Operation Energy Consumption Estimation Method of Electric Bus Based on CNN Time Series Prediction

Abstract

In order to further improve the accuracy of electric bus energy consumption estimation and reduce the complexity of using data, the paper proposes a new method for estimating electric bus energy consumption based on a deep learning approach with a data-driven model. The method can estimate the single-trip energy consumption of an electric bus by employing CNN (convolutional neural network) to time series prediction, which takes into account easily accessible trip data of electric buses, including initial SOC (state of charge), average speed, average temperature. First, we need to convert the raw data into a trip dataset by preprocessing the collected real-world trip data of an electric bus. Then, the single trip of the bus from the original station to the terminal station is considered the basic unit for energy consumption estimation, and the trip data are processed in a quasi-time series. Following that, the trip data were modified so that the subsequent convolutional operations more closely matched the interactions between adjacent trips, and a time series prediction method based on CNN was used instead of the regression analysis methods used in traditional data-driven models. Finally, single-trip operation energy consumption estimation of electric buses is achieved with time series prediction based on CNN, and this method is compared and analysed with the LSTM (long-short term memory) time series prediction method and multivariate nonlinear regression prediction methods in traditional data-driven models. The results show that the energy consumption estimation model for electric buses developed in this paper has a higher prediction accuracy, which can improve by 3.68 percent over the traditional multivariate nonlinear regression prediction method and by 1.32 percent over the LSTM time series prediction method.

Empowering Sustainability: A Consumer-Centric Analysis Based on Advanced Electricity Consumption Predictions

“…The literature on electricity consumption predictions offers several merits and demerits that shape the understanding and application of forecasting models in the energy sector. The literature showcases a wide array of methodologies, such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) Networks, Support Vector Machines (SVM), and Artificial Neural Networks (ANNs) [35][36][37]. This diversity allows for exploring various approaches to electricity consumption prediction, catering to different data characteristics and prediction requirements.…”

“…This diversity allows for exploring various approaches to electricity consumption prediction, catering to different data characteristics and prediction requirements. Studies have demonstrated that advanced prediction models, such as those incorporating deep learning techniques like LSTM with attention mechanisms, can significantly enhance prediction accuracy [36,38]. These models leverage complex patterns in electricity consumption data, leading to more precise forecasts.…”

A Trip-Based Data-Driven Model for Predicting Battery Energy Consumption of Electric City Buses

“…This analysis aims to guide the selection of battery capacity and design of charging infrastructure. A recurrent neural network (NN) with long short-term memory (LSTM) and a convolutional NN (CNN) was considered in [23], where the energy consumption and input parameters were formulated as time series.…”

Driving and Energy Profiles of Urban Bus Routes Predicted for Operation with Battery Electric Buses

“…The energy consumption of an electric bus can be estimated by using the deep learning approach. Single trip energy consumption is established using a convolutional neural network and trip data that include state of charge, speed, and temperature [31]. The energy consumption of electric buses is related to driving behaviors and environmental properties.…”

“…The proposed method for the estimation of energy consumption differs significantly from the well-known methods such as kinematic methods (e.g., [4]) and methods using machine learning (e.g., [31]). The novelty of the proposed method lies in the assumption that only GPS data are used to determine the driving and energy properties of the bus route.…”