Conscious mobility for urban spaces: case studies review and indicator framework design

A lack of data collection on conscious mobility behaviors has been identified in current sustainable and smart mobility planning, development and implementation strategies. This leads to technocentric solutions that do not place people and their behavior at the center of new mobility solutions in urban centers around the globe. This paper introduces the concept of conscious mobility to link techno-economic analyses with user awareness on the impact of their travel decisions on other people, local urban infrastructure and the environment through systematic big data collection. A preliminary conscious mobility indicator framework is presented to leverage behavioral considerations to enhance urban-community mobility systems. Key factors for conscious mobility analysis have been derived from five case studies. The sample offers regional diversity (i.e., local, regional and the global urban contexts), as well as different goals in the transformation of conventional urban transport systems, from improving public transport efficiency and equipment electrification to mitigate pollution and climate risks, to focusing on equity, access and people safety. The case studies selected provide useful metrics on the adoption of cleaner, smarter, safer and more autonomous mobility technologies, along with novel people-centric program designs to build an initial set of conscious mobility indicators frameworks. The parameters were applied to the city of Monterrey, Nuevo Leon in Mexico focusing on the needs of the communities that work, study and live around the local urban campus of the Tecnologico de Monterrey’s Distrito Tec. This case study, served as an example of how conscious mobility indicators could be applied and customized to a community and region of interest. This paper introduces the first application of the conscious mobility framework for urban communities’ mobility system analysis. This more holistic assessment approach includes dimensions such as society and culture, infrastructure and urban spaces, technology, government, normativity, economy and politics, and the environment. The expectation is that the conscious mobility framework of analysis will become a useful tool for smarter and sustainable urban and mobility problem solving and decision making to enhance the quality of life all living in urban communities

Adjustable Scaling Parameters for State of Charge Estimation for Lithium-Ion Batteries Using Iterative Multiple UKFs

In this paper, one unscented Kalman filter with adjustable scaling parameters is proposed to estimate the state of charge (SOC) for lithium-ion batteries, as SOC is most important in monitoring the latter battery management system. After the equivalent circuit model is applied to describe the lithium-ion battery charging and discharging properties, a state space equation is constructed to regard SOC as its first state variable. Based on this state space model about SOC, one state estimation problem corresponding to the nonlinear system is established. In implementing the unscented Kalman filter, state estimation is influenced by the scaling parameter. Then, one criterion function is constructed to choose the scaling parameter adaptively by minimizing this criterion function. To extend one single unscented Kalman filter with adjustable scaling parameters to multiple module estimation, one improved unscented Kalman filter is advised based on iterative multiple models. Generally, the main contributions of this paper consist in two folds: one is to introduce a selection strategy for the scaling parameter adaptively, and the other is to combine iterative multiple models and a single unscented Kalman filter with adjustable scaling parameters. Finally, two simulation examples confirm that our unscented Kalman filter with adjustable scaling parameters and its improved iterative form are better than the classical Kalman filter; i.e., our obtained SOC estimation error converges to zero

Motorcycle tire rolling radius estimation for TPMS applications via GPS sensing

An accurate knowledge of the tire effective rolling radius is fundamental for many vehicle dynamics applications relying on an estimate of the vehicle speed, like ABS, stability and traction control. It may also serve as a tool to detect tire deflation and improve the safety on-board. Many algorithms for rolling radius estimation have been presented in the literature, but most of them cannot be applied on production vehicles, as they require too accurate measurements or a too high sampling rate. In this work, a signal processing procedure for on-line estimation of the rolling radius is presented, with a focus on motorcycles and pressure monitoring applications. The procedure assumes that a standard production Gps sensor is available and it is tested on a real production motorcycle

Parameter-Dependent H∞ Filter for LPV Semi-Active Suspension Systems

International audienc

Off-Road Motorbike Performance Analysis Using a Rear Semiactive EH Suspension

The topic of this paper is the analysis of a control system for a semiactive rear suspension in an off-road two-wheeled vehicle. Several control methods are studied, as well as the recently proposed Frequency Estimation Based (FEB) algorithm. The motorcycle dynamics, as well as the passive, and semiactive dampers, and the algorithm controlled shock absorber models are loaded into BikeSim, a professional two-wheeled vehicle simulation software, and tested in several road conditions. The results show a detailed comparison of the theoretical performance of the different control approaches in a novel environment for semiactive dampers

Analytical Design and Optimization of an Automotive Rubber Bushing

The ride comfort, driving safety, and handling of the vehicle should be designed and tuned to achieve the expectations defined in the company’s design. The ideal method of tuning the characteristics of the vehicle is to modify the bushings and mounts used in the chassis system. To deal with the noise, vibration and harshness on automobiles, elastomeric materials in mounts and bushings are determinant in the automotive components design, particularly those related to the suspension system. For most designs, stiffness is a key design parameter. Determination of stiffness is often necessary in order to ensure that excessive forces or deflections do not occur. Many companies use trial and error method to meet the requirements of stiffness curves. Optimization algorithms are an effective solution to this type of design problems. This paper presents a simulation-based methodology to design an automotive bushing with specific characteristic curves. Using an optimum design formulation, a mathematical model is proposed to design and then optimize structural parameters using a genetic algorithm. To validate the resulting data, a finite element analysis (FEA) is carried out with the optimized values. At the end, results between optimization, FEA, and characteristic curves are compared and discussed to establish the correlation among them

