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

Abstract

The paper presents a novel approach for predicting battery energy consumption in electric city buses (e-buses) by means of a trip-based data-driven regression model. The model was parameterized based on the data collected by running a physical experimentally validated e-bus simulation model, and it consists of powertrain and heating, ventilation, and air conditioning (HVAC) system submodels. The main advantage of the proposed approach is its reliance on readily available trip-related data, such as travel distance, mean velocity, average passenger count, mean and standard deviation of road slope, and mean ambient temperature and solar irradiance, as opposed to the physical model, which requires high-sampling-rate driving cycle data. Additionally, the data-driven model is executed significantly faster than the physical model, thus making it suitable for large-scale city bus electrification planning or online energy consumption prediction applications. The data-driven model development began with applying feature selection techniques to identify the most relevant set of model inputs. Machine learning methods were then employed to achieve a model that effectively balances accuracy, simplicity, and interpretability. The validation results of the final eight-input quadratic-form e-bus model demonstrated its high precision and generalization, which was reflected in the R2 value of 0.981 when tested on unseen data. Owing to the trip-based, mean-value formulation, the model executed six orders of magnitude faster than the physical model.

Energy Assessment of Alternative City Bus Lines: A Case Study in Gijón, Spain

“…The increasing availability of real data and the development of IoT and Big Data enable open access to this information [6][7][8]. Most driving cycles used in these studies are either standardized, like the SORT cycles [9]; synthetic, where a standard cycle is adapted to specific bus line parameters [10]; semi-synthetic, where real trip recordings are modified for considering changing driving conditions [11]; or real cycles [12]. Each approach has its advantages and disadvantages, and the choice depends on factors such as the scale of the intended analysis, and the availability of detailed data, as well as the goals of the research.…”

“…In particular, the use of real data has been identified as the most suitable for a city bus analysis [13]. However, none of these studies has addressed the comparison of equivalent bus routes and are limited to single bus lines in cities like Jerusalem, Bogotá, and Liaocheng [10][11][12].…”

Prediction Method of PHEV Driving Energy Consumption Based on the Optimized CNN BiLSTM Attention Network

“…In addition to utilizing actual collected data, data generated by simulation models can also be employed for energy consumption modeling [17]. The advantage of employing physical models to obtain data lies in the capacity to arbitrarily set the values of input features in the simulation, allowing for the facile acquisition of large amounts of data.…”