Engineering Faculty, Campus San Juan del Río, Universidad Autónoma de Querétaro, Av. Río Moctezuma 249, San Juan del Río 76807, Querétaro, Mexico.
Sensors (Basel). 2023 Mar 8;23(6):2924. doi: 10.3390/s23062924.
Nowadays, the use of renewable, green/eco-friendly technologies is attracting the attention of researchers, with a view to overcoming recent challenges that must be faced to guarantee the availability of Electric Vehicles (EVs). Therefore, this work proposes a methodology based on Genetic Algorithms (GA) and multivariate regression for estimating and modeling the State of Charge (SOC) in Electric Vehicles. Indeed, the proposal considers the continuous monitoring of six load-related variables that have an influence on the SOC (State of Charge), specifically, the vehicle acceleration, vehicle speed, battery bank temperature, motor RPM, motor current, and motor temperature. Thus, these measurements are evaluated in a structure comprised of a Genetic Algorithm and a multivariate regression model in order to find those relevant signals that better model the State of Charge, as well as the Root Mean Square Error (RMSE). The proposed approach is validated under a real set of data acquired from a self-assembly Electric Vehicle, and the obtained results show a maximum accuracy of approximately 95.5%; thus, this proposed method can be applied as a reliable diagnostic tool in the automotive industry.
如今，可再生、绿色/环保技术的使用引起了研究人员的关注，以期克服为保证电动汽车 (EV) 的可用性而必须面对的最新挑战。因此，这项工作提出了一种基于遗传算法 (GA) 和多元回归的方法，用于估计和建模电动汽车的充电状态 (SOC)。事实上，该提案考虑了对六个与负载相关的变量的连续监测，这些变量对 SOC（充电状态）有影响，具体来说，是车辆加速度、车辆速度、电池组温度、电机 RPM、电机电流和电机温度。因此，这些测量值在由遗传算法和多元回归模型组成的结构中进行评估，以找到那些更好地模拟充电状态以及均方根误差 (RMSE) 的相关信号。该方法在从自组装电动汽车采集的一组真实数据下进行了验证，得到的结果显示出了大约 95.5%的最高精度；因此，该方法可以作为汽车行业的可靠诊断工具应用。
