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Journal articles on the topic 'Self-balancing vehicle'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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1

Ajitha, S. P. "Two Wheeled Self Balancing Vehicle." International Journal for Research in Applied Science and Engineering Technology 6, no. 1 (January 31, 2018): 824–32. http://dx.doi.org/10.22214/ijraset.2018.1126.

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2

Qi, Ben Sheng, Kang Wang, Xuan Xuan Xiao, and Hong Xia Miao. "Design and Implementation of Self-Balancing Electric Vehicle Control System." Applied Mechanics and Materials 738-739 (March 2015): 950–54. http://dx.doi.org/10.4028/www.scientific.net/amm.738-739.950.

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In order to further optimize the control system of self-balancing electric vehicle, the method of linear quadratic regulator (LQR) based on genetic algorithm (GA) was presented in this paper. Firstly, the mathematical model of self-balancing electric vehicle was established by Lagrange equation, and then matrix Q and R in LQR which is used to control self-balancing electric vehicle system were optimized by GA. Thus the optimal control of self-balancing electric vehicle control system was realized. The optimization method was proved to be effective by comparing the simulation results of the optimized controller with the original.

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3

Liu, Yunping, Xijie Huang, Tianmiao Wang, Yonghong Zhang, and Xianying Li. "Nonlinear dynamics modeling and simulation of two-wheeled self-balancing vehicle." International Journal of Advanced Robotic Systems 13, no. 6 (November 16, 2016): 172988141667372. http://dx.doi.org/10.1177/1729881416673725.

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Two-wheeled self-balancing vehicle system is a kind of naturally unstable underactuated system with high-rank unstable multivariable strongly coupling complicated dynamic nonlinear property. Nonlinear dynamics modeling and simulation, as a basis of two-wheeled self-balancing vehicle dynamics research, has the guiding effect for system design of the project demonstration and design phase. Dynamics model of the two-wheeled self-balancing vehicle is established by importing a TSi ProPac package to the Mathematica software (version 8.0), which analyzes the stability and calculates the Lyapunov exponents of the system. The relationship between external force and stability of the system is analyzed by the phase trajectory. Proportional–integral–derivative control is added to the system in order to improve the stability of the two-wheeled self-balancing vehicle. From the research, Lyapunov exponent can be used to research the stability of hyperchaos system. The stability of the two-wheeled self-balancing vehicle is better by inputting the proportional–integral–derivative control. The Lyapunov exponent and phase trajectory can help us analyze the stability of a system better and lay the foundation for the analysis and control of the two-wheeled self-balancing vehicle system.

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4

Li, Huanping, Jian Wang, Guopeng Bai, and Xiaowei Hu. "Research on Self-Balancing System of Autonomous Vehicles Based on Queuing Theory." Sensors 21, no. 13 (July 5, 2021): 4619. http://dx.doi.org/10.3390/s21134619.

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In order to explore the changes that autonomous vehicles on the road would bring to the current traffic and make full use of the intelligent features of autonomous vehicles, the article defines a self-balancing system of autonomous vehicles. Based on queuing theory and stochastic process, the self-balancing system model with self-balancing characteristics is established to balance the utilization rate of autonomous vehicles under the conditions of ensuring demand and avoiding an uneven distribution of vehicle resources in the road network. The performance indicators of the system are calculated by the MVA (Mean Value Analysis) method. The analysis results show that the self-balancing process could reduce the average waiting time of customers significantly in the system, alleviate the service pressure while ensuring travel demand, fundamentally solve the phenomenon of concentrated idleness after the use of vehicles in the current traffic, maximize the use of the mobile vehicles in the system, and realize the self-balancing of the traffic network while reducing environmental pollution and saving energy.

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5

Dai, Min, Jian Wang, Xiao Gang Sun, Shuang Hu, and Jun Xiang Jia. "Design and Implementation of the Control System for Two-Wheeled Self-Balancing Vehicles." Advanced Materials Research 588-589 (November 2012): 1606–10. http://dx.doi.org/10.4028/www.scientific.net/amr.588-589.1606.

