Driving Ranges Prediction of Pure Electric Vehicle with Dual – Energy Storage System Based on BP Neural Network

Du, Shuyuan; Zuo, Chuncheng; Province, Jilin

doi:10.14257/ijunesst.2015.8.3.26

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Cited by 3 publications

(2 citation statements)

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“…A vehicle-level controller is implemented, embedding a forward-looking Powertrain model. Even though this scheme is built originally for PHEV, EV models can also employ the SoC (Status-Of-Charger) estimation method, especially when there are many unpredictable features even for the same route such as stop positions, average speeds, speed limits on a road, and the like [14].…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of a Tour Scheduler Focusing on Time-dependent Gains for Electric Vehicles

Lee¹,

Park²

2015

IJSH

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Section: Related Workmentioning

confidence: 99%

Performance Analysis of a Tour Scheduler Focusing on Time-dependent Gains for Electric Vehicles

Lee¹,

Park²

2015

IJSH

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“…In addition, no electricity can be generated even when the demand is high and the main grid must meet the demand solely. This problem arises from the fact that electricity cannot be economically stored for later use [3]. For more efficient operation, the main grid must decide how much energy it will generate from its power generation facility, taking into account the amount of electricity that can come from renewable energies.…”

Section: Introductionmentioning

confidence: 99%

Vote-Based Wind Speed Forecast Scheme Built Upon Different Artificial Neural Network Models

Lee

Park

2016

IJCA

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This paper designs and develops a wind speed forecast model based on artificial neural networks and voting strategies, aiming at efficiently integrating renewable energies into the power network. The hour-by-hour speed records collected in Jeju city during the past 15 years are classified and converted to create sequential, monthly, and seasonal forecast models, respectively. To predict the next hour speed, the speed records of the previous 5 hours are simultaneously fed to each model first. Then, the voting process picks and averages the two predictions having the best proximity out of 3. The evaluation procedure compares the predicted values and the actual speeds of 2014, which have not been used for training, and finds out that the maximum daily root mean square error for the proposed scheme is smaller than other stand-alone methods by 0.11mps. Moreover, the vote-based scheme avoids the worst case mis-prediction.

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A novel design of non-contact GMR displacement measurement system based on neural network

Liu

Zhang

2017

2017 IEEE International Conference on Information and Automation (ICIA)

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Driving Ranges Prediction of Pure Electric Vehicle with Dual – Energy Storage System Based on BP Neural Network

Abstract: Abstract

Cited by 3 publications

References 1 publication

Performance Analysis of a Tour Scheduler Focusing on Time-dependent Gains for Electric Vehicles

Performance Analysis of a Tour Scheduler Focusing on Time-dependent Gains for Electric Vehicles

Vote-Based Wind Speed Forecast Scheme Built Upon Different Artificial Neural Network Models

A novel design of non-contact GMR displacement measurement system based on neural network

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