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Advanced Control and Estimation Concepts, and New Hardware Topologies for Future Mobility

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "E: Electric Vehicles".

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 28276

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Prof. Dr. Francis F. Assadian

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Department of Mechanical and Aerospace Engineering, University of California Davis, Davis, CA 95616, USA
Interests: autonomous vehicles; alternative powertrain; robust control; energetic modelling; application of artificial intelligence and machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

According to the National Research Council, the use of embedded systems throughout society could dominate previous milestones in the information revolution.

Mechatronics is the synergistic combination of electronic, mechanical, controls, software, and systems engineering in the design of processes and products. Mechatronic systems put “intelligence” into physical systems. Embedded sensors/actuators/processors are integral parts of mechatronic systems.

On the one hand, the implementation of mechatronic systems is on the continuous rise, especially in the applications of Future Mobility. On the other hand, manufacturers are working hard to reduce the implementation cost of these systems while trying not to comprise product quality. One way of addressing these conflicting objectives is through automatic controls and virtual sensing.

Therefore, this Special Issue seeks to contribute to advanced control and estimation concepts and new hardware topologies for future mobility. Topics of interest for publication include, but are not limited to:

New sensor fusion concepts;
Integrated vehicle dynamics and control architectures (IVDC);
New energy management and vehicle controls;
Steer by wire and advanced steering systems and controls;
New topologies for braking Systems including brake system optimization;
New traction and anti-lock braking control methods;
Advanced control methods for vehicle suspensions;
Low calibration estimation concepts;
Control and optimization of electric vehicles;
New alternative powertrains for Future Mobility;
Application of energy harvesting in Future Mobility;
New passive and active methods for vehicle dynamics improvement of Future Mobility.

Prof. Dr. Francis F. Assadian
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

estimation
sensor fusion
alternative powertrain
electric vehicles
vehicle dynamics
advanced control
energy harvesting
steering; suspension
braking systems
traction
ABS

Published Papers (11 papers)

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Editorial

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3 pages, 151 KiB

Open AccessEditorial

Advanced Control and Estimation Concepts and New Hardware Topologies for Future Mobility

by Francis F. Assadian

Energies 2022, 15(4), 1568; https://doi.org/10.3390/en15041568 - 21 Feb 2022

Viewed by 1196

Abstract

Current transportation and, in general, mobility have a negative impact on our environment [...] Full article

(This article belongs to the Special Issue Advanced Control and Estimation Concepts, and New Hardware Topologies for Future Mobility)

Research

Jump to: Editorial, Review

22 pages, 1958 KiB

Open AccessArticle

A Comparative Analysis of Brake-by-Wire Smart Actuators Using Optimization Strategies

by Ehsan Arasteh and Francis Assadian

Energies 2022, 15(2), 634; https://doi.org/10.3390/en15020634 - 17 Jan 2022

Cited by 11 | Viewed by 2488

Abstract

Since the automotive industry is shifting towards electrification, brake-by-wire technologies are becoming more prevalent. However, there has been little research comparing and optimizing brake-by-wire actuators in terms of their energy expenditure and response time. This paper investigates the comparison of three different smart brake-by-wire actuators, Electro-Hydraulic Brakes (EHB), Electro-Mechanical Brakes (EMB), and Electronic Wedge Brakes (EWB), first by defining an objective metric and then using both linear and nonlinear optimization techniques. Modeling of the actuators is performed using the bond graph method. Then, the controllers are designed using a robust control strategy, Youla parameterization. After designing the controllers, two types of optimization are performed on the actuators. Optimizations are performed in two ways: 1. by linearizing the plants and optimizing using their transfer functions and 2. by nonlinear optimization of the plants in the closed-loop following a specific clamp force target. The objective metrics or the cost functions for these optimizations are chosen to be the energy usage of the plants during the closed-loop operation, maximum power requirement, and their dynamic responsiveness. Using this optimization framework, we can show a significant improvement in the energy usage of the actuators and slight improvements in their responsiveness. In the end, the actuators are compared in terms of their energy usage for sets of initial and optimized physical parameters. Full article

(This article belongs to the Special Issue Advanced Control and Estimation Concepts, and New Hardware Topologies for Future Mobility)