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A control-system design for a two-wheeled self-balancing vehicle is discussed in this paper. We have developed a low-cost hardware platform based on AVR MCU, accelerometer sensor and gyroscope sensor, for which the critical circuits, such as sensors and motor driver, are introduced. The control strategy operates by two steps: a) securing the real-time vehicle posture by integrating the data from accelerometer and gyroscope sensors; b) using a closed-loop PID controller to keep the vehicle balanced. This control system is applied to a prototype two-wheeled self-balancing vehicle, whose performance has turned out to be a satisfaction.

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6

Maddahi, A., A. H. Shamekhi, and A. Ghaffari. "A Lyapunov controller for self-balancing two-wheeled vehicles." Robotica 33, no. 1 (March 5, 2014): 225–39. http://dx.doi.org/10.1017/s0263574714000307.

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SUMMARYSegway is a self-balancing motorized two-wheeled vehicle which is able to carry the human body. The main issue in design of such a vehicle is to choose a stable control system capable of keeping the rider close to the upright position over smooth and non-smooth surfaces. This work extends the research previously performed by the authors for design of a controller, using the feedback linearization technique, to increase the stability of a two-wheeled vehicle carrying human. This paper investigates the design and validation of a controller for an inertial mobile vehicle using the Lyapunov's feedback control design technique. The system equations of motion are derived followed by finding the Lyapunov function required to design the controller. Owing to the discontinuity, originating from a sign function in the control law, the proposed control system is discontinuous. Therefore, the existence, continuity, and uniqueness of the solution are proven utilizing the Filippov's solution. Afterwards, the asymptotic stability of the control system is proven using the extensions of Lyapunov's stability theory to nonsmooth systems, and LaSalle's invariant set theorem. Finally, the effectiveness of the proposed control system is validated using simulation studies. Results confirm that the controller keeps the system stable while provides good position tracking responses.

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7

Vu, Ngoc Kien, and Hong Quang Nguyen. "Design Low-Order Robust Controller for Self-Balancing Two-Wheel Vehicle." Mathematical Problems in Engineering 2021 (May 24, 2021): 1–22. http://dx.doi.org/10.1155/2021/6693807.

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When there is no driver, balancing the two-wheel vehicle is a challenging but fascinating problem. There are various solutions for maintaining the balance of a two-wheel vehicle. This article presents a solution for balancing a two-wheel vehicle using a flywheel according to the inverted pendulum principle. Since uncertainties influence the actual operating environment of the vehicle, we have designed a robust controller RH∞ to maintain the vehicle equilibrium. Robust controllers often have a high order that can affect the actual control performance; therefore, order reduction algorithms are proposed. Using Matlab/Simulink, we compared the performance of the control system with different reduced-order controllers to choose a suitable low-order controller. Finally, experimental results using a low-order robust controller show that the vehicle balances steadily in different scenarios: no-load, variable load, stationary, and moving.

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8

Shabana, Ahmed A. "Geometric self-centering and force self-balancing of railroad-vehicle hunting oscillations." Acta Mechanica 232, no. 8 (May 25, 2021): 3323–29. http://dx.doi.org/10.1007/s00707-021-02983-w.

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9

Xu, Jun, Shi Shang, Guizhen Yu, Hongsheng Qi, Yunpeng Wang, and Shucai Xu. "Are electric self-balancing scooters safe in vehicle crash accidents?" Accident Analysis & Prevention 87 (February 2016): 102–16. http://dx.doi.org/10.1016/j.aap.2015.10.022.

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10

Gao, Mei Xia, and Jian Pu Bai. "The Research of Self-Balancing Vehicle Based on Posture Sensor System." Applied Mechanics and Materials 599-601 (August 2014): 735–38. http://dx.doi.org/10.4028/www.scientific.net/amm.599-601.735.

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The main subject of this thesis is to study a uneasily two-wheeled self-balancing vehicle system. Two tires are placed on two sides of the body parallel in this system . Controlling the rotation of two DC motors can achieve the goal of walking upright. The circuit part is mainly made up by attitude sensors parts (including Gyroscope and Accelerometer), control circuit and the driver board. Attitude sensors measure the tilt angle and the rate of change of inclination of vehicle, and then the controller calculate the responding data and finally drive two DC motors forward or backward to produce forward or backward acceleration to make the car balancing.

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11

Gogoi, Pallav, Manish Nath, Bumi Trueman Doley, Abhijit Boruah, and Hirok Jan Barman. "Design and Fabrication of Self Balancing Two Wheeler Vehicle Using Gyroscope." International Journal of Engineering and Technology 9, no. 3 (June 30, 2017): 2051–58. http://dx.doi.org/10.21817/ijet/2017/v9i3/1709030206.

