The invention provides an unmanned vehicle power battery power control method and system based on deep learning, and relates to the field of power battery control, and the method comprises the steps: setting an unmanned vehicle at the ith moment of a control cycle according to the battery power determined by a battery power determination model at the (i-1) th moment; obtaining test operation data; determining a brake distance relation function between the test vehicle speed, the test battery power, the test ground humidity and the test ground temperature and the test brake distance; acquiring battery information; acquiring environment information, other vehicle information and vehicle running speed; judging whether the battery power determination model at the (i-1) th moment needs to be trained or not; under the condition that the battery power determination model at the (i-1) th moment needs to be trained, determining a training loss function; obtaining a battery power determination model at the ith moment; and determining the battery power at the (i + 1) th moment. According to the invention, the power control accuracy of the power battery can be improved.
本发明提供一种基于深度学习的无人车辆动力电池功率控制方法和系统,涉及动力电池控制领域,所述方法包括:在控制周期的第i个时刻,按照第i‑1个时刻的电池功率确定模型确定的电池功率对无人车辆进行设置;获取测试运行数据;确定测试车速、测试电池功率、测试地面湿度和测试地面温度与测试制动距离之间的制动距离关系函数;获取电池信息;获取环境信息、其他车辆信息和车辆行驶速度;判断第i‑1个时刻的电池功率确定模型是否需要训练;在第i‑1个时刻的电池功率确定模型需要训练的情况下,确定训练损失函数;获得第i个时刻的电池功率确定模型;确定第i+1个时刻的电池功率。根据本发明,可提高动力电池功率控制的准确性。
Unmanned vehicle power battery power control method and system based on deep learning
基于深度学习的无人车辆动力电池功率控制方法和系统
15.04.2025
Patent
Elektronische Ressource
Chinesisch
IPC: | B60L PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES , Antrieb von elektrisch angetriebenen Fahrzeugen |
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