Simultaneous Suspension Control and Energy Harvesting through Novel Design and Control of a New Nonlinear Energy Harvesting Shock Absorber

Simultaneous vibration control and energy harvesting of vehicle suspensions have attracted significant research attention over the past decades. However, existing energy harvesting shock absorbers (EHSAs) are mainly designed based on the principle of linear resonance, thereby compromising suspension performance for high-efficiency energy harvesting and being only responsive to narrow bandwidth vibrations. In this paper, we propose a new EHSA design -- inerter pendulum vibration absorber (IPVA) -- that integrates an electromagnetic rotary EHSA with a nonlinear pendulum vibration absorber. We show that this design simultaneously improves ride comfort and energy harvesting efficiency by exploiting the nonlinear effects of pendulum inertia. To further improve the performance, we develop a novel stochastic linearization model predictive control (SL-MPC) approach in which we employ stochastic linearization to approximate the nonlinear dynamics of EHSA that has superior accuracy compared to standard linearization. In particular, we develop a new stochastic linearization method with guaranteed stabilizability, which is a prerequisite for control designs. This leads to an MPC problem that is much more computationally efficient than the nonlinear MPC counterpart with no major performance degradation. Extensive simulations are performed to show the superiority of the proposed new nonlinear EHSA and to demonstrate the efficacy of the proposed SL-MPC

Simultaneous observation of hybrid states for cyber-physical systems: a case study of electric vehicle powertrain

As a typical cyber-physical system (CPS), electrified vehicle becomes a hot research topic due to its high efficiency and low emissions. In order to develop advanced electric powertrains, accurate estimations of the unmeasurable hybrid states, including discrete backlash nonlinearity and continuous half-shaft torque, are of great importance. In this paper, a novel estimation algorithm for simultaneously identifying the backlash position and half-shaft torque of an electric powertrain is proposed using a hybrid system approach. System models, including the electric powertrain and vehicle dynamics models, are established considering the drivetrain backlash and flexibility, and also calibrated and validated using vehicle road testing data. Based on the developed system models, the powertrain behavior is represented using hybrid automata according to the piecewise affine property of the backlash dynamics. A hybrid-state observer, which is comprised of a discrete-state observer and a continuous-state observer, is designed for the simultaneous estimation of the backlash position and half-shaft torque. In order to guarantee the stability and reachability, the convergence property of the proposed observer is investigated. The proposed observer are validated under highly dynamical transitions of vehicle states. The validation results demonstrates the feasibility and effectiveness of the proposed hybrid-state observer

Road profile estimation for suspension system based on the minimum model error criterion combined with a Kalman filter

This paper presents a novel approach for improving the estimation accuracy of the road profile for a vehicle suspension system. To meet the requirements of road profile estimation for road management and reproduction of system excitation, previous studies can be divided into data-driven and model based approaches. These studies mainly focused on road profile estimation while seldom considering the uncertainty of parameters. However, uncertainty is unavoidable for various aspects of suspension system, e.g., varying sprung mass, damper and tire nonlinear performance. In this study, to improve the estimation accuracy for a varying sprung mass, a novel algorithm was derived based on the Minimum Model Error (MME) criterion and a Kalman Filter (KF). Since the MME criterion method utilizes the minimum value principle to solve the model error based on a model error function, the MME criterion can effectively deal with the estimation error. Then, the proposed algorithm was applied to a 2 degree-of-freedom (DOF) suspension system model under ISO Level-B, ISO Level-C and ISO Level-D road excitations. Simulation results and experimental data obtained using a quarter-vehicle test rig revealed that the proposed approach achieves higher road estimation accuracy compared to traditional KF methods