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12

Yih, Chih-Chen. "Sliding-Mode Velocity Control of a Two-Wheeled Self-Balancing Vehicle." Asian Journal of Control 16, no. 6 (May 8, 2014): 1880–90. http://dx.doi.org/10.1002/asjc.900.

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13

Ciężkowski, M. "Modeling the Interaction Between two-Wheeled Self-Balancing Vehicle and its Rider." International Journal of Applied Mechanics and Engineering 18, no. 2 (June 1, 2013): 341–51. http://dx.doi.org/10.2478/ijame-2013-0020.

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Abstract The paper presents a modeling method and mathematical description of a two-wheeled self-balancing vehicle and its rider. A model of the rider that was used contains a model of the ankle joint, so we could determine the interaction between the rider and the vehicle. The paper presents results of computer simulations , which show the fundamental processes during riding, such as acceleration and braking.

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14

Xue, Han, Qionglin Fang, Jifeng Zhong, and Zhe-ping Shao. "H∞ Time-Delayed Fractional Order Adaptive Sliding Mode Control for Two-Wheel Self-Balancing Vehicles." Computational Intelligence and Neuroscience 2020 (August 10, 2020): 1–12. http://dx.doi.org/10.1155/2020/4529131.

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In this paper, a time-delayed fractional order adaptive sliding mode control algorithm is proposed for a two-wheel self-balancing vehicle system. The closed-loop system is proved based on the Lyapunov-Razumikhin function. The switching function is designed to make the system robust when facing uncertainties and external disturbances. It is designed to avoid monotonically increasing gains and can handle state-dependent uncertainties without a prior bound. The two-wheel self-balancing vehicle used in the experiment consists of a gyroscope MPU-6050 and accelerometer, a motor driving circuit composed of a motor driving chip TB6612FNG, and STM32F103x8B that is selected as the control core. The experimental results show that the time-delayed fractional order adaptive sliding mode control algorithm can make the vehicle achieve autonomous balance and quickly restore its stable state while appropriate disturbance is introduced.

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15

Zhang, Qingqing. "Research on Double Cascade Control Algorithm for Self-Balancing Two-Wheeled Vehicle." Journal of Applied Mathematics and Physics 04, no. 04 (2016): 618–22. http://dx.doi.org/10.4236/jamp.2016.44069.

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16

Yang, Zheng Cai, and Bao Hua Wang. "Study on Two-Wheeled Self-Balancing Electric Vehicle Based on Fuzzy PD Control Algorithm." Advanced Materials Research 1056 (October 2014): 162–65. http://dx.doi.org/10.4028/www.scientific.net/amr.1056.162.

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A Two-Wheeled Self-Balancing Electric vehicle control system was developed base on the system design, hardware development and software strategies.The paper designed the posture acquisition module by gyroscopes and accelerometer sensors, and improved the control ccuracy using Calman filtering algorithm. Based on the system structure model, construction of Two-Wheeled Self-Balancing Electric vehicle dynamic equations by using the analysis method of Newtonian echanics was developed.A fuzzy PD controller was designed,the displacement and velocity as the input variable is controlled by fuzzy controller, and the tilt angle and angular velocity is controlled by PD controller. Finally, the platform of real-time control system was established for parameters adjustment and real-time control.Experiments show that the system has good robustness,high real-time response and has a higher market value.

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17

Kilian, F. Johannes, Hubert Gattringer, and Hartmut Bremer. "Modeling and Quasi-Static Trajectory Control of a Self-Balancing Two-Wheeled Vehicle." PAMM 12, no. 1 (December 2012): 75–76. http://dx.doi.org/10.1002/pamm.201210029.

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18

Lin, Shui-Chun, Ching-Chih Tsai, and Hsu-Chih Huang. "Adaptive Robust Self-Balancing and Steering of a Two-Wheeled Human Transportation Vehicle." Journal of Intelligent & Robotic Systems 62, no. 1 (August 27, 2010): 103–23. http://dx.doi.org/10.1007/s10846-010-9460-5.

