D Obuli Pranav, Babu Preethem S, V Indragandhi, B Ashok, S Vedhanayaki, C Kavitha
School of Electrical Engineering, VIT, Vellore, India.
School of Mechanical Engineering, VIT, Vellore, India.
Sci Rep. 2024 Jul 11;14(1):16036. doi: 10.1038/s41598-024-66997-9.
Accurately estimating Battery State of Charge (SOC) is essential for safe and optimal electric vehicle operation. This paper presents a comparative assessment of multiple machine learning regression algorithms including Support Vector Machine, Neural Network, Ensemble Method, and Gaussian Process Regression for modelling the complex relationship between real-time driving data and battery SOC. The models are trained and tested on extensive field data collected from diverse drivers across varying conditions. Statistical performance metrics evaluate the SOC prediction accuracy on the test set. Gaussian process regression demonstrates superior precision surpassing the other techniques with the lowest errors. Case studies analyse model competence in mimicking actual battery charge/discharge characteristics responding to changing drivers, temperatures, and drive cycles. The research provides a reliable data-driven framework leveraging advanced analytics for precise real-time SOC monitoring to enhance battery management.
准确估计电池荷电状态（SOC）对于电动汽车的安全和优化运行至关重要。本文对多种机器学习回归算法进行了比较评估，包括支持向量机、神经网络、集成方法和高斯过程回归，用于对实时驾驶数据与电池SOC之间的复杂关系进行建模。这些模型在从不同条件下的不同驾驶员收集的大量现场数据上进行训练和测试。统计性能指标评估测试集上的SOC预测准确性。高斯过程回归表现出卓越的精度，超过其他技术且误差最低。案例研究分析了模型在模拟实际电池充电/放电特性以响应不断变化的驾驶员、温度和驾驶循环方面的能力。该研究提供了一个可靠的数据驱动框架，利用先进分析技术进行精确的实时SOC监测，以加强电池管理。
