causing SOC estimation errors. To address this problem, an enhanced Ah counting method that can alter the available capacity and coulombic efficiency according to temperature is proposed during the SOC calculation. Moreover, the battery SOCs between different temperatures can be mutually converted in accordance with the capacity loss. To compensate for the accumulating errors in Ah counting caused by the low precision of current sensors and lack of accurate initial SOC, the OCV method is used for calibration and as a complement. Given the variation of available capacities at different temperatures, rated/non-rated OCV–SOCs are established to estimate the initial SOCs in accordance with the Ah counting SOCs. Two dynamic tests, namely, constant- and alternated-temperature tests, are employed to verify the combined method at different temperatures. The results indicate that our method can provide effective and accurate SOC estimation at different ambient temperatures.