Orderly charging strategy of battery electric vehicle driven by real-world driving data

Tao, Ye; Huang, Miaohua; Chen, Yupu; Yang, Liang

doi:10.1016/j.energy.2019.116806

Cited by 44 publications

(24 citation statements)

References 29 publications

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“…The base learner usually is a regression tree. To train the model, an objective function is needed to measure how well the model fits the training data, as shown in formula (1). The objective function consists of two parts:…”

Section: Principle Of Xgboost， Lightgbm and The Blended Modelmentioning

confidence: 99%

“…An optimal tree structure needs to be chosen at each step, and XGBoost uses the objective function expressed by (1) to optimize the objective. The main principle of LightGBM is the same as that of XGBoost.…”

Section: Principle Of Xgboost， Lightgbm and The Blended Modelmentioning

confidence: 99%

“…We use the sign of the motor current to distinguish the vehicle state information such as acceleration, stopping, and braking. If the sign of motor current is positive, the vehicle is at acceleration or at a constant speed (denoted as [1,0,0] (denoted as [0, 1, 0]). If the sign of motor current is negative, then the vehicle is at regenerative braking (expressed as [0, 0, 1]).…”

Section: E Feature Engineeringmentioning

confidence: 99%

“…Electric vehicles (EVs) are regarded as a promising means of transportation to reduce air pollution and fossil fuel consumptions [1][2][3][4]. The major bottleneck for further deployment of EVs is related to the power batteries.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning-Based Method for Remaining Range Prediction of Electric Vehicles

et al. 2020

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Limited driving range is one of the major obstacles to the widespread application of electric vehicles (EVs). Accurately predicting the remaining driving range can effectively reduce the range anxiety of drivers. In this paper, a blended machine learning model was proposed to predict the remaining driving range of EVs based on real-world historical driving data. The blended model fuses two advanced machine learning algorithms of Extreme Gradient Boosting Regression Tree (XGBoost) and Light Gradient Boosting Regression Tree (LightGBM). The proposed model was trained to "learn" the relationship between the driving distance and the proposed features such as cumulative output energy of the motor and the battery, different driving patterns, and temperature of the battery). In addition, an "anchor (baseline) based" strategy was proposed and was seen to be able to effectively eliminate the unbalance distribution of dataset. The results of experiments suggest that our proposed anchor-based blended model has better performances with a smaller prediction error range of [-0.8, 0.8] as compared with previous methods. INDEX TERMS electric vehicle, remaining driving range estimation, machine learning, data mining.

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Section: Principle Of Xgboost， Lightgbm and The Blended Modelmentioning

confidence: 99%

Section: Principle Of Xgboost， Lightgbm and The Blended Modelmentioning

confidence: 99%

Section: E Feature Engineeringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine Learning-Based Method for Remaining Range Prediction of Electric Vehicles

et al. 2020

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“…The energy situation of the battery dramatically limits the task completion of the robots [ 7 ]. Energy-efficient strategies for mobile robots can expand the range of uses, perform more missions, and accomplish more complex operations [ 8 , 9 , 10 ]. Energy modeling has significance for battery energy management and range estimation of mobile robots [ 11 , 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Practical Model for Energy Consumption Analysis of Omnidirectional Mobile Robot

Hou

Zhou

Kim³

et al. 2021

Sensors

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The four-wheeled Mecanum robot is widely used in various industries due to its maneuverability and strong load capacity, which is suitable for performing precise transportation tasks in a narrow environment. While the Mecanum wheel robot has mobility, it also consumes more energy than ordinary robots. The power consumed by the Mecanum wheel mobile robot varies enormously depending on their operating regimes and environments. Therefore, only knowing the working environment of the robot and the accurate power consumption model can we accurately predict the power consumption of the robot. In order to increase the applicable scenarios of energy consumption modeling for Mecanum wheel robots and improve the accuracy of energy consumption modeling, this paper focuses on various factors that affect the energy consumption of the Mecanum wheel robot, such as motor temperature, terrain, the center of gravity position, etc. The model is derived from the kinematic and kinetic model combined with electrical engineering and energy flow principles. The model has been simulated in MATLAB and experimentally validated with the four-wheeled Mecanum robot platform in our lab. Experimental results show that the accuracy of the model reached 95%. The results of energy consumption modeling can help robots save energy by helping them to perform rational path planning and task planning.

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Electric Vehicle Battery Charging Scheduling Under the Battery Swapping Mode

Zhang

2021

Lecture Notes in Operations Research

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Orderly charging strategy of battery electric vehicle driven by real-world driving data

Cited by 44 publications

References 29 publications

Machine Learning-Based Method for Remaining Range Prediction of Electric Vehicles

Machine Learning-Based Method for Remaining Range Prediction of Electric Vehicles

Practical Model for Energy Consumption Analysis of Omnidirectional Mobile Robot

Electric Vehicle Battery Charging Scheduling Under the Battery Swapping Mode

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