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19

Le, LIU, XING Li-hua, and SUN Zhang-jun. "Attitude control of self-balancing vehicle based on sliding mode variable structure control." Journal of Physics: Conference Series 1884, no. 1 (April 1, 2021): 012043. http://dx.doi.org/10.1088/1742-6596/1884/1/012043.

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20

Setiawan, Eko, and Dahnial Syauqy. "Semi-Adaptive Control Systems on Self-Balancing Robot using Artificial Neural Networks." INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi 5, no. 2 (August 8, 2021): 176–92. http://dx.doi.org/10.29407/intensif.v5i2.15296.

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A self-balancing type of robot works on the principle of maintaining the balance of the load's position to remains in the center. As a consequence of this principle, the driver can go forward reverse the vehicle by leaning in a particular direction. One of the factors affecting the control model is the weight of the driver. A control system that has been designed will not be able to balance the system if the driver using the vehicle exceeds or less than the predetermined weight value. The main objective of the study is to develop a semi-adaptive control system by implementing an Artificial Neural Network (ANN) algorithm that can estimate the driver's weight and use this information to reset the gain used in the control system. The experimental results show that the Artificial Neural Network can be used to estimate the weight of the driver's body by using 50-ms-duration of tilt sensor data to categorize into three defined classes that have been set. The ANN algorithm provides a high accuracy given by the results of the confusion matrix and the precision calculations, which show 99%.

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21

Kim, Jeyeon, Kenta Sato, Naohisa Hashimoto, Alexey Kashevnik, Kohji Tomita, Seiichi Miyakoshi, Yusuke Takinami, Osamu Matsumoto, and Ali Boyali. "Context-Based Rider Assistant System for Two Wheeled Self-Balancing Vehicles." SPIIRAS Proceedings 18, no. 3 (June 4, 2019): 583–614. http://dx.doi.org/10.15622/sp.2019.18.3.582-613.

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Personal mobility devises become more and more popular last years. Gyroscooters, two wheeled self-balancing vehicles, wheelchair, bikes, and scooters help people to solve the first and last mile problems in big cities. To help people with navigation and to increase their safety the intelligent rider assistant systems can be utilized that are used the rider personal smartphone to form the context and provide the rider with the recommendations. We understand the context as any information that characterize current situation. So, the context represents the model of current situation. We assume that rider mounts personal smartphone that allows it to track the rider face using the front-facing camera. Modern smartphones allow to track current situation using such sensors as: GPS / GLONASS, accelerometer, gyroscope, magnetometer, microphone, and video cameras. The proposed rider assistant system uses these sensors to capture the context information about the rider and the vehicle and generates context-oriented recommendations. The proposed system is aimed at dangerous situation detection for the rider, we are considering two dangerous situations: drowsiness and distraction. Using the computer vision methods, we determine parameters of the rider face (eyes, nose, mouth, head pith and rotation angles) and based on analysis of this parameters detect the dangerous situations. The paper presents a comprehensive related work analysis in the topic of intelligent driver assistant systems and recommendation generation, an approach to dangerous situation detection and recommendation generation is proposed, and evaluation of the distraction dangerous state determination for personal mobility device riders.

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22

Vu, Ngoc Kien, and Hong Quang Nguyen. "Balancing Control of Two-Wheel Bicycle Problems." Mathematical Problems in Engineering 2020 (July 22, 2020): 1–12. http://dx.doi.org/10.1155/2020/6724382.

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In recent years, more and more scientists have been interested in research on driving two-wheel bicycles. The problems in two-wheel bicycle control problem are self-balancing, uncertain models, and the impact of noise. In the paper, to solve the self-balancing problem, we use the flywheel method according to the inverted pendulum principle. To overcome the effects of the uncertain model, the impact of noise, we designed the vehicle balance controller according to the robust control algorithm. However, robust controllers often have a high order, which affects the quality during real control. To simplify the robust controller, we propose the use of a model order reduction algorithm. The simulation and experimental results have proved the correctness of the solutions given in the paper.

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23

Karkoub, M. A. "Modelling and robust μ-synthesis control of an intelligent self balancing two-wheel vehicle." Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics 220, no. 4 (December 2006): 293–302. http://dx.doi.org/10.1243/1464419jmbd35.

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24

Clarke, Andrew D., and Elham B. Makram. "A Novel Idea for Self-Balancing Car Parks for Plug in Electric Vehicle Charging." Journal of Power and Energy Engineering 02, no. 10 (2014): 34–40. http://dx.doi.org/10.4236/jpee.2014.210005.

