scholarly journals Residual Value Evaluation of Operating Pure Electric Vehicles Based on Machine Learning

2021 ◽  
Vol 1885 (4) ◽  
pp. 042019
Author(s):  
Yujiu Wang ◽  
Miaohua Huang ◽  
Kailun Chen
Keyword(s):  
Machine Learning ◽  
Electric Vehicles ◽  
Residual Value ◽  
Value Evaluation

Performance analysis on liquid-cooled battery thermal management for electric vehicles based on machine learning

2021 ◽  
Vol 494 ◽  
pp. 229727
Author(s):  
Xingwang Tang ◽  
Qin Guo ◽  
Ming Li ◽  
Changhua Wei ◽  
Zhiyao Pan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Performance Analysis ◽  
Thermal Management ◽  
Electric Vehicles ◽  
Battery Thermal Management

Machine learning roles in advancing the power network stability due to deployments of renewable energies and electric vehicles

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Mohamad Nassereddine
Keyword(s):  
Machine Learning ◽  
Renewable Energy ◽  
Electric Vehicles ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Power Network ◽  
Network Stability ◽  
Fast Charging ◽  
Electrical Loads ◽  
Charging Stations

AbstractRenewable energy sources are widely installed across countries. In recent years, the capacity of the installed renewable network supports large percentage of the required electrical loads. The relying on renewable energy sources to support the required electrical loads could have a catastrophic impact on the network stability under sudden change in weather conditions. Also, the recent deployment of fast charging stations for electric vehicles adds additional load burden on the electrical work. The fast charging stations require large amount of power for short period. This major increase in power load with the presence of renewable energy generation, increases the risk of power failure/outage due to overload scenarios. To mitigate the issue, the paper introduces the machine learning roles to ensure network stability and reliability always maintained. The paper contains valuable information on the data collection devises within the power network, how these data can be used to ensure system stability. The paper introduces the architect for the machine learning algorithm to monitor and manage the installed renewable energy sources and fast charging stations for optimum power grid network stability. Case study is included.

scholarly journals A Study on the Anomaly Detection of Engine Clutch Engagement/Disengagement Using Machine Learning for Transmission Mounted Electric Drive Type Hybrid Electric Vehicles

2021 ◽  
Vol 11 (21) ◽  
pp. 10187
Author(s):  
Yonghyeok Ji ◽  
Seongyong Jeong ◽  
Yeongjin Cho ◽  
Howon Seo ◽  
Jaesung Bang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Electric Vehicles ◽  
Electric Drive ◽  
Hybrid Electric Vehicles ◽  
Short Term Memory ◽  
Support Vector ◽  
Clutch Engagement ◽  
Hybrid Electric ◽  
Engine Clutch

Transmission mounted electric drive type hybrid electric vehicles (HEVs) engage/disengage an engine clutch when EV↔HEV mode transitions occur. If this engine clutch is not adequately engaged or disengaged, driving power is not transmitted correctly. Therefore, it is required to verify whether engine clutch engagement/disengagement operates normally in the vehicle development process. This paper studied machine learning-based methods for detecting anomalies in the engine clutch engagement/disengagement process. We trained the various models based on multi-layer perceptron (MLP), long short-term memory (LSTM), convolutional neural network (CNN), and one-class support vector machine (one-class SVM) with the actual vehicle test data and compared their results. The test results showed the one-class SVM-based models have the highest anomaly detection performance. Additionally, we found that configuring the training architecture to determine normal/anomaly by data instance and conducting one-class classification is proper for detecting anomalies in the target data.

scholarly journals A novel communication‐free charge controller for electric vehicles using machine learning

2021 ◽  
Author(s):  
Saifullah Shafiq ◽  
Bilal Khan ◽  
Petra Raussi ◽  
Ali Taleb Al‐Awami
Keyword(s):  
Machine Learning ◽  
Electric Vehicles ◽  
Free Charge

scholarly journals Enhancement of energy consumption estimation for electric vehicles by using machine learning

2021 ◽  
Vol 10 (1) ◽  
pp. 215
Author(s):  
Adnane Cabani ◽  
Peiwen Zhang ◽  
Redouane Khemmar ◽  
Jin Xu
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Electric Vehicles ◽  
Optimal Path ◽  
Influence Factors ◽  
Air Conditioner ◽  
Driver Behaviour ◽  
Variable Parameters ◽  
New Approach

<p>Three main classes are considered of significant influence factors when predicting the energy consumption rate of electric vehicles (EV): environment, driver behaviour, and vehicle. These classes take into account constant or variable parameters which influences the energy consumption of the EV. In this paper, we develop a new model taking into account the three classes as well as the interaction between them in order to improve the quality of EV energy consumption. The model depends on a new approach based on machine learning and especially k-NN algorithm in order to estimate the EV energy consumption. Following a lazy learning paradigm, this approach allows better estimation performance. The advantage of our proposal, in regards to mathematical approach, is taking into account the real situation of the ecosystem on the basis of historical data. In fact, the behavior of the driver (driving style, heating usage, air conditioner usage, battery state, etc.) impacts directly the EV energy consumption. The obtained results show that we can reach up to 96.5% of accuracy about the estimated of energy-consumption. The proposed method is used in order to find the optimal path between two points (departure-destination) in terms of energy consumption.</p>

Sign in / Sign up

Export Citation Format

Share Document