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25 pages, 629 KiB

Open AccessArticle

A Study of Control Methodologies for the Trade-Off between Battery Aging and Energy Consumption on Electric Vehicles with Hybrid Energy Storage Systems

by Kevin Mallon and Francis Assadian

Energies 2022, 15(2), 600; https://doi.org/10.3390/en15020600 - 14 Jan 2022

Cited by 7 | Viewed by 1697

Abstract

Hybrid and electric vehicle batteries deteriorate from use due to irreversible internal chemical and mechanical changes, resulting in decreased capacity and efficiency of the energy storage system. This article investigates the modeling and control of a lithium-ion battery and ultracapacitor hybrid energy storage system for an electric vehicle for improved battery lifespan and energy consumption. By developing a control-oriented aging model for the energy storage components and integrating the aging models into an energy management system, the trade-off between battery degradation and energy consumption can be minimized. This article produces an optimal aging-aware energy management strategy that controls both battery and ultracapacitor aging and compares these results to strategies that control only battery aging, strategies that control battery aging factors but not aging itself, and non-optimal benchmark strategies. A case study on an electric bus with variously-sized hybrid energy storage systems shows that a strategy designed to control battery aging, ultracapacitor aging, and energy losses simultaneously can achieve a 28.2% increase to battery lifespan while requiring only a 7.0% decrease in fuel economy. Full article

(This article belongs to the Special Issue Advanced Control and Estimation Concepts, and New Hardware Topologies for Future Mobility)

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28 pages, 7825 KiB

Open AccessArticle

Optimization-Driven Powertrain-Oriented Adaptive Cruise Control to Improve Energy Saving and Passenger Comfort

by Pier Giuseppe Anselma

Energies 2021, 14(10), 2897; https://doi.org/10.3390/en14102897 - 17 May 2021

Cited by 15 | Viewed by 2719

Abstract

Assessing the potential of advanced driver assistance systems requires developing dedicated control algorithms for controlling the longitudinal speed of automated vehicles over time. In this paper, a multiobjective off-line optimal control approach for planning the speed of the following vehicle in adaptive cruise control (ACC) driving is proposed. The implemented method relies on the principle of global optimality fostered by dynamic programming (DP) and aims to minimize propelling energy consumption and enhance passenger comfort. The powertrain model and onboard control system are integrated within the proposed car-following optimization framework. The retained ACC approach ensures that the distance between the following vehicle and the preceding vehicle is always maintained within allowed limits. The flexibility of the proposed method is demonstrated here through ease of implementation on a wide range of powertrain categories, including a conventional vehicle propelled by an internal combustion engine solely, a pure electric vehicle, a parallel P2 hybrid electric vehicle (HEV) and a power-split HEV. Moreover, different driving conditions are considered to prove the effectiveness of the proposed optimization-driven ACC approach. Obtained simulation results suggest that up to 22% energy-saving and 48% passenger comfort improvement might be achieved for the ACC-enabled vehicle compared with the preceding vehicle by implementing the proposed optimization-driven ACC approach. Engineers may adopt the proposed workflow to evaluate corresponding real-time ACC approaches and assess optimal powertrain design solutions for ACC driving. Full article

(This article belongs to the Special Issue Advanced Control and Estimation Concepts, and New Hardware Topologies for Future Mobility)

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13 pages, 1309 KiB

Open AccessArticle

Estimation of Tire Normal Forces including Suspension Dynamics

by Louis Filipozzi, Francis Assadian, Ming Kuang, Rajit Johri and Jose Velazquez Alcantar

Energies 2021, 14(9), 2378; https://doi.org/10.3390/en14092378 - 22 Apr 2021

Cited by 7 | Viewed by 2428

Abstract

Tire normal forces are difficult to measure, but information on the vehicle normal force can be used in many automotive engineering applications, e.g., rollover detection and vehicle and wheel stability. Previous papers use algebraic equations to estimate the tire normal force. In this article, the estimation of tire normal force is formulated as an input estimation problem. Two observers are proposed to solve this problem by using a quarter-car suspension model. First, the Youla Controller Output Observer framework is presented. It converts the estimation problem into a control problem and produces a Youla parameterized controller as observer. Second, a Kalman filter approach is taken and the input estimation problem is addressed with an Unbiased Minimum Variance Filter. Both methods use accelerometer and suspension deflection sensors to determine the vehicle normal force. The design of the observers is validated in simulation and a sensitivity analysis is performed to evaluate their robustness. Full article

(This article belongs to the Special Issue Advanced Control and Estimation Concepts, and New Hardware Topologies for Future Mobility)

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21 pages, 803 KiB

Open AccessArticle

Stochastic Drift Counteraction Optimal Control of a Fuel Cell-Powered Small Unmanned Aerial Vehicle

by Jiadi Zhang, Ilya Kolmanovsky and Mohammad Reza Amini

Energies 2021, 14(5), 1304; https://doi.org/10.3390/en14051304 - 27 Feb 2021

Cited by 1 | Viewed by 1585

Abstract

This paper investigates optimal power management of a fuel cell hybrid small unmanned aerial vehicle (sUAV) from the perspective of endurance (time of flight) maximization in a stochastic environment. Stochastic drift counteraction optimal control is exploited to obtain an optimal policy for power management that coordinates the operation of the fuel cell and battery to maximize the expected flight time while accounting for the limits on the rate of change of fuel cell power output and the orientation dependence of fuel cell efficiency. The proposed power management strategy accounts for known statistics in transitions of propeller power and climb angle during the mission, but does not require the exact preview of their time histories. The optimal control policy is generated offline using value iterations implemented in Cython, demonstrating an order of magnitude speedup as compared to MATLAB. It is also shown that the value iterations can be further sped up using a discount factor, but at the cost of decreased performance. Simulation results for a