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25

Lin, Shui-Chun, and Ching-Chih Tsai. "Development of a Self-Balancing Human Transportation Vehicle for the Teaching of Feedback Control." IEEE Transactions on Education 52, no. 1 (February 2009): 157–68. http://dx.doi.org/10.1109/te.2008.921799.

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26

Shang, Shi, Yanting Zheng, Ming Shen, Xianfeng Yang, and Jun Xu. "Numerical Investigation on Head and Brain Injuries Caused by Windshield Impact on Riders Using Electric Self-Balancing Scooters." Applied Bionics and Biomechanics 2018 (2018): 1–15. http://dx.doi.org/10.1155/2018/5738090.

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To investigate head-brain injuries caused by windshield impact on riders using electric self-balancing scooters (ESS). Numerical vehicle ESS crash scenarios are constructed by combining the finite element (FE) vehicle model and multibody scooter/rider models. Impact kinematic postures of the head-windshield contact under various impact conditions are captured. Then, the processes during head-windshield contact are reconstructed using validated FE head/laminated windshield models to assess the severity of brain injury caused by the head-windshield contact. Governing factors, such as vehicle speed, ESS speed, and the initial orientation of ESS rider, have nontrivial influences over the severity of a rider’s brain injuries. Results also show positive correlations between vehicle speed and head-windshield impact speeds (linear and angular). Meanwhile, the time of head-windshield contact happens earlier when the vehicle speed is faster. According to the intensive study, windshield-head contact speed (linear and angular), impact location on the windshield, and head collision area are found to be direct factors on ESS riders’ brain injuries during an impact. The von Mises stress and shear stress rise when relative contact speed of head-windshield increases. Brain injury indices vary widely when the head impacting the windshield from center to the edge or impacting with different areas.

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27

Klöppel, Manfred, Felix Römer, Michael Wittmann, Bijan Hatam, Thomas Herrmann, Lee Sim, Jun Lim, et al. "Scube—Concept and Implementation of a Self-balancing, Autonomous Mobility Device for Personal Transport." World Electric Vehicle Journal 9, no. 4 (December 5, 2018): 48. http://dx.doi.org/10.3390/wevj9040048.

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Public transportation (PT) systems suffer from disutility compared to private transportation due to the inability to provide passengers with a door-to-door service, referred to as the first/last mile problem. Personal mobility devices (PMDs) are thought to improve PT service quality by closing this first/last mile gap. However, current PMDs are generally driven manually by the rider and require a learning phase for safe vehicle operation. Additionally, most PMDs require a standing riding position and are not easily accessible to elderly people or persons with disabilities. In this paper, the concept of an autonomously operating mobility device is introduced. The visionary concept is designed as an on-demand transportation service which transports people for short to medium distances and increases the accessibility to public transport. The device is envisioned to be operated as a larger fleet and does not belong to an individual person. The vehicle features an electric powertrain and a one-axle self-balancing design with a small footprint. It provides one seat for a passenger and a tilt mechanism that is designed to improve the ride comfort and safety at horizontal curves. An affordable 3D-camera system is used for autonomous localization and navigation. For the evaluation and demonstration of the concept, a functional prototype is implemented.

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28

Chen, Long, Hai Wang, Yunzhi Huang, Zhaowu Ping, Ming Yu, Xuefeng Zheng, Mao Ye, and Youhao Hu. "Robust hierarchical sliding mode control of a two-wheeled self-balancing vehicle using perturbation estimation." Mechanical Systems and Signal Processing 139 (May 2020): 106584. http://dx.doi.org/10.1016/j.ymssp.2019.106584.

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29

Velazquez, Miguel, David Cruz, Salatiel Garcia, and Manuel Bandala. "Velocity and Motion Control of a Self-Balancing Vehicle Based on a Cascade Control Strategy." International Journal of Advanced Robotic Systems 13, no. 3 (January 2016): 106. http://dx.doi.org/10.5772/63933.

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30

Remy Neris, Olivier. "VHIPOD, Individual transport vehicle standing self-balancing station for disabled person with standing aid, ANR." Impact 2017, no. 4 (May 8, 2017): 80–82. http://dx.doi.org/10.21820/23987073.2017.4.81.

