A Regional Analysis of Electric LDV Portfolio Choices by Vehicle Manufacturers

Global light duty electric vehicle (EV) sales exceeded 10.5 million units in 2022, with a year-on-year growth of 55%, but these trends differ regionally. Despite the robust growth, upfront purchase price remains a challenge for consumers in different regions, and thus, OEMs make technology choices to respond to market needs. This paper examines the electrification portfolio choices of three major automotive manufacturers (OEMs) in different regions of the world, including Europe, Americas, Asia Pacific, and Africa/Middle-East. The analysis focuses on trends in dominant segments for Battery Electric Vehicles (BEV) and Plug-in Hybrid Electric Vehicles (PHEV), as well as battery chemistry choices. Regional differences show a trend towards SUVs for both BEVs and PHEVs. Tesla's dominance in the BEV market influences battery chemistry choices. Average battery sizes for BEVs remain similar in Europe and Americas, but lower in Asia Pacific and Africa/Middle East.Comment: 12 pages, 18 figures, 2 table

Charging demand of Plug-in Electric Vehicles: Forecasting travel behavior based on a novel Rough Artificial Neural Network approach

The market penetration of Plug-in Electric Vehicles (PEVs) is escalating due to their energy saving and environmental benefits. In order to address PEVs impact on the electric networks, the aggregators need to accurately predict the PEV Travel Behavior (PEV-TB) since the addition of a great number of PEVs to the current distribution network poses serious challenges to the power system. Forecasting PEV-TB is critical because of the high degree of uncertainties in drivers’ behavior. Existing studies mostly simplified the PEV-TB by mapping travel behavior from conventional vehicles. This could cause bias in power estimation considering the differences in PEV-TB because of charging pattern which consequently could bungle economic analysis of aggregators. In this study, to forecast PEV-TB an artificial intelligence-based method -feedforward and recurrent Artificial Neural Networks (ANN) with Levenberg Marquardt (LM) training method based on Rough structure - is developed. The method is based on historical data including arrival time, departure time and trip length. In this study, the correlation among arrival time, departure time and trip length is also considered. The forecasted PEV-TB is then compared with Monte Carlo Simulation (MCS) which is the main benchmarking method in this field. The results comparison depicted the robustness of the proposed methodology. The proposed method reduces the aggregators’ financial loss approximately by 16 $/PEV per year compared to the conventional methods. The findings underline the importance of applying more accurate methods to forecast PEV-TB to gain the most benefit of vehicle electrification in the years to come.Peer ReviewedPostprint (author's final draft

Forecasting Battery Electric Vehicle Charging Behavior: A Deep Learning Approach Equipped with Micro-Clustering and SMOTE Techniques

Energy systems, climate change, and public health are among the primary reasons for moving toward electrification in transportation. Transportation electrification is being promoted worldwide to reduce emissions. As a result, many automakers will soon start making only battery electric vehicles (BEVs). BEV adoption rates are rising in California, mainly due to climate change and air pollution concerns. While great for climate and pollution goals, improperly managed BEV charging can lead to insufficient charging infrastructure and power outages. This study develops a novel Micro Clustering Deep Neural Network (MCDNN), an artificial neural network algorithm that is highly effective at learning BEVs trip and charging data to forecast BEV charging events, information that is essential for electricity load aggregators and utility managers to provide charging stations and electricity capacity effectively. The MCDNN is configured using a robust dataset of trips and charges that occurred in California between 2015 and 2020 from 132 BEVs, spanning 5 BEV models for a total of 1570167 vehicle miles traveled. The numerical findings revealed that the proposed MCDNN is more effective than benchmark approaches in this field, such as support vector machine, k nearest neighbors, decision tree, and other neural network-based models in predicting the charging events.Comment: 18 pages,8 figures, 4 table

Life Cycle Assessment of Hydrogen Transportation Pathways via Pipelines and Truck Trailers: Implications as a Low Carbon Fuel

Hydrogen fuel cells have the potential to play a significant role in the decarbonization of the transportation sector globally and especially in California, given the strong regulatory and policy focus. Nevertheless, numerous questions arise regarding the environmental impact of the hydrogen supply chain. Hydrogen is usually delivered on trucks in gaseous form but can also be transported via pipelines as gas or via trucks in liquid form. This study is a comparative attributional life cycle analysis of three hydrogen production methods alongside truck and pipeline transportation in gaseous form. Impacts assessed include global warming potential (GWP), nitrogen oxide, volatile organic compounds, and particulate matter 2.5 (PM2.5). In terms of GWP, the truck transportation pathway is more energy and ecologically intensive than pipeline transportation, despite gaseous truck transport being more economical. A sensitivity analysis of pipeline transportation and life cycle inventories (LCI) attribution is included. Results are compared across multiple scenarios of the production and transportation pathways to discover the strongest candidates for minimizing the environmental footprint of hydrogen production and transportation. The results indicate the less ecologically intensive pathway is solar electrolysis through pipelines. For 1 percent pipeline attribution, the total CO2eq produced per consuming 1 MJ of hydrogen in a fuel cell pickup truck along this pathway is 50.29 g

