Research

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23 pages, 6240 KiB

Open AccessArticle

Efficient Anticipatory Longitudinal Control of Electric Vehicles through Machine Learning-Based Prediction of Vehicle Speeds

by Tobias Eichenlaub, Paul Heckelmann and Stephan Rinderknecht

Vehicles 2023, 5(1), 1-23; https://doi.org/10.3390/vehicles5010001 - 21 Dec 2022

Cited by 2 | Viewed by 2031

Abstract

Driving style and external factors such as traffic density have a significant influence on the vehicle energy demand especially in city driving. A longitudinal control approach for intelligent, connected vehicles in urban areas is proposed in this article to improve the efficiency of [...] Read more.

Driving style and external factors such as traffic density have a significant influence on the vehicle energy demand especially in city driving. A longitudinal control approach for intelligent, connected vehicles in urban areas is proposed in this article to improve the efficiency of automated driving. The control approach incorporates information from Vehicle-2-Everything communication to anticipate the behavior of leading vehicles and to adapt the longitudinal control of the vehicle accordingly. A supervised learning approach is derived to train a neural prediction model based on a recurrent neural network for the speed trajectories of the ego and leading vehicles. For the development, analysis and evaluation of the proposed control approach, a co-simulation environment is presented that combines a generic vehicle model with a microscopic traffic simulation. This allows for the simulation of vehicles with different powertrains in complex urban traffic environment. The investigation shows that using V2X information improves the prediction of vehicle speeds significantly. The control approach can make use of this prediction to achieve a more anticipatory driving in urban areas which can reduce the energy consumption compared to a conventional Adaptive Cruise Control approach. Full article

(This article belongs to the Special Issue Electrified Intelligent Transportation Systems)

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19 pages, 4543 KiB

Open AccessArticle

A Real-Time Energy Consumption Minimization Framework for Electric Vehicles Routing Optimization Based on SARSA Reinforcement Learning

by Tawfiq M. Aljohani and Osama Mohammed

Vehicles 2022, 4(4), 1176-1194; https://doi.org/10.3390/vehicles4040062 - 18 Oct 2022

Cited by 8 | Viewed by 2283

Abstract

A real-time, metadata-driven electric vehicle routing optimization to reduce on-road energy requirements is proposed in this work. The proposed strategy employs the state–action–reward–state–action (SARSA) algorithm to learn the EV’s maximum travel policy as an agent. As a function of the received reward signal, [...] Read more.

A real-time, metadata-driven electric vehicle routing optimization to reduce on-road energy requirements is proposed in this work. The proposed strategy employs the state–action–reward–state–action (SARSA) algorithm to learn the EV’s maximum travel policy as an agent. As a function of the received reward signal, the policy model evaluates the optimal behavior of the agent. Markov chain models (MCMs) are used to estimate the agent’s energy requirements on the road, in which a single Markov step represents the average energy consumption based on practical driving conditions, including driving patterns, road conditions, and restrictions that may apply. A real-time simulation in Python with TensorFlow, NumPy, and Pandas library requirements was run, considering real-life driving data for two EVs trips retrieved from Google’s API. The two trips started at 4.30 p.m. on 11 October 2021, in Los Angeles, California, and Miami, Florida, to reach EV charging stations six miles away from the starting locations. According to simulation results, the proposed AI-based energy minimization framework reduces the energy requirement by 11.04% and 5.72%, respectively. The results yield lower energy consumption compared with Google’s suggested routes and previous work reported in the literature using the DDQN algorithm. Full article

(This article belongs to the Special Issue Electrified Intelligent Transportation Systems)

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13 pages, 1110 KiB

Open AccessArticle

A Way Forward for Electric Vehicle in Greater Bay Area: Challenges and Opportunities for the 21st Century

by Yui-Yip Lau, Andrew Yang Wu and Mak Wing Yan

Vehicles 2022, 4(2), 420-432; https://doi.org/10.3390/vehicles4020025 - 29 Apr 2022

Cited by 7 | Viewed by 4333

Abstract

The Greater Bay Area (GBA) accounts for a high percentage of pollution due to the large number of internal combustion engines. In the past few decades, there has been a significant increase in internal combustion engines vehicles while electric vehicles have not taken [...] Read more.