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31

Gonzalez Vaya, Marina, and Goran Andersson. "Self Scheduling of Plug-In Electric Vehicle Aggregator to Provide Balancing Services for Wind Power." IEEE Transactions on Sustainable Energy 7, no. 2 (April 2016): 886–99. http://dx.doi.org/10.1109/tste.2015.2498521.

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32

Lee, Suhan, Yongkuk Kim, and SangJoo Kwon. "Real-Time Estimation and Compensation Technique for Eccentricity of a Self-Balancing Vehicle Using Loadcells." Journal of Institute of Control, Robotics and Systems 27, no. 3 (March 31, 2021): 255–61. http://dx.doi.org/10.5302/j.icros.2021.20.0184.

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33

Hou, Quanshan, Yanan Zhang, Shuai Zhao, Yunhao Hu, and Yongwang Shen. "Tracking Control of Intelligent Vehicle Lane Change Based on RLMPC." E3S Web of Conferences 233 (2021): 04019. http://dx.doi.org/10.1051/e3sconf/202123304019.

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Autonomous lane changing, as a key module to realize high-level automatic driving, has important practical significance for improving the driving safety, comfort and commuting efficiency of vehicles. Traditional controllers have disadvantages such as weak scene adaptability and difficulty in balancing multi-objective optimization. In this paper, combined with the excellent self-learning ability of reinforcement learning, an interactive model predictive control algorithm is designed to realize the tracking control of the lane change trajectory. At the same time, two typical scenarios are verified by PreScan and Simulink, and the results show that the control algorithm can significantly improve the tracking accuracy and stability of the lane change trajectory.

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34

Oldenbroek, Vincent, Gilbert Smink, Tijmen Salet, and Ad J. M. van Wijk. "Fuel Cell Electric Vehicle as a Power Plant: Techno-Economic Scenario Analysis of a Renewable Integrated Transportation and Energy System for Smart Cities in Two Climates." Applied Sciences 10, no. 1 (December 23, 2019): 143. http://dx.doi.org/10.3390/app10010143.

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Renewable, reliable, and affordable future power, heat, and transportation systems require efficient and versatile energy storage and distribution systems. If solar and wind electricity are the only renewable energy sources, what role can hydrogen and fuel cell electric vehicles (FCEVs) have in providing year-round 100% renewable, reliable, and affordable energy for power, heat, and transportation for smart urban areas in European climates? The designed system for smart urban areas uses hydrogen production and FCEVs through vehicle-to-grid (FCEV2G) for balancing electricity demand and supply. A techno-economic analysis was done for two technology development scenarios and two different European climates. Electricity and hydrogen supply is fully renewable and guaranteed at all times. Combining the output of thousands of grid-connected FCEVs results in large overcapacities being able to balance large deficits. Self-driving, connecting, and free-floating car-sharing fleets could facilitate vehicle scheduling. Extreme peaks in balancing never exceed more than 50% of the available FCEV2G capacity. A simple comparison shows that the cost of energy for an average household in the Mid Century scenario is affordable: 520–770 €/year (without taxes and levies), which is 65% less compared to the present fossil situation. The system levelized costs in the Mid Century scenario are 71–104 €/MWh for electricity and 2.6–3.0 €/kg for hydrogen—and we expect that further cost reductions are possible.

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35

Jeong, Seonghee, Satoshi Takahara, Takayuki Takahashi, Yutaka Hiroi, and Osamu Matsumoto. "Driving Performance and User's Evaluation of Self-Balancing Personal Mobility Vehicle with a Pedal Driving Mechanism." International Journal of Advanced Robotic Systems 12, no. 7 (July 8, 2015): 87. http://dx.doi.org/10.5772/60719.

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36

Kim, Sangtae, and SangJoo Kwon. "Robust transition control of underactuated two-wheeled self-balancing vehicle with semi-online dynamic trajectory planning." Mechatronics 68 (June 2020): 102366. http://dx.doi.org/10.1016/j.mechatronics.2020.102366.

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37

Nguyen, C. X., A. D. Lukianov, T. D. Pham, and A. D. Nguyen. "Synthesis of a nonlinear control law with efficiency energy for the self-balancing two wheeled vehicle." IOP Conference Series: Materials Science and Engineering 900 (September 4, 2020): 012002. http://dx.doi.org/10.1088/1757-899x/900/1/012002.

