利用系统识别和深度学习预测电池电动汽车的辅助动力和能源需求

IF 2.3 4区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Lukas Schäfers, Kai Franke, Rene Savelsberg, Stefan Pischinger
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引用次数: 0

摘要

电池电动汽车辅助设备的能源需求在整个行程的能源需求中占很大比例,因此在预测车辆剩余行驶里程或执行预测驾驶功能时必须将其考虑在内。本文研究了一种方法,利用系统识别和带有双向长短期记忆层的神经网络,根据出行前已知的信息预测辅助设备的功率需求。通过使用自学、数据驱动的方法以及无需额外仪器即可测量的数据,无需事先设计详细的物理模型即可进行预测。此外,还可根据环境条件对训练数据进行规则分配,使各个模型适应热力系统的不同气候模式。该方法的潜力已在三个不同的系统中得到证实,在能源方面的预测准确率平均为 3% 至 8%,而预测功耗的偏差平均约为 500 瓦。由于整个过程完全自动化,预计预测精度还会进一步提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Auxiliaries’ power and energy demand prediction of battery electric vehicles using system identification and deep learning

Auxiliaries’ power and energy demand prediction of battery electric vehicles using system identification and deep learning

Auxiliaries’ power and energy demand prediction of battery electric vehicles using system identification and deep learning

The energy demand of the auxiliaries of battery electric vehicles can account for a significant share of the total energy demand of a trip and must be taken into account for the prediction of the vehicle's remaining driving range or the implementation of predictive driving functions. This paper investigates a method that uses system identification and neural networks with bidirectional long short-term memory layers to predict the power requirements of the auxiliaries depending on information that is known prior to the trip. By using a self-learning, data-driven approach as well as data that can be measured without additional instrumentation, a prediction is made possible without the need to design detailed physical models in advance. Additionally, a rule-based allocation of the training data based on environmental conditions is implemented, which serves to adapt individual models to different climatic modes of the thermal system. The potential of the method is demonstrated for three different systems showing a prediction accuracy of on average 3% to 8% in terms of energy, while the deviation of the predicted power consumption is on average about 500 watts. Due to the complete automation of the process, a further increase in prediction accuracy can be expected.

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来源期刊
IET Intelligent Transport Systems
IET Intelligent Transport Systems 工程技术-运输科技
CiteScore
6.50
自引率
7.40%
发文量
159
审稿时长
3 months
期刊介绍: IET Intelligent Transport Systems is an interdisciplinary journal devoted to research into the practical applications of ITS and infrastructures. The scope of the journal includes the following: Sustainable traffic solutions Deployments with enabling technologies Pervasive monitoring Applications; demonstrations and evaluation Economic and behavioural analyses of ITS services and scenario Data Integration and analytics Information collection and processing; image processing applications in ITS ITS aspects of electric vehicles Autonomous vehicles; connected vehicle systems; In-vehicle ITS, safety and vulnerable road user aspects Mobility as a service systems Traffic management and control Public transport systems technologies Fleet and public transport logistics Emergency and incident management Demand management and electronic payment systems Traffic related air pollution management Policy and institutional issues Interoperability, standards and architectures Funding scenarios Enforcement Human machine interaction Education, training and outreach Current Special Issue Call for papers: Intelligent Transportation Systems in Smart Cities for Sustainable Environment - https://digital-library.theiet.org/files/IET_ITS_CFP_ITSSCSE.pdf Sustainably Intelligent Mobility (SIM) - https://digital-library.theiet.org/files/IET_ITS_CFP_SIM.pdf Traffic Theory and Modelling in the Era of Artificial Intelligence and Big Data (in collaboration with World Congress for Transport Research, WCTR 2019) - https://digital-library.theiet.org/files/IET_ITS_CFP_WCTR.pdf
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