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Abstract:The battery internal temperature estimation is important for the thermal safety in applications, because the internal temperature is hard to measure directly. In this work, an online internal temperature estimation method based on a simplified thermal model using a Kalman filter is proposed. As an improvement, the influences of entropy change and overpotential on heat generation are analyzed quantitatively. The model parameters are identified through a current pulse test. The charge/discharge experiments under different current rates are carried out on the same battery to verify the estimation results. The internal and surface temperatures are measured with thermocouples for result validation and model construction. The accuracy of the estimated result is validated with a maximum estimation error of around 1 K.

The open-circuit voltage (OCV) of batteries is a crucial characteristic parameter that reflects many aspects of a battery's performance, such as capacity, state-of-charge (SOC) and state-of-health. OCV is most widely used to determine the SOC when the battery works in a charge-depleting state. However, the application of the OCV to SOC estimation can be difficult because of the need for a long rest time for full relaxation. In this study, based on the analysis on the curve shape of battery voltage relaxation, a new adaptive model for simulating the voltage relaxation process is developed to predict the final static OCV in a few minutes instead of via the traditional long-term rest method. Avoiding this disadvantage, the SOC can be deduced from the predicted OCV via the corresponding relationship obtained in a short amount of time. A working condition experiment is performed to validate the new methods and the results are very accurate.

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.

Abstract:This study describes an online estimation of the model parameters and state of charge (SOC) of lithium iron phosphate batteries in electric vehicles. A widely used SOC estimator is based on the dynamic battery model with predeterminate parameters. However, model parameter variances that follow with their varied operation temperatures can result in errors in estimating battery SOC. To address this problem, a battery online parameter estimator is presented based on an equivalent circuit model using an adaptive joint extended Kalman filter algorithm. Simulations based on actual data are established to verify accuracy and stability in the regression of model parameters. Experiments are also performed to prove that the proposed estimator exhibits good reliability and adaptability under different loading profiles with various temperatures. In addition, open-circuit voltage (OCV) is used to estimate SOC in the proposed algorithm. However, the OCV based on the proposed online identification includes a part of concentration polarization and hysteresis, which is defined as parametric identification-based OCV (OCVPI). Considering the temperature factor, a novel OCV-SOC relationship map is established by using OCVPI under various temperatures. Finally, a validating experiment is conducted based on the consecutive loading profiles. Results indicate that our method is effective and adaptable when a battery operates at different ambient temperatures.

This paper presents a novel grouping method for lithium iron phosphate batteries. In this method, a simplified electrochemical impedance spectroscopy (EIS) model is utilized to describe the battery characteristics. Dynamic stress test (DST) and fractional joint Kalman filter (FJKF) are used to extract battery model parameters. In order to realize equal-number grouping of batteries, a new modified K-means clustering algorithm is proposed. Two rules are designed to equalize the numbers of elements in each group and exchange samples among groups. In this paper, the principles of battery model selection, physical meaning and identification method of model parameters, data preprocessing and equal-number clustering method for battery grouping are comprehensively described. Additionally, experiments for battery grouping and method validation are designed. This method is meaningful to application involving the grouping of fresh batteries for electric vehicles (EVs) and screening of aged batteries for recycling.

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