基于LSTM-DDPG的車速預(yù)測對增程式汽車能量管理研究

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中圖分類號：U461 收稿日期：2025-02-12 DOI： 10.19999/j.cnki.1004-0226.2025.05.001

Study on Speed Prediction for Energy Management of Add-on Vehicles Based on LSTM-DDPG

Tang Jianxing Yang Chao Yue Zhigang Luo Jiaxin Yu Hengbin Sun Guoyang Automotive Technology Co.，Ltd.，Beijing 10260o，China

Abstract：Inordertoenhancetheenergy management eficiencyofanad-onelectricvehicle（EREV），thepaperfirstlyuses a longshort-term memory（LSTM）neuralnetwork topredictthevehiclesped.Basedonthispredictionresult，thepowerdemandrequiredatfuture momentsisfurthercalculatedandcombinedwiththepowerdemandatthecurent moment，andthesedataare fedto getherintothedeepdeterministicplicygadient （DDPG）intellgence.Tisintellgentbodyisesponsibleforeneratingctrolcommands，andsubsequentlysimulationexperimentsareconductedtoverifytheeal-tieresponsivenessoftheproposedcontrolstrategy. TheexperimentalresultsshowthattheLSTM-DDPGenergymanagement strategyproposed inthisstudyreduces theequivalent fuel consumption by 0.613kg ， 0.350kg ，and 0.607kg ，respectively，compared with the DDPG strategy only，the deep Q-network（DQN） strategy，andtheconventionalpower-folowingcontrolstrategyundertheworldheavycommercialvehicletransientcycling（WTVC） operating conditions. In addition， the difference in equivalent fuel consumption is only 0.128kg when compared to the dynamic programming （DP）control strategy，which showsthe advantagesand high eficiencyof this strategy interms ofenergy saving.

Key words： Incremental electric vehicles;Long andshor-term memory neural networks;Deepreinforcement learing;Energy management

1前言

增程式電動汽車（EREV）作為一種新能源汽車技術(shù)，正受到越來越多的關(guān)注。（剩余8151字）