Electric vehicle energy consumption modeling using real world driving data: system identification vs. machine learning

Abstract

Implementing energy-saving practices requires accurately modeling electric vehicle (EV) energy consumption. This research proposes a data-centric approach to predict the energy consumption of EVs using the nonlinear autoregressive with exogenous inputs (NARX) model structure with the motor torque and vehicle speed as inputs. The model was identified and validated with on-road driving data from four trips between Southampton and various U.K. cities, achieving at least 99.2% accuracy in total energy consumption predictions. The NARX model achieved good accuracy in both simulation and prediction configurations, while reducing the mean squared error (MSE) by 30% compared to the Long Short-Term Memory (LSTM) network, which is a machine learning model structure with feedback and feedforward components. Although a feedforward neural network (NN) performed comparable to the NARX model when using past inputs and outputs, the NARX model proved more computationally efficient due to its more straightforward structure with fewer parameters. These results also demonstrate potential benefits of integrating system identification techniques to enhance machine learning models' performance for dynamic systems.

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Identifiers

ORCID for Mohamad Bilal Baluch: orcid.org/0009-0002-9404-3193

ORCID for Suleiman Sharkh: orcid.org/0000-0001-7335-8503

ORCID for Anil Madhusudhanan: orcid.org/0000-0002-0965-3767

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