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38

Bździuch, D., and W. Grzegożek. "A Two-Wheeled, Self-Balancing Electric Vehicle Used As an Environmentally Friendly Individual Means of Transport." IOP Conference Series: Materials Science and Engineering 148 (September 2016): 012003. http://dx.doi.org/10.1088/1757-899x/148/1/012003.

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39

Curiel-Olivares, G., J. Linares-Flores, J. F. Guerrero-Castellanos, and A. Hernández-Méndez. "Self-balancing based on Active Disturbance Rejection Controller for the Two-In-Wheeled Electric Vehicle, Experimental results." Mechatronics 76 (June 2021): 102552. http://dx.doi.org/10.1016/j.mechatronics.2021.102552.

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40

Thomas, Rémy, Fanny Lehmann, Jérôme Blatter, Ghislain Despesse, and Vincent Heiries. "Performance Analysis of a Novel High Frequency Self-Reconfigurable Battery." World Electric Vehicle Journal 12, no. 1 (January 11, 2021): 10. http://dx.doi.org/10.3390/wevj12010010.

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Self-reconfigurable battery architectures have gained a lot of interest recently in the literature, with more and more advanced functionalities. This paper describes the performance analysis of our proposed High Frequency Self-Reconfigurable Battery (HF SRB). To evaluate specific features with long-term dependencies of our system, a full functional behavioral simulator was developed. A comparison with a real 128-level HF SRB validated the simulator operation. The balancing performances obtained on vehicle test cycles showed the cell capacity discrepancy that the HF SRB is capable of handling in a single complete charge or discharge cycle. The magnitude of this gap demonstrated the extent to which the HF SRB is capable of operating with second life cells or even different chemistry mixes.

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Thomas, Rémy, Fanny Lehmann, Jérôme Blatter, Ghislain Despesse, and Vincent Heiries. "Performance Analysis of a Novel High Frequency Self-Reconfigurable Battery." World Electric Vehicle Journal 12, no. 1 (January 11, 2021): 10. http://dx.doi.org/10.3390/wevj12010010.

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Self-reconfigurable battery architectures have gained a lot of interest recently in the literature, with more and more advanced functionalities. This paper describes the performance analysis of our proposed High Frequency Self-Reconfigurable Battery (HF SRB). To evaluate specific features with long-term dependencies of our system, a full functional behavioral simulator was developed. A comparison with a real 128-level HF SRB validated the simulator operation. The balancing performances obtained on vehicle test cycles showed the cell capacity discrepancy that the HF SRB is capable of handling in a single complete charge or discharge cycle. The magnitude of this gap demonstrated the extent to which the HF SRB is capable of operating with second life cells or even different chemistry mixes.

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Jeong, Seonghee, Kazuki Kouzai, and Shinji Noguchi. "Influence of a rider’s rapid weight-shifting motion on the braking behavior of a self-balancing personal mobility vehicle." Advanced Robotics 30, no. 7 (February 17, 2016): 449–58. http://dx.doi.org/10.1080/01691864.2016.1144522.

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Qian, Qingwen, Junfeng Wu, and Zhe Wang. "Optimal path planning for two-wheeled self-balancing vehicle pendulum robot based on quantum-behaved particle swarm optimization algorithm." Personal and Ubiquitous Computing 23, no. 3-4 (April 13, 2019): 393–403. http://dx.doi.org/10.1007/s00779-019-01216-1.

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Barreras, Jorge Varela, Ricardo de Castro, Yihao Wan, and Tomislav Dragicevic. "A Consensus Algorithm for Multi-Objective Battery Balancing." Energies 14, no. 14 (July 15, 2021): 4279. http://dx.doi.org/10.3390/en14144279.

