Event prediction algorithm using neural networks for the power management system of electric vehicles

Koo, Ki-sung; Manimaran, G.; Tian, Jin

doi:10.1016/j.asoc.2019.105709

Cited by 5 publications

(1 citation statement)

References 24 publications

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“…Additionally, estimating IMU yaw misalignment by fusing information from automotive onboard sensors and an adaptive Kalman filter can enhance the accuracy of ML models in capturing vehicle dynamics [177]. IoT-based datasets [44,47,56,64,81,85,95,[106][107][108]110,117,122,125,126,128,134,136,138,140,144,149,155,172] 24…”

Section: What Datasets Are Used?mentioning

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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