The invention discloses a new energy automobile electric energy management method and system based on machine learning, and relates to the technical field of new energy automobile electric energy management, and the method comprises the steps: collecting multi-modal data through various sensors and external data interfaces; processing the multi-modal data by removing abnormal values, filling missing values and standardizing; performing feature extraction on the processed multi-modal data by using a multi-modal convolutional neural network, and fusing the multi-modal data into a comprehensive feature vector; inputting the comprehensive feature vector into a reinforcement learning model, and generating an electric energy management strategy through interactive learning with the environment; and inputting the electric energy management strategy into the online learning framework of the self-adaptive learning rate, and optimizing the electric energy management strategy in real time. According to the method, the comprehensive feature vector is input into the reinforcement learning model, and the electric energy management strategy is generated through interactive learning with the environment, so that the efficiency and performance of electric energy management of the new energy automobile are remarkably improved.
本发明公开了一种基于机器学习的新能源汽车电能管理方法及系统,涉及新能源汽车电能管理技术领域,包括,通过各种传感器和外部数据接口收集多模态数据;通过剔除异常值、填补缺失值和标准化,对多模态数据进行处理;使用多模态卷积神经网络对处理后的多模态数据进行特征提取,并融合成综合特征向量;将综合特征向量输入强化学习模型,并通过与环境交互学习生成电能管理策略;将电能管理策略输入到自适应学习率的在线学习框架中,实时优化电能管理策略。本发明通过将综合特征向量输入强化学习模型,并通过与环境交互学习生成电能管理策略,显著提升了新能源汽车电能管理的效率和性能。
New energy automobile electric energy management method and system based on machine learning
一种基于机器学习的新能源汽车电能管理方法及系统
2025-02-18
Patent
Electronic Resource
Chinese
IPC: | G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / B60L PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES , Antrieb von elektrisch angetriebenen Fahrzeugen / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen |
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