In this paper, a model of vehicle torque demand prediction based on Long Short Term Memory networks (LSTM) is presented. The correlation analysis of the data used to train the network is carried out, and appropriate characteristic parameters including acceleration pedal, brake pedal, speed and torque are selected as the input of the network. The characteristic parameters of the past 2 steps are used to predict the torque demand of the future. The data collected from vehicle controller is divided into two sections which are training set to train the LSTM and testing set to verify the performance. The results compared with that of artificial neural network show that prediction model can effectively improve the prediction accuracy.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Prediction Algorithm for Vehicle Torque Demand based on LSTM


    Contributors:
    Li, Tao (author) / Cui, Naxin (author) / Du, Yi (author) / Shi, Yuemei (author) / Nie, Hao (author) / Wang, Ming (author)


    Publication date :

    2020-12-18


    Size :

    1958676 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Electric Vehicle Battery Temperature Prediction Using LSTM Algorithm

    Sridhar, B / Allirani, S / Reshmin Shafi, R S et al. | IEEE | 2024


    Air quality prediction based on LSTM algorithm

    Ren, Qiankun | British Library Conference Proceedings | 2022


    Vehicle trajectory prediction based on LSTM network

    Yang, Zhifang / Liu, Dun / Ma, Li | IEEE | 2022


    TORQUE BASED VEHICLE PATH PREDICTION

    VARUNJIKAR TEJAS M / SHENG JIAN | European Patent Office | 2020

    Free access

    Transform and LSTM-based vehicle trajectory prediction method

    CHENG DENGYANG / GU XIANG / QIAN CONG et al. | European Patent Office | 2023

    Free access