Socio-Cognitive and Affective Computing

Social cognition focuses on how people process, store, and apply information about other people and social situations. It focuses on the role that cognitive processes play in social interactions. On the other hand, the term cognitive computing is generally used to refer to new hardware and/or software that mimics the functioning of the human brain and helps to improve human decision-making. In this sense, it is a type of computing with the goal of discovering more accurate models of how the human brain/mind senses, reasons, and responds to stimuli. Socio-Cognitive Computing should be understood as a set of theoretical interdisciplinary frameworks, methodologies, methods and hardware/software tools to model how the human brain mediates social interactions. In addition, Affective Computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects, a fundamental aspect of socio-cognitive neuroscience. It is an interdisciplinary field spanning computer science, electrical engineering, psychology, and cognitive science. Physiological Computing is a category of technology in which electrophysiological data recorded directly from human activity are used to interface with a computing device. This technology becomes even more relevant when computing can be integrated pervasively in everyday life environments. Thus, Socio-Cognitive and Affective Computing systems should be able to adapt their behavior according to the Physiological Computing paradigm. This book integrates proposals from researchers who use signals from the brain and/or body to infer people's intentions and psychological state in smart computing systems. The design of this kind of systems combines knowledge and methods of ubiquitous and pervasive computing, as well as physiological data measurement and processing, with those of socio-cognitive and affective computing

Optimising route comfort indices for neonatal transfers by road

The risk of severe brain injuries in sick premature infants increases when transferred between hospitals. Causality is uncertain, but stress levels are elevated during ambulance journeys; potentially due to excessive levels of noise and vibration. It has been proposed that reducing these levels would reduce the risk, with one prospective method being comfort-optimised navigation. An Android app was developed that logs noise level, Inertial Measurement Unit (IMU) and location data during journeys, sampling at the fastest rates possible depending on the hardware and firmware. The smartphone used during development was found to sample noise levels accurate to 0.3 dB up to 80 dB(A) and accelerations accurate to 10\% up to 40~Hz, although considerable jitter was present in the IMU sampling. Recorded data were shown to be repeatable for multiple passes over the same stretch of road (acceleration interquartile range (IQR): 0.14ms^{-2}; noise IQR: 2.8 dB). Data were influenced by both supplementary audio and the smartphone model so an initial idea of gathering data through public engagement was determined unsuitable. Controlled collection of data was planned, utilising the neonatal ambulances operated by CenTre Neonatal Transport (CenTre). A new smartphone model was identified that was capable of sampling accelerations at a sufficient rate to comply with the "Evaluation of human exposure to whole-body vibration" standard, ISO 2631. This model also had greater processing power than the previous model used during initial testing, resulting in reduced jitter, and was found to provide more accurate accelerations (within 5% up to 55 Hz). Logging of periods before and after each journey was added along with meta-data describing each journey. Journeys performed by CenTre were recorded over the course of 12 months. Recorded variables were supplemented by calculation of ISO-weighted vibration parameters. The final dataset comprises 1,487 journeys over 81,901 km and 1,318 hours. Strong similarities between meta-data and officially reported transport data suggested there was no bias in the journeys that the staff recorded. Roads driven between Nottingham City Hospital (NCH) and Leicester Royal Infirmary (LRI) were chosen as a case study. Data from 588 journeys contributed towards the analysis. A range of metrics, derived from previous studies and adult standards, were used to assess the roads of the NCH to LRI network. Both speed and road classification were found to influence vibration and noise level, however the influence could not be separated due to the inherent link between both parameters. All routes involved either use of motorway or a concrete A-road, with the latter producing worse vibration. Although individual road sections varied, differences were reduced between the routes. Assessments were also performed of the metrics at each of the 42 hospitals (36 departing; 38 arriving) present in the data. Results were similar between hospitals, but differed between loading and unloading phases. High magnitude shocks were more abundant during the loading phases, whereas low impact vibrations were more frequent during unloading. Both phases registered greater shocks than those found during journeys. In summary, this work provides a low-cost method of obtaining large amounts of data describing the ambulance environment without requiring any technical knowledge to operate. The theory that the physical environment could be altered through routing has also been confirmed. The data collected during this work could be utilised in the future to aid determination of neonatal responses and subsequently establish optimