Recognition of Driving Behavior in Electric Vehicle’s Li-Ion Battery Aging

Abstract

In the foreseeable future, electric vehicles (EVs) will play a key role in the decarbonization of transport systems. Replacing vehicles powered by internal combustion engines (ICEs) with electric ones reduces the amount of CO2 being released into the atmosphere on a daily basis. The Achilles heel of electrical transportation lies in the car battery management system (BMS) that brings challenges to lithium-ion (Li-ion) battery optimization in finding the trade-off between driving and battery health in both the long- and short-term use. In order to optimize the state-of-health (SOH) of the EV battery, this study focuses on a review of the common Li-ion battery aging process and behavior detection methods. To implement the driving behavior approaches, a study of the public dataset produced by real-world EVs is also provided. This research clarifies the specific battery aging process and factors brought on by EVs. According to the battery aging factors, the unclear meaning of driving behavior is also clarified in an understandable manner. This work concludes by highlighting some challenges to be researched in the future to encourage the industry in this area.

A Lightweight Robust Distance Estimation Method for Navigation Aiding in Unsupervised Environment Using Monocular Camera

“…In this work, the largest target object is a long fence that has a width of 1.42 m. Assume that a user is wearing a camera at 1.2 m height and standing at 2.5 m from the object (the blind area is around 2.4 m in that case). The maximum error introduced by the simplification is calculated by Equation (12), which introduces the maximum of 4% error on the actual distance.…”

“…To locate the fences in the street, object detection is required, and it can be accomplished by a deep learning approach [10]. The distance information can then be utilized for various applications, including robotics, drone racing, autonomous vehicles, and navigation assistance tools [11][12][13][14]. Based on the trained model on the dataset, detection of the fences from public objects by the images and videos are captured by the user camera.…”