Machine learning toward advanced energy storage devices and systems.

Connected automated driving technologies have shown tremendous improvement in recent years. However, it is still not clear how driving behaviors and energy consumption correlate with each other and to what extent these factors related to connected vehicles can influence the motion prediction performance. The precise recognition of driving behaviors and prediction of the vehicle motion is critical to the driving safety for connected automated vehicles (CAVs). Hence, in this study, an energy-aware driving pattern analysis and motion prediction system are proposed for CAVs using a deep learning-based time-series modeling approach. First, energy-aware longitudinal acceleration and deceleration behaviors and lateral lane-change behaviors are statistically analyzed. Then, a sliding standard deviation (SSD) test is applied to evaluate the smoothness of the trajectory and velocity signals considering different energy consumption levels. An energy-aware personalized joint time-series modeling (PJTSM) approach based on a deep recurrent neural network (RNN) and long short-term memory (LSTM) cell are proposed for accurate motion (trajectory and velocity) prediction of the leading vehicle. Finally, the differences in the prediction performance regarding different energy consumption levels are compared and discussed. It is shown that due to the higher randomness of the driving behaviors, the prediction accuracy for heavy energy users is the lowest among the three categories, which means it is harder to anticipate the driving behaviors of cars exhibiting heavy energy consumption. The personalized estimation of driving behaviors of CAVs will contribute to safer automated driving and transportation systems.

/pdf/toward-safe-and-smart-mobility-energy-aware-deep-learning-dnvb9nmanv.pdf

Toward Safe and Smart Mobility: Energy-Aware Deep Learning for Driving Behavior Analysis and Prediction of Connected Vehicles

A newly introduced term in the field of simulating an artificial or physical system is that of the “Digital Twin” concept method. It employs a digital representation and modeling method, capable of expanding and improving the life cycle of complex items, systems, and processes. Nowadays, digital twin technology has become a key research field worldwide. In this context, it is applied and utilized in various fields. One such field is the automotive industry, a technological field that has great implications in users’ everyday life. Digital twin technology not only has great contributions from the initial stages of design until the final construction stages of vehicles, but also during its use, drawing useful information from its daily functions and making the driving experience more enjoyable, comfortable, and safe. It is worth noting that the vehicles that can greatly benefit from the use of digital twins are electric vehicles, which has tended to acquire greater shares in the last decade.

/pdf/digital-twins-in-the-automotive-industry-the-road-toward-22vrd3a7.pdf

Digital Twins in the Automotive Industry: The Road toward Physical-Digital Convergence

The continuous technical improvements involving electric motors, battery packs, and general powertrain equipment make it strictly necessary to predict or evaluate the energy consumption of electric vehicles (EVs) with reasonable accuracy. The significant improvements in computing power in the last decades have allowed the implementation of various simulation scenarios and the development of strategies for vehicle modelling, thus estimating energy consumption with higher accuracy. This paper gives a general overview of the strategies adopted to model EVs for evaluating or predicting energy consumption. The need to develop such solutions is due to the basis of each analysis, as well as the type of results that must be produced and delivered. This last point strongly influences the whole set-up process of the analysis, from the available and collected dataset to the choice of the algorithm itself.

https://www.mdpi.com/1996-1073/15/21/8115/pdf?version=1667380484

Strategies for the Modelisation of Electric Vehicle Energy Consumption: A Review

Literature Review on Wireless Charging Technologies: Future Trend for Electric Vehicle?

An accurate prediction of the electric vehicles (EVs) energy consumption is the crucial requirement to deliver the promise of the green energy solution for relieving the concerns from fossil energy depletion and vehicle emissions. To solve the problem, the most substantial facing challenges are laying down on heterogeneous data insight, modelling the non-linear problem, and the lack of supporting technologies to provide the primary data to model the problem. In this paper, the latent pattern of the heterogeneous data is extracted and presented as a new set of metadata. Statistical features extracted from crowdsourced EVs profiles for the energy prediction since they consider the varying impact factors of the individual impact factors. Supervised learning approach is followed to address the non-linear problem and unseen correlation in the heterogeneous data. The results show an improvement of the energy prediction if the statistical features are considered.

Energy Consumption Prediction of Electric Vehicles Based on Big Data Approach

In this study, a novel estimation model presented in order to calculate the total energy consumption of EVs for different time intervals in the scenario that all the Taxi in New York City are replaced with a specific Electric Vehicle (EV). While the other studies focused on energy consumption prediction on an individual EV, the outputs and results of this study provide a promising approach to estimate the total energy demands of e-Cars. One of the main contributions of this article is to identify the demands patterns at any time intervals (the day, month, and year) to adjust the power generation's schedules and prevent any disturbances due to the consequences of charging a massive amount of EVs in the same time. The output shows that energy consumptions are highly correlated to the weekdays; for instance, generally, the Thursdays and Fridays are considered as a high peak while weekend-days and holidays are the minimums.

Seyedmahdi Miraftabzadeh

Papers

Energy Consumption Prediction of Electric Vehicles Based on Big Data Approach

Estimating the Total energy Consumptions of Electrical Vehicles under Real-World Driving Conditions