Fuzzy Logic Implementation for Accurate Electric Car Battery SoC Measurement

Muhammad Dzaky Ashidqi, Miftahul Anwar, Chico Hermanu B.A., Agus Ramelan, feri. adriyanto
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引用次数: 2

Abstract

Changes in temperature can affect the accuracy of the estimated SoC value based on voltage. In this study, fuzzy logic was implemented to correct the SoC estimation error caused by the influence of temperature. The system acquired data through sensors and then processed it using the Arduino microcontroller. Parameters in the form of voltage, temperature, and current were processed by Arduino with a fuzzy logic program which was uploaded into it and produced the output of the estimated SoC value. From the observations, it was found that the estimated SoC value from this method had better accuracy with a smaller error than the SoC estimation based on voltage alone. Using the RMSE method, the errors calculated in this method in the process of charging and discharging without running were 2.26 and 7.74, while the SoC estimation error based on voltage alone reached 4.88 and 12.8. In the discharging process with a running car, the SoC estimation results using fuzzy logic also showed accurate results. There was only 1% of SoC value increasing pattern during the discharging process, which the value trend should continue to decrease and should not be an increase. In addition, compared to the previous method applied to the same research object, namely the chemical equilibrium constant method, this method also showed more accurate results.
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电动汽车电池SoC精确测量的模糊逻辑实现
温度的变化会影响基于电压估计的SoC值的准确性。在本研究中,采用模糊逻辑对温度影响下的SoC估计误差进行修正。该系统通过传感器采集数据,然后利用Arduino单片机进行处理。电压、温度、电流等参数通过Arduino上传到模糊逻辑程序进行处理,输出估计的SoC值。通过观察发现,该方法估计的荷电状态值比单独基于电压估计的荷电状态值具有更好的精度和更小的误差。使用RMSE方法,该方法在不运行的充放电过程中计算出的误差分别为2.26和7.74,而单独基于电压的SoC估计误差分别为4.88和12.8。在有车行驶的放电过程中,采用模糊逻辑的荷电状态估计结果也较为准确。放电过程中SoC值仅呈1%的上升趋势,其值趋势应持续下降而不应上升。此外,与之前应用于同一研究对象的方法,即化学平衡常数法相比,该方法也显示出更准确的结果。
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