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Development of an Energy Prediction Model Based on Driving Data for Predicting the Driving Distance of an Electric Vehicle

机译：基于驱动数据的预测模型的开发用于预测电动车辆的驱动距离

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In 2016, the 4th industrial revolution, represented by hyper-connected and hyper-intelligent technologies, began, and the global automobile industry is now focusing on the development of smart cars, such as driverless vehicles, connected vehicles, and electric vehicles. In particular, as electric vehicles with autonomous driving features are to be manufactured and sold, the precise prediction of driving distances has become more important. If the actual driving distance is shorter than the prediction of the available driving distance, the autonomous vehicle will stop on the way to its destination. Moreover, the route for electric vehicles should be determined by considering the location of charging stations and available driving distance. Existing studies about the expected driving distance do not appropriately reflect all of the various circumstances, components, and variables, thus restricting their predictive performance. In particular, current methods do not precisely predict the running resistance of electric vehicles, or changes in driving distance caused by the use of electrical functions in such vehicles. Thus, a vehicle energy model for predicting the exact driving distance of an electric vehicle is described in this paper, considering the driving speed, road status, tire pressure, temperature, driving altitude, regenerative braking, and other factors. Finally, the proposed model for predicting the driving distance of electric vehicles was confirmed to be feasible by an in-vehicle test on real roads.
机译：2016年，由超互联和超智能技术，开始和全球汽车工业代表的第四届工业革命现在关注智能汽车的开发，如无人驾驶车辆，连通车辆和电动车辆。特别是，随着要制造和销售的具有自主驱动特征的电动车辆，驾驶距离的精确预测变得更加重要。如果实际驱动距离短于可用驾驶距离的预测，则自主车辆将停止到其目的地。此外，通过考虑充电站的位置和可用的驱动距离来确定电动车辆的路线。关于预期驾驶距离的现有研究不会适当地反映所有各种情况，组件和变量，从而限制其预测性能。特别地，目前的方法不准确地预测电动车辆的运行阻力，或者在这种车辆中使用电气功能引起的驱动距离的变化。因此，本文描述了用于预测电动车辆的精确驱动距离的车辆能量模型，考虑到驱动速度，道路状态，轮胎压力，温度，驾驶高度，再生制动和其他因素。最后，确认通过在真正的道路上的车载测试是可行的，所提出的用于预测电动车辆的驾驶距离。

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