1.5

kg sUAV are reported that illustrate the optimal coordination between the fuel cell and the battery during aircraft maneuvers, including a turnpike in the battery state of charge (

S O C

) trajectory. As the fuel cell is not able to support fast changes in power output, the optimal policy is shown to charge the battery to the turnpike value if starting from a low initial

S O C

value. If starting from a high

S O C

value, the battery energy is used till a turnpike value of the

S O C

is reached with further discharge delayed to later in the flight. For the specific scenarios and simulated sUAV parameters considered, the results indicate the capability of up to

2.7

h of flight time. Full article

(This article belongs to the Special Issue Advanced Control and Estimation Concepts, and New Hardware Topologies for Future Mobility)

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18 pages, 16082 KiB

Open AccessArticle

Energy Optimization of Electric Vehicles by Distributing Driving Power Considering System State Changes

by In-Gyu Jang, Chung-Seong Lee and Sung-Ho Hwang

Energies 2021, 14(3), 594; https://doi.org/10.3390/en14030594 - 25 Jan 2021

Cited by 5 | Viewed by 2815

Abstract

In a battery-electric vehicle, a representative electric vehicle, there is a growing demand for performance and one-charge mileage improvement. As an alternative to such improvements, the capacity of the battery has been increased; however, due to the corresponding increase in the weight of the battery and the limited space in the vehicle, increasing the capacity of the battery also has limitations. Therefore, researches are being actively conducted to improve system operation efficiency to overcome such limitations. This paper proposes a distributing method of the driving forces to a battery-powered electric shuttle bus for last-mile mobility equipped with the decentralized driving system while taking into account voltage changes of the input terminals due to changes in the battery charge. The system operation efficiency changes were compared and evaluated by performing energy consumption analysis using ‘Manhattan Bus Driving Cycle’ at low voltage condition (SOC 20%). Various analyzes were performed and compared, such as the uniform distribution method of driving forces of the front and rear wheels (Uniform), the optimization method without considering the input terminal voltage change (V_norm = 90 V), and the optimization method considering the input terminal voltage change (V_dclink). As a result, it shows that the proposed algorithm can improve 6.0% compared to the conventional uniform driving force distribution method (Uniform). Moreover, it shows that the real-time optimization method without considering the input voltage change (V_norm = 90 V) can improve 5.3% compared to the uniform distribution method. The proposed method can obtain an additional 0.7% increase in total cost compared to the existing optimization method, which shows that the vehicle system has cost-effectiveness by reducing the battery capacity required to achieve the same mileage. Full article

(This article belongs to the Special Issue Advanced Control and Estimation Concepts, and New Hardware Topologies for Future Mobility)

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17 pages, 3571 KiB

Open AccessArticle

Power Split Supercharging: A Mild Hybrid Approach to Boost Fuel Economy

by Shima Nazari, Jason Siegel, Robert Middleton and Anna Stefanopoulou

Energies 2020, 13(24), 6580; https://doi.org/10.3390/en13246580 - 14 Dec 2020

Cited by 7 | Viewed by 1879

Abstract

This work investigates an innovative low-voltage (<60 V) hybrid device that enables engine boosting and downsizing in addition to mild hybrid functionalities such as regenerative braking, start-stop, and torque assist. A planetary gear set and a brake permit the power split supercharger (PSS) to share a 9 kW motor between supercharging the engine and direct torque supply to the crankshaft. In contrast, most e-boosting schemes use two separate motors for these two functionalities. This single motor structure restricts the PSS operation to only one of the supercharging or parallel hybrid modes; therefore, an optimized decision making strategy is necessary to select both the device mode and its power split ratio. An adaptive equivalent consumption minimization strategy (A-ECMS), which uses the battery state of charge (SoC) history to adjust the equivalence factor, is developed for energy management of the PSS. The A-ECMS effectiveness is compared against a dynamic programming (DP) solution with full drive cycle preview through hardware-in-the-loop experiments on an engine dynamometer testbed. The experiments show that the PSS with A-ECMS reduces vehicle fuel consumption by 18.4% over standard FTP75 cycle, compared to a baseline turbocharged engine, while global optimal DP solution decreases the fuel consumption by 22.8% compared to the baseline. Full article

(This article belongs to the Special Issue Advanced Control and Estimation Concepts, and New Hardware Topologies for Future Mobility)

