The invention discloses a short-term vehicle speed working condition real-time prediction method based on interaction between a preceding vehicle and a self vehicle, and the method comprises the following steps: S1, obtaining historical vehicle speed and vehicle distance information of the preceding vehicle and the self vehicle, and extracting effective data; S2, constructing a future vehicle speed prediction model based on an artificial neural network; S3, performing offline training on the constructed future vehicle speed prediction model; S4, predicting the future speed of the self vehicleon line; S5, realizing adaptive learning of the vehicle speed prediction neural network; S6, calculating a torque demand according to the predicted short-term vehicle speed; and S7, calculating optimal torque distribution according to the torque demand and a dynamic programming algorithm. According to the invention, the artificial neural network method is used to predict the short-term speed of the self vehicle, and the speed prediction accuracy is improved; and the predicted short-term vehicle speed of the vehicle is applied to an energy management control strategy, so that the fuel economy is improved.
本发明公开了一种基于前车与自车互动的短期车速工况实时预测方法,包括以下步骤:S1.获取自车和前车历史车速、车距信息,并提取有效数据;S2.构建基于人工神经网络的未来车速预测模型;S3.对构建的未来车速预测模型进行离线训练;S4.在线预测自车的未来车速;S5.实现车速预测神经网络的自适应学习;S6.根据预测的短期车速计算扭矩需求;S7.根据转矩需求和动态规划算法计算最优转矩分配。本发明运用人工神经网络方法对自车短期车速进行预测,提高车速预测的准确度;并将预测出的汽车短期车速运用到能量管理控制策略中,提高燃油经济性。
Short-term vehicle speed working condition real-time prediction method based on interaction between preceding vehicle and self vehicle
一种基于前车与自车互动的短期车速工况实时预测方法
14.04.2020
Patent
Elektronische Ressource
Chinesisch
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