The Greater Bay Area (GBA) accounts for a high percentage of pollution due to the large number of internal combustion engines. In the past few decades, there has been a significant increase in internal combustion engines vehicles while electric vehicles have not taken off yet in GBA. To a certain extent, the acceptance of electric vehicles is still questionable from the industrial practitioners and local communities. As such, this research study aims to identify the challenges and opportunities of electric vehicles in GBA to address the future direction of electric vehicles in GBA. In this study, it identifies technology and economy as the main driving forces behind the development of electric vehicles. Furthermore, sustainability, safety, and the life of the batteries may induce the slow adoption of electric vehicles. As expected, the study develops a research agenda and contributes new knowledge in the field of electric vehicle. Full article

(This article belongs to the Special Issue Electrified Intelligent Transportation Systems)

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17 pages, 9107 KiB

Open AccessArticle

Estimation of Parallel Hybrid Scooter’s Energy Consumption through Real Urban Drive Cycle Using IMU

by Supriya Kalyankar-Narwade, Ramesh Kumar Chidambaram and Sanjay Patil

Vehicles 2022, 4(1), 297-313; https://doi.org/10.3390/vehicles4010018 - 15 Mar 2022

Viewed by 2080

Abstract

Drive cycle a is primary information useful for analyzing, designing, and optimizing automotive controllers for vehicle homologation. Conventional and electric vehicles are tested and certified based on the specified standard driving cycles as per vehicle category for emission compliance and energy consumption, respectively. [...] Read more.

Drive cycle a is primary information useful for analyzing, designing, and optimizing automotive controllers for vehicle homologation. Conventional and electric vehicles are tested and certified based on the specified standard driving cycles as per vehicle category for emission compliance and energy consumption, respectively. In countries such as India, this drive cycle fails to conceal the real-time drive cycles on urban roads with heavy traffic. This real-time drive cycle details the driving skill, congestion, road characteristics, acceleration and deceleration durations, etc. In this context, the real-time drive cycle is captured with the help of an Inertial Measurement Unit. Analysis of IMU measured data with a suitable sampling rate is carried out and energy characterizations are presented in this article. For better accuracy, the IMU data logger is set for an 8 Hz sampling rate which logs the vehicle dynamics data of a scooter. For urban traffic data collection, Pune city is selected and actual energy spent is estimated with the engine, electric, and hybrid modes. State of Charge based switching is carried out with the help of a hybrid controller and observations are tabulated. State of Charge thresholds are monitored and energy-efficient switching is decided. It is estimated from the results that hybrid conversion of a scooter is more efficient due to charge/regeneration into a Lithium-ion battery when the engine powers the wheel and while braking. The range is extended with the above configuration, and further can be increased based on higher battery capacity. Energy management is better handled with a hybrid electric controller for urban roads. Range anxiety issues of EV are lowered in HEV configuration and it is also estimated that parallel Hybrid scooters are more energy-efficient and release lower carbon emissions than conventional vehicles. Full article

(This article belongs to the Special Issue Electrified Intelligent Transportation Systems)

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9 pages, 3740 KiB

Open AccessArticle

Wireless Power Transfer System in Dynamic Conditions: A Field-Circuit Analysis

by Manuele Bertoluzzo, Paolo Di Barba, Michele Forzan, Maria Evelina Mognaschi and Elisabetta Sieni

Vehicles 2022, 4(1), 234-242; https://doi.org/10.3390/vehicles4010015 - 09 Mar 2022

Cited by 4 | Viewed by 1714

Abstract

In the paper, a Finite Element (FE) Analysis for investigating the electric properties of a Wireless Power Transfer System (WPTS) devoted to charging the batteries of electric vehicles is performed. In particular, the dynamic-WPTS, which is challenging because of the position-varying properties of [...] Read more.

In the paper, a Finite Element (FE) Analysis for investigating the electric properties of a Wireless Power Transfer System (WPTS) devoted to charging the batteries of electric vehicles is performed. In particular, the dynamic-WPTS, which is challenging because of the position-varying properties of the system, is considered. The field analysis is computationally heavy because of thin conductive layers modelling the car chassis: an effective analytical approximation for the field calculation in thin layers is applied to both the car frame bottom and the shielding aluminum layer. This approach allows for an accurate solution and, meanwhile, for a reduction in the computational costs, making the repeated simulations feasible. Full article

(This article belongs to the Special Issue Electrified Intelligent Transportation Systems)

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18 pages, 5744 KiB

Open AccessArticle

iLDM: An Interoperable Graph-Based Local Dynamic Map

by Mikel García, Itziar Urbieta, Marcos Nieto, Javier González de Mendibil and Oihana Otaegui

Vehicles 2022, 4(1), 42-59; https://doi.org/10.3390/vehicles4010003 - 08 Jan 2022

Cited by 10 | Viewed by 4325

Abstract

Local dynamic map (LDM) is a key component in the future of autonomous and connected vehicles. An LDM serves as a local database with the necessary tools to have a common reference system for both static data (i.e., map information) and dynamic data [...] Read more.

