Data-driven Energy Consumption Prediction of New Energy Buses
In view of the most of the existing energy consumption prediction of electric vehicles based on the laboratory conditions,the results are too ideal and the actual deviation is large or the accuracy is insufficient.According to the actual running data of Beijing No.51 bus,the influencing factors of energy consumption is analyzed,the time information through clock cycle coding is optimized,and a driving behavior evaluation system is established based on the entropy weight method for auxiliary analysis of driving behavior and operating conditions by using the boxplots to set thresholds to construct driving conditions.Finally,the LSTM energy consumption prediction model by introducing the attention mechanism is established for the four types of typical working condition segments after clustering,and it is compared and analyzed with the various prediction models such as traditional LSTM and LGBM,the validation results show that the performance of the LSTM prediction model by incorporating the attention mechanism is significantly higher than the other models.
国家科技期刊平台
NSTL
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