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24 pages, 9069 KiB

Open AccessArticle

Virtual Simulation of Electric Bus Fleets for City Bus Transport Electrification Planning

by Jakov Topić, Jure Soldo, Filip Maletić, Branimir Škugor and Joško Deur

Energies 2020, 13(13), 3410; https://doi.org/10.3390/en13133410 - 02 Jul 2020

Cited by 16 | Viewed by 4349

Abstract

City bus transport electrification has a strong potential of improving city air quality, reducing noise pollution and increasing passenger satisfaction. Since the city bus operation is rather deterministic and intermittent, the driving range- and charging-related concerns may be effectively overcome by means of fast charging at end stations and/or slow charging in depot. In order to support decision making processes, a simulation tool for planning of city bus transport electrification has been developed and it is presented in this paper. The tool is designed to use real/recorded driving cycles and techno-economic data, in order to calculate the optimal type and number of e-buses and chargers, and predict the total cost of ownership including investment and exploitation cost. The paper focuses on computationally efficient e-bus fleet simulation including powertrain control and charging management aspects, which is illustrated through main results of a pilot study of bus transport electrification planning for the city of Dubrovnik. Full article

(This article belongs to the Special Issue Advanced Control and Estimation Concepts, and New Hardware Topologies for Future Mobility)

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22 pages, 8099 KiB

Open AccessArticle

Active Disturbance Rejection Control of Differential Drive Assist Steering for Electric Vehicles

by Junnian Wang, Xiandong Wang, Zheng Luo and Francis Assadian

Energies 2020, 13(10), 2647; https://doi.org/10.3390/en13102647 - 22 May 2020

Cited by 20 | Viewed by 3068

Abstract

The differential drive assist steering (DDAS) system makes full use of the advantages of independent control of wheel torque of electric vehicle driven by front in-wheel motors to achieve steering assistance and reduce the steering effort of the driver, as the electric power steering (EPS) system does. However, as an indirect steering assist technology that applies steering system assistance via differential drive, its linear control algorithm, like existing proportion integration differentiation (PID) controllers, cannot take the nonlinear characteristics of the tires’ dynamics into account which results in poor performance in road feeling and tracking accuracy. This paper introduces an active disturbance rejection control (ADRC) method into the control issue of the DDAS. First, the third-order ADRC controller of the DDAS is designed, and the simulated annealing algorithm is used to optimize the parameters of ADRC controller offline considering that the parameters of ADRC controller are too many and the parameter tuning is complex. Finally, the 11-DOF model of the electric vehicle driven by in-wheel motors is built, and the standard working conditions are selected for simulation and experimental verification. The results show that the ADRC controller designed in this paper can not only obviously reduce the steering wheel effort of the driver like PID controller, but also have better nonlinear control performance in tracking accuracy and smooth road feeling of the driver than the traditional PID controller. Full article

(This article belongs to the Special Issue Advanced Control and Estimation Concepts, and New Hardware Topologies for Future Mobility)

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Review

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30 pages, 5900 KiB

Open AccessReview

Velocity Prediction Based on Vehicle Lateral Risk Assessment and Traffic Flow: A Brief Review and Application Examples

by Lin Li, Serdar Coskun, Jiaze Wang, Youming Fan, Fengqi Zhang and Reza Langari

Energies 2021, 14(12), 3431; https://doi.org/10.3390/en14123431 - 10 Jun 2021

Cited by 9 | Viewed by 2988

Abstract

Forecasting future driving conditions such as acceleration, velocity, and driver behaviors can greatly contribute to safety, mobility, and sustainability issues in the development of new energy vehicles (NEVs). In this brief, a review of existing velocity prediction techniques is studied from the perspective of traffic flow and vehicle lateral dynamics for the first time. A classification framework for velocity prediction in NEVs is presented where various state-of-the-art approaches are put forward. Firstly, we investigate road traffic flow models, under which a driving-scenario-based assessment is introduced. Secondly, vehicle speed prediction methods for NEVs are given where an extensive discussion on traffic flow model classification based on traffic big data and artificial intelligence is carried out. Thirdly, the influence of vehicle lateral dynamics and correlation control methods for vehicle speed prediction are reviewed. Suitable applications of each approach are presented according to their characteristics. Future trends and questions in the development of NEVs from different angles are discussed. Finally, different from existing review papers, we introduce application examples, demonstrating the potential applications of the highlighted concepts in next-generation intelligent transportation systems. To sum up, this review not only gives the first comprehensive analysis and review of road traffic network, vehicle handling stability, and velocity prediction strategies, but also indicates possible applications of each method to prospective designers, where researchers and scholars can better choose the right method on velocity prediction in the development of NEVs. Full article

(This article belongs to the Special Issue Advanced Control and Estimation Concepts, and New Hardware Topologies for Future Mobility)

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