Local dynamic map (LDM) is a key component in the future of autonomous and connected vehicles. An LDM serves as a local database with the necessary tools to have a common reference system for both static data (i.e., map information) and dynamic data (vehicles, pedestrians, etc.). The LDM should have a common and well-defined input system in order to be interoperable across multiple data sources such as sensor detections or V2X communications. In this work, we present an interoperable graph-based LDM (iLDM) using Neo4j as our database engine and OpenLABEL as a common data format. An analysis on data insertion and querying time to the iLDM is reported, including a vehicle discovery service function in order to test the capabilities of our work and a comparative analysis with other LDM implementations showing that our proposed iLDM outperformed in several relevant features, furthering its practical utilisation in advanced driver assistance system development. Full article

(This article belongs to the Special Issue Electrified Intelligent Transportation Systems)

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17 pages, 2645 KiB

Open AccessArticle

Scheduling of a Microgrid with High Penetration of Electric Vehicles Considering Congestion and Operations Costs

by Alejandra Nitola, Jennyfer Marin and Sergio Rivera

Vehicles 2021, 3(3), 578-594; https://doi.org/10.3390/vehicles3030035 - 01 Sep 2021

Cited by 3 | Viewed by 2361

Abstract

This paper reviews the impact that can be presented by the immersion of generation sources and electric vehicles into the distribution network, with a technical, operational and commercial approach, given by the energy transactions between customer and operator. This requires a mathematical arrangement [...] Read more.

This paper reviews the impact that can be presented by the immersion of generation sources and electric vehicles into the distribution network, with a technical, operational and commercial approach, given by the energy transactions between customer and operator. This requires a mathematical arrangement to identify the balance between congestion and the operating cost of a microgrid when the operation scheduling of the system a day ahead of horizon time it is required. Thus, this research is directed to the solution, using heuristic algorithms, since they allow the non-convex constraints of the proposed mathematical problem. The optimization algorithm proposed for the analysis is given by the Multi-Object Particle Swarm Optimization (MOPSO) method, which provides a set of solutions that are known as Optimal Pareto. This algorithm is presented in an IEEE 141-bus system, which consists of a radial distribution network that considers 141 buses used by Matpower; this system was modified and included a series of renewable generation injections, systems that coordinate electric vehicles and battery storage, and the slack node was maintained and assumed to have (traditional generation). In the end it can be shown that the algorithm can provide solutions for network operation planning, test system robustness and verify some contingencies comparatively, always optimizing the balance between congestion and cost. Full article

(This article belongs to the Special Issue Electrified Intelligent Transportation Systems)

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21 pages, 945 KiB

Open AccessArticle

EV Overnight Charging Strategy in Residential Sector: Case of Winter Season in Quebec

by Alben Cardenas, Cristina Guzman and Wilmar Martinez

Vehicles 2021, 3(3), 557-577; https://doi.org/10.3390/vehicles3030034 - 01 Sep 2021

Cited by 9 | Viewed by 3885

Abstract

Electric Vehicle (EV) technologies offer a leading-edge solution for clean transportation and have evolved substantially in recent years. The growing market and policies of governments predict EV massive penetration shortly; however, their large deployment faces some resistances such as the high prices compared [...] Read more.

Electric Vehicle (EV) technologies offer a leading-edge solution for clean transportation and have evolved substantially in recent years. The growing market and policies of governments predict EV massive penetration shortly; however, their large deployment faces some resistances such as the high prices compared to Internal Combustion Engine (ICE) cars, the required infrastructure, the liability for novelty and standardisation. During winter periods of cold countries, since the use of heating systems increases, the peak power may produce stress to the grid. This fact, combined with EVs high penetration, during charging periods inside of high consumption hours might overload the network, becoming a threat to its stability. This article presents a framework to evaluate load shifting strategies to reschedule the EV charging to lower grid load periods. The undesirable “rebound” effect of load shifting strategies is confirmed, leading us to our EV local overnight charging strategy (EV-ONCS). Our strategy combines the forecast of residential demand using probabilistic distribution from historical consumption, prediction of the EV expected availability to charge and the charging strategy itself. EV-ONCS avoids demand rebound of classic methods and allows a peak-to-average ratio reduction demonstrating the relief for the grid with very low implementation cost. Full article