Survey on Video-Based Biomechanics and Biometry Tools for Fracture and Injury Assessment in Sports

This work presents a survey literature review on biomechanics, specifically aimed at the study of existent biomechanical tools through video analysis, in order to identify opportunities for researchers in the field, and discuss future proposals and perspectives. Scientific literature (journal papers and conference proceedings) in the field of video-based biomechanics published after 2010 were selected and discussed. The most common application of the study of biomechanics using this technique is sports, where the most reported applications are american football, soccer, basketball, baseball, jumping, among others. These techniques have also been studied in a less proportion, in ergonomy, and injury prevention. From the revised literature, it is clear that biomechanics studies mainly focus on the analysis of angles, speed or acceleration, however, not many studies explore the dynamical forces in the joints. The development of video-based biomechanic tools for force analysis could provide methods for assessment and prediction of biomechanical force associated risks such as injuries and fractures. Therefore, it is convenient to start exploring this field. A few case studies are reported, where force estimation is performed via manual tracking in different scenarios. This demonstration is carried out using conventional manual tracking, however, the inclusion of similar methods in an automated manner could help in the development of intelligent healthcare, force prediction tools for athletes and/or elderly population. Future trends and challenges in this field are also discussed, where data availability and artificial intelligence models will be key to proposing new and more reliable methods for biomechanical analysis

Survey on Video-Based Biomechanics and Biometry Tools for Fracture and Injury Assessment in Sports

This work presents a survey literature review on biomechanics, specifically aimed at the study of existent biomechanical tools through video analysis, in order to identify opportunities for researchers in the field, and discuss future proposals and perspectives. Scientific literature (journal papers and conference proceedings) in the field of video-based biomechanics published after 2010 were selected and discussed. The most common application of the study of biomechanics using this technique is sports, where the most reported applications are american football, soccer, basketball, baseball, jumping, among others. These techniques have also been studied in a less proportion, in ergonomy, and injury prevention. From the revised literature, it is clear that biomechanics studies mainly focus on the analysis of angles, speed or acceleration, however, not many studies explore the dynamical forces in the joints. The development of video-based biomechanic tools for force analysis could provide methods for assessment and prediction of biomechanical force associated risks such as injuries and fractures. Therefore, it is convenient to start exploring this field. A few case studies are reported, where force estimation is performed via manual tracking in different scenarios. This demonstration is carried out using conventional manual tracking, however, the inclusion of similar methods in an automated manner could help in the development of intelligent healthcare, force prediction tools for athletes and/or elderly population. Future trends and challenges in this field are also discussed, where data availability and artificial intelligence models will be key to proposing new and more reliable methods for biomechanical analysis

Campus City Project: Challenge Living Lab for Smart Cities

This work presents the Campus City initiative followed by the Challenge Living Lab platform to promote research, innovation, and entrepreneurship with the intention to create urban infrastructure and creative talent (human resources) that solves different community, industrial and government Pain Points within a Smart City ecosystem. The main contribution of this work is to present a working model and the open innovation ecosystem used in Tecnologico de Monterrey that could be used as both, a learning mechanism as well as a base model for scaling it up into a Smart Campus and Smart City. Moreover, this work presents the Smart Energy challenge as an example of a pedagogic opportunity for the development of competencies. This included the pedagogic design of the challenge, the methodology followed by the students and the results. Finally, a discussion on the findings and learnings of the model and challenge implementation. Results showed that Campus City initiative and the Challenge Living Lab allows the identification of highly relevant and meaningful challenges while providing a pedagogic framework in which students are highly motivated, engaged, and prepared to tackle different problems that involve government, community, industry, and academia

Design and Implementation of an IoT-Oriented Strain Smart Sensor with Exploratory Capabilities on Energy Harvesting and Magnetorheological Elastomer Transducers

This work designed and implemented a new low-cost, Internet of Things-oriented, wireless smart sensor prototype to measure mechanical strain. The research effort explores the use of smart materials as transducers, e.g., a magnetorheological elastomer as an electrical-resistance sensor, and a cantilever beam with piezoelectric sensors to harvest energy from vibrations. The study includes subsequent and validated results with a magnetorheological elastomer transducer that contained multiwall carbon nanotubes with iron particles, generated voltage tests from an energy-harvesting system that functions with an array of piezoelectric sensors embedded in a rubber-based cantilever beam, wireless communication to send data from the sensor’s central processing unit towards a website that displays and stores the handled data, and an integrated manufactured prototype. Experiments showed that electrical-resistivity variation versus measured strain, and the voltage-generation capability from vibrations have the potential to be employed in smart sensors that could be integrated into commercial solutions to measure strain in automotive and aircraft systems, and civil structures. The reported experiments included cloud-computing capabilities towards a potential Internet of Things application of the smart sensor in the context of monitoring automotive-chassis vibrations and airfoil damage for further analysis and diagnostics, and in general structural-health-monitoring applications