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Batteries stacks are made of cells in certain series-parallel arrangements. Unfortunately, cell performance degrades over time in terms of capacity, internal resistance, or self-discharge rate. In addition, degradation rates are heterogeneous, leading to cell-to-cell variations. Balancing systems can be used to equalize those differences. Dissipative or non-dissipative systems, so-called passive or active balancing, can be used to equalize either voltage at end-of-charge, or state-of-charge (SOC) at all times. While passive balancing is broadly adopted by industry, active balancing has been mostly studied in academia. Beyond that, an emerging research field is multi-functional balancing, i.e., active balancing systems that pursue additional goals on top of SOC equalization, such as equalization of temperature, power capability, degradation rates, or losses minimization. Regardless of their functionality, balancing circuits are based either on centralized or decentralized control systems. Centralized control entails difficult expandability and single point of failure issues, while decentralized control has severe controllability limitations. As a shift in this paradigm, here we present for the first time a distributed multi-objective control algorithm, based on a multi-agent consensus algorithm. We implement and validate the control in simulations, considering an electro-thermal lithium-ion battery model and an electric vehicle model parameterized with experimental data. Our results show that our novel multi-functional balancing can enhance the performance of batteries with substantial cell-to-cell differences under the most demanding operating conditions, i.e., aggressive driving and DC fast charging (2C). Driving times are extended (>10%), charging times are reduced (>20%), maximum cell temperatures are decreased (>10 °C), temperature differences are lowered (~3 °C rms), and the occurrence of low voltage violations during driving is reduced (>5×), minimizing the need for power derating and enhancing the user experience. The algorithm is effective, scalable, flexible, and requires low implementation and tuning effort, resulting in an ideal candidate for industry adoption.

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Memon, Irfan Ahmed, Saima Kalwar, Noman Sahito, Mir Aftab Hussain Talpur, Imtiaz Ahmed Chandio, Madzlan Napiah, and Hasan Tayyeb. "Mode Choice Modeling to Shift Car Travelers towards Park and Ride Service in the City Centre of Karachi." Sustainability 13, no. 10 (May 18, 2021): 5638. http://dx.doi.org/10.3390/su13105638.

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Currently, congestion in Karachi’s central business district (CBD) is the result of people driving their cars to work. Consequently, a park and ride (P&R) service has proved successful in decreasing traffic congestion and the difficulty of finding parking spaces from urban centers. The travelers cannot be convinced to shift towards the P&R service without an understanding of their travel behavior. Therefore, a travel behavior survey needs to be conducted to reduce the imbalance between public and private transport. Hence, mode choice models were developed to determine the factors that influence single-occupant vehicle (SOV) travelers’ decision to adopt the P&R service. Data were collected by an adapted self-administered questionnaire. Mode choice models were developed through logistic regression modeling by using the Statistical Package for the Social Sciences version 22. The findings concluded that more than 70%, specifically motorbike users, to avoid mental stress, and to protect the environment are willing to adopt the P&R service. Moreover, to validate the mode choice models, logit model training and a testing approach were used. In conclusion, by overcoming these influencing factors and balancing push and pull measures of travel demand management (TDM), SOV users can be encouraged to shift towards P&R services. Thus, research outcomes can support policymakers in implementing sustainable modes of public transportation.

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Tian, Jie, Jie Ding, Yongpeng Tai, and Zheshu Ma. "Control of Different-Axis Two-Wheeled Self-Balancing Vehicles." IEEE Access 8 (2020): 158839–51. http://dx.doi.org/10.1109/access.2020.3019538.

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Alkhedher et.al., Mohammad. "Adaptive 6 DOF Self-Balancing Platform for Autonomous Vehicles." International Journal of Computing and Digital Systems 9, no. 1 (January 1, 2020): 69–75. http://dx.doi.org/10.12785/ijcds/090107.

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Huang, Chung-Neng. "The Development of Self-Balancing Controller for One-Wheeled Vehicles." Engineering 02, no. 04 (2010): 212–19. http://dx.doi.org/10.4236/eng.2010.24031.

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LI, Ang, Ryosuke ANDO, Yasuhide NISHIHORI, Noriyasu KACHI, and Hideki KATO. "MEASURING THE ACCEPTABILITY OF SELF-BALANCING TWO-WHEELED PERSONAL MOBILITY VEHICLES." Journal of Japan Society of Civil Engineers, Ser. D3 (Infrastructure Planning and Management) 68, no. 5 (2012): I_599—I_605. http://dx.doi.org/10.2208/jscejipm.68.i_599.

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Karkoub, M. A., M. Zribi, and M. Parent. "Modelling and stabilization of a series of self-balancing two-wheel vehicles." Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics 224, no. 2 (October 8, 2009): 221–31. http://dx.doi.org/10.1243/14644193jmbd150.

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