To Charge or Not to Charge: Enhancing Electric Vehicle Charging Management with LSTM-based Prediction of Non-Critical Charging Sessions and Renewable Energy Integration

To maximize the greenhouse gas (GHG) emission reduction potential of Battery Electric Vehicles (BEVs), it is critical to develop EV dynamic charging management strategies. These strategies leverage the temporal variability in emissions associated with generated electricity to align EV charging with periods of low-carbon power generation. This study introduces a deep neural network tool to enable BEV drivers to make charging sessions align with the availability of cleaner energy resources. This study leverages a Long Short-Term Memory network to forecast individual BEV vehicle miles traveled (VMT) up to two days ahead, using a year-long dataset of driving and charging patterns from 66 California-based BEVs. Based on the predicted VMT, the model then estimates the vehicle's energy needs and the necessity of a charging session. This allows drivers to charge theirvehicles strategically, prioritizing low-carbon electricity periods without risking incomplete journeys. This framework empowers drivers to actively contribute to cleaner electricity consumption with minimal disruption to their daily routines. The tool developed in this project outperforms benchmark models such as recurrent neural networks and autoregressive integrated moving averages, demonstrating its predictive capabilities. To enhance the reliability of predictions, confidence intervals are integrated into the model, ensuring that the model does not disrupt drivers' daily routine trips when skipping non-critical charging events. The potential benefits of the tool are demonstrated by applying it to real-world EV data, finding that if drivers follow the tool’s predictive suggestion, they can reduce overall GHG emissions by 41% without changing their driving patterns. This study also found that even charging in regions with higher carbon-intensity electricity than California can achieve Californian emission levels for EV charging in the short term through strategic management of non-critical charging events. This findingreveals new possibilities for further emissions reduction from EV charging, even before the full transition to a carbon-neutral grid. View the NCST Project Webpag

Charging demand of Plug-in Electric Vehicles: Forecasting travel behavior based on a novel Rough Artificial Neural Network approach

The market penetration of Plug-in Electric Vehicles (PEVs) is escalating due to their energy saving and environmental benefits. In order to address PEVs impact on the electric networks, the aggregators need to accurately predict the PEV Travel Behavior (PEV-TB) since the addition of a great number of PEVs to the current distribution network poses serious challenges to the power system. Forecasting PEV-TB is critical because of the high degree of uncertainties in drivers’ behavior. Existing studies mostly simplified the PEV-TB by mapping travel behavior from conventional vehicles. This could cause bias in power estimation considering the differences in PEV-TB because of charging pattern which consequently could bungle economic analysis of aggregators. In this study, to forecast PEV-TB an artificial intelligence-based method -feedforward and recurrent Artificial Neural Networks (ANN) with Levenberg Marquardt (LM) training method based on Rough structure - is developed. The method is based on historical data including arrival time, departure time and trip length. In this study, the correlation among arrival time, departure time and trip length is also considered. The forecasted PEV-TB is then compared with Monte Carlo Simulation (MCS) which is the main benchmarking method in this field. The results comparison depicted the robustness of the proposed methodology. The proposed method reduces the aggregators’ financial loss approximately by 16 $/PEV per year compared to the conventional methods. The findings underline the importance of applying more accurate methods to forecast PEV-TB to gain the most benefit of vehicle electrification in the years to come.Peer Reviewe

A novel electricity price forecasting approach based on dimension reduction strategy and rough artificial neural networks

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.An accurate electricity price forecasting (EPF) plays a vital role in the deregulated energy markets and has a specific effect on optimal management of the power system. Considering all the potent factors in determining the electricity prices—some of which have stochastic nature—makes this a cumbersome task. In this article, first, Grey correlation analysis is applied to select the effective parameters in EPF problem and eliminate redundant factors based on low correlation grades. Then, a deep neural network with stacked denoising auto-encoders has been utilized to denoise data sets from different sources individually. After that, to detect the main features of the input data and putting aside the unnecessary features, dimension reduction process is implemented. Finally, the rough structure artificial neural network (ANN) has been executed to forecast the day-ahead electricity price. The proposed method is implemented on the data of Ontario, Canada, and the forecasted results are compared with different structures of ANN, support vector machine, long shortterm memory—benchmarking methods in this field—and forecasting data reported by independent electricity system operator (IESO) to evaluate the efficiency of the proposed approach. Furthermore, the results of this article indicate that the proposed method is efficient in terms of reducing error criterion and improves the forecasting error about 5–10 percent in comparison with IESO. This is a remarkable achievement in EPF field.Peer ReviewedPostprint (author's final draft