(This article belongs to the Special Issue Electrified Intelligent Transportation Systems)

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12 pages, 599 KiB

Open AccessArticle

A Priority-Based Autonomous Intersection Management (AIM) Scheme for Connected Automated Vehicles (CAVs)

by Hui Zhang, Rongqing Zhang, Chen Chen, Dongliang Duan, Xiang Cheng and Liuqing Yang

Vehicles 2021, 3(3), 533-544; https://doi.org/10.3390/vehicles3030032 - 13 Aug 2021

Cited by 8 | Viewed by 2535

Abstract

In this paper, we investigate the intersection traffic management for connected automated vehicles (CAVs). In particular, a decentralized autonomous intersection management scheme that takes into account both the traffic efficiency and scheduling flexibility is proposed, which adopts a novel intersection–vehicle model to check [...] Read more.

In this paper, we investigate the intersection traffic management for connected automated vehicles (CAVs). In particular, a decentralized autonomous intersection management scheme that takes into account both the traffic efficiency and scheduling flexibility is proposed, which adopts a novel intersection–vehicle model to check conflicts among CAVs in the entire intersection area. In addition, a priority-based collision-avoidance rule is set to improve the performance of traffic efficiency and shorten the delays of emergency CAVs. Moreover, a multi-objective function is designed to obtain the optimal trajectories of CAVs, which considers ride comfort, velocities of CAVs, fuel consumption, and the constraints of safety, velocity, and acceleration. Simulation results demonstrate that our proposed scheme can achieve good performance in terms of traffic efficiency and shortening the delays of emergency CAVs. Full article

(This article belongs to the Special Issue Electrified Intelligent Transportation Systems)

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21 pages, 781 KiB

Open AccessArticle

A Survey of Path Planning Algorithms for Mobile Robots

by Karthik Karur, Nitin Sharma, Chinmay Dharmatti and Joshua E. Siegel

Vehicles 2021, 3(3), 448-468; https://doi.org/10.3390/vehicles3030027 - 04 Aug 2021

Cited by 124 | Viewed by 22936

Abstract

Path planning algorithms are used by mobile robots, unmanned aerial vehicles, and autonomous cars in order to identify safe, efficient, collision-free, and least-cost travel paths from an origin to a destination. Choosing an appropriate path planning algorithm helps to ensure safe and effective [...] Read more.

Path planning algorithms are used by mobile robots, unmanned aerial vehicles, and autonomous cars in order to identify safe, efficient, collision-free, and least-cost travel paths from an origin to a destination. Choosing an appropriate path planning algorithm helps to ensure safe and effective point-to-point navigation, and the optimal algorithm depends on the robot geometry as well as the computing constraints, including static/holonomic and dynamic/non-holonomically-constrained systems, and requires a comprehensive understanding of contemporary solutions. The goal of this paper is to help novice practitioners gain an awareness of the classes of path planning algorithms used today and to understand their potential use cases—particularly within automated or unmanned systems. To that end, we provide broad, rather than deep, coverage of key and foundational algorithms, with popular algorithms and variants considered in the context of different robotic systems. The definitions, summaries, and comparisons are relevant to novice robotics engineers and embedded system developers seeking a primer of available algorithms. Full article

(This article belongs to the Special Issue Electrified Intelligent Transportation Systems)

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11 pages, 949 KiB

Open AccessArticle

Public Transportation Service Quality Evaluation during the COVID-19 Pandemic in Amman City Using Integrated Approach Fuzzy AHP-Kendall Model

by Ahmad Alkharabsheh and Szabolcs Duleba

Vehicles 2021, 3(3), 330-340; https://doi.org/10.3390/vehicles3030020 - 29 Jun 2021

Cited by 14 | Viewed by 3378

Abstract

The COVID-19 pandemic has affected public transportation worldwide, and its implications need to be evaluated and study deeply on all public transportation aspects. Therefore, an analysis has been created to examine the effects of the pandemic on public transportation service quality decisions to [...] Read more.

The COVID-19 pandemic has affected public transportation worldwide, and its implications need to be evaluated and study deeply on all public transportation aspects. Therefore, an analysis has been created to examine the effects of the pandemic on public transportation service quality decisions to have a better vision of the different stakeholders’ needs to keep the system functioning in a profitable way. Stakeholder participation in complex, multi-criteria decision-making often produces very different results in prioritizing the decision attributes. Rank correlation techniques generally measure the degree of agreement or non-agreement among the evaluator groups. However, the multi-criteria methodology can determine not only ordinal but also cardinal priorities. Consequently, except for the attributes’ positions, the weight values are also significant in the final decision. This paper aims to apply a more sophisticated measure of group agreement than rank correlation. First, the Fuzzy-hierarchical analytical process (FAHP) has been used to find out the aggregated weights, then the Kendall correlation values are computed to reveal stakeholder opinions. Finally, the agreement measure approach has been tested in a real-world case study: the public transport development decision of Amman, Jordan. The analysis shows that by applying the Kendall technique, Kendall could gain a more profound insight into the priority characteristics of different evaluator groups. Full article

(This article belongs to the Special Issue Electrified Intelligent Transportation Systems)

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15 pages, 1472 KiB

Open AccessArticle

Simulation-Based Assessment of Parking Constraints for Automated Mobility on Demand: A Case Study of Zurich

by Claudio Ruch, Roman Ehrler, Sebastian Hörl, Milos Balac and Emilio Frazzoli

Vehicles 2021, 3(2), 272-286; https://doi.org/10.3390/vehicles3020017 - 01 Jun 2021

Cited by 3 | Viewed by 3513

Abstract

In a coordinated mobility-on-demand system, a fleet of vehicles is controlled by a central unit and serves transportation requests in an on-demand fashion. An emerging field of research aims at finding the best way to operate these systems given certain targets, e.g., customer [...] Read more.

In a coordinated mobility-on-demand system, a fleet of vehicles is controlled by a central unit and serves transportation requests in an on-demand fashion. An emerging field of research aims at finding the best way to operate these systems given certain targets, e.g., customer service level or the minimization of fleet distance. In this work, we introduce a new element of fleet operation: the assignment of idle vehicles to a limited set of parking spots. We present two different parking operating policies governing this process and then evaluate them individually and together on different parking space distributions. We show that even for a highly restricted number of available parking spaces, the system can perform quite well, even though the total fleet distance is increased by 20% and waiting time by 10%. With only one parking space available per vehicle, the waiting times can be reduced by 30% with 20% increase in total fleet distance. Our findings suggest that increasing the parking capacity beyond one parking space per vehicle does not bring additional benefits. Finally, we also highlight possible directions for future research such as to find the best distribution of parking spaces for a given mobility-on-demand system and city. Full article

(This article belongs to the Special Issue Electrified Intelligent Transportation Systems)

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Review

Jump to: Research

14 pages, 429 KiB

Open AccessReview

Systemwide Considerations for Electrification of Transportation in Islands and Remote Locations

by Efrain O’Neill-Carrillo, Matthew Lave and Thad Haines

Vehicles 2021, 3(3), 498-511; https://doi.org/10.3390/vehicles3030030 - 06 Aug 2021

Cited by 1 | Viewed by 3630

Abstract

Electric vehicles (EVs) represent an important socio-economic development opportunity for islands and remote locations because they can lead to reduced fuel imports, electricity storage, grid services, and environmental and health benefits. This paper presents an overview of opportunities, challenges, and examples of EVs [...] Read more.

Electric vehicles (EVs) represent an important socio-economic development opportunity for islands and remote locations because they can lead to reduced fuel imports, electricity storage, grid services, and environmental and health benefits. This paper presents an overview of opportunities, challenges, and examples of EVs in islands and remote power systems, and is meant to provide background to researchers, utilities, energy offices, and other stakeholders interested in the impacts of electrification of transportation. The impact of uncontrolled EV charging on the electric grid operation is discussed, as well as several mitigation strategies. Of particular importance in many islands and remote systems is taking advantage of local resources by combining renewable energy and EV charging. Policy and economic issues are presented, with emphasis on the need for an overarching energy policy to guide the strategies for EVs growth. The key conclusion of this paper is that an orderly transition to EVs, one that maximizes benefits while addressing the challenges, requires careful analysis and comprehensive planning. Full article

(This article belongs to the Special Issue Electrified Intelligent Transportation Systems)

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