DeepFEC: Energy Consumption Prediction under Real-World Driving Conditions for Smart Cities

Elmi, Sayda; Tan, Kian-Lee

doi:10.1145/3442381.3449983

Cited by 11 publications

(7 citation statements)

References 25 publications

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“…Previous studies on vehicle energy consumption estimation can be mainly categorized into numerical approaches [1,6,8,20] and datadriven approaches [4,7,10,14,17,18,22,31]. Based on the vehicle dynamics equation as the underlying physical model, Cauwer et al [6] proposed using multiple linear regression (MLR) models to identify correlations between the kinematic parameters of the vehicle and VEC.…”

Section: Related Work 61 Vehicle Energy Consumption Estimationmentioning

confidence: 99%

“…Liu et al [17] utilized an attention-based GRU to estimate the road-level VEC. DeepFEC [10] proposed a deep-learning-based model to forecast energy consumption on every road in a city based on real traffic conditions. Hua et al [14] developed a transfer learning model for electric vehicle energy consumption estimation based on insufficient electric vehicles and ragged driving trajectories.…”

Section: Related Work 61 Vehicle Energy Consumption Estimationmentioning

confidence: 99%

“…Prior studies on VEC estimation can be roughly divided into two categories: the Numerical methods [6,8,20] and the Data-driven methods [10,14,17,18,22]. Specifically, the numerical methods mainly focus on identifying factors that have the most significant influence on VEC.…”

Section: Introductionmentioning

confidence: 99%

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A Preference-aware Meta-optimization Framework for Personalized Vehicle Energy Consumption Estimation

Lai

Zhang

2023

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

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Vehicle Energy Consumption (VEC) estimation aims to predict the total energy required for a given trip before it starts, which is of great importance to trip planning and transportation sustainability. Existing approaches mainly focus on extracting statistically significant factors from typical trips to improve the VEC estimation. However, the energy consumption of each vehicle may diverge widely due to the personalized driving behavior under varying travel contexts. To this end, this paper proposes a preference-aware meta-optimization framework (Meta-Pec) for personalized vehicle energy consumption estimation. Specifically, we first propose a spatiotemporal behavior learning module to capture the latent driver preference hidden in historical trips. Moreover, based on the memorization of driver preference, we devise a selection-based driving behavior prediction module to infer driver-specific driving patterns on a given route, which provides additional basis and supervision signals for VEC estimation. Besides, a driver-specific meta-optimization scheme is proposed to enable fast model adaption by learning and sharing transferable knowledge globally. Extensive experiments on two real-world datasets show the superiority of our proposed framework against ten numerical and datadriven machine learning baselines. The source code is available at https://github.com/usail-hkust/Meta-Pec. CCS CONCEPTS• Information systems → Spatial-temporal systems.

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Section: Related Work 61 Vehicle Energy Consumption Estimationmentioning

confidence: 99%

Section: Related Work 61 Vehicle Energy Consumption Estimationmentioning

confidence: 99%

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A Preference-aware Meta-optimization Framework for Personalized Vehicle Energy Consumption Estimation

Lai

Zhang

2023

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

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“…The modeling of energy consumption in public transportation systems has been widely studied, and several methods have been explored to develop models to estimate energy consumption by taking into account a range of factors and parameters (7)(8)(9)(10)(11)(12)(13)(14)(15). The earliest framework to model fuel consumption and emissions in heavy-duty diesel vehicles was introduced in 2004.…”

Section: Related Workmentioning

confidence: 99%

Comprehensive Energy Modeling Framework for Multi-Powertrain Bus Transit Systems

Arabi

Oke

2023

Transportation Research Record: Journal of the Transportation R

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With the increasing emphasis on sustainable transportation solutions, there is an urgent need for a system that can accurately assess vehicle-specific energy consumption in real-time. However, the current modeling frameworks have a significant gap in that they are not demand- and congestion-responsive, nor are they capable of modeling the system-wide energy consumption of bus fleets at the vehicle-level, encompassing all three types of powertrains—conventional, hybrid, and electric. To address this gap, we present a comprehensive modeling framework that takes into account passenger loading, considers the dynamics of the system including acceleration, and is capable of predicting the real-time energy usage of each bus in a transit network. Using the Pioneer Valley Transit Authority system as a case study, we calibrated existing powertrain-specific models for conventional diesel, hybrid, and battery electric buses. Our demand- and congestion-responsive energy consumption modeling framework fills this critical gap and offers significant potential for future sustainable transportation planning. Relying on detailed trajectories computed from one month of bus location data, along with passenger counts and road grade, our framework is a novel contribution in that it provides spatiotemporal energy predictions for an entire bus system. As it is readily calibrated with potential improvements in accuracy given more data, this framework can be applied by transit agencies for the purposes of real-time energy consumption monitoring, fleet electrification planning, equity analyses, and carbon emission reduction strategies.

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“…Adaptive Neuro Fuzzy Inference System (ANFIS) [19,118,162] Multilayer feed forward NN (MFFNN) [99,118,121] Back Propagation NN (BPNN) [95,96,100,104,107,115,117,128,129,134,142,159] Multilayer Perceptron (MLP) [54,71,84,108,112,127,138,141,145,181] NARX NN [97] Stacked Autoencoder (SAEs) [116] ST-ResNE [151] Feed Forward NN [86] Extreme Learning Machine (ELM) [155] Recurrent 1 Kernel LMS (KLMS) [106] Fixed budget quantized KLMS (QKLMS-FB) [106] Recursive Least Squares (RLS) RLS [106] 1 Kernel RLS (KRLS) [106] RLS Tracker KRLS-T [106] Fuzzy Logic (FL) FL [32,113,123,142] 6 Fuzzy Inference System (Mamdami) [131] Interval Type-2 Fuzzy System (IT2FS) [153] Differential Evolution And Grey Wolf Optimizer (DEGWO) [110] 1…”

Section: # Of Studies Totalmentioning

confidence: 99%

Efficient Management of Energy Consumption of Electric Vehicles Using Machine Learning—A Systematic and Comprehensive Survey

2023

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Electric vehicles are growing in popularity as a form of transportation, but are still underused for several reasons, such as their relatively low range and the high costs associated with manufacturing and maintaining batteries. Many studies using several approaches have been conducted on electric vehicles. Among all studied subjects, here we are interested in the use of machine learning to efficiently manage the energy consumption of electric vehicles, in order to develop intelligent electric vehicles that make quick unprogrammed decisions based on observed data allowing minimal electricity consumption. Our interest is motivated by the adequate results obtained using machine learning in many fields and the increasing but still insufficient use of machine learning to efficiently manage the energy consumption of electric vehicles. From this standpoint, we have built this comprehensive survey covering a broad variety of scientific papers in the field published over the last few years. According to the findings, we identified the current trend and revealed future perspectives.

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DeepFEC: Energy Consumption Prediction under Real-World Driving Conditions for Smart Cities

Cited by 11 publications

References 25 publications

A Preference-aware Meta-optimization Framework for Personalized Vehicle Energy Consumption Estimation

A Preference-aware Meta-optimization Framework for Personalized Vehicle Energy Consumption Estimation

Comprehensive Energy Modeling Framework for Multi-Powertrain Bus Transit Systems

Efficient Management of Energy Consumption of Electric Vehicles Using Machine Learning—A Systematic and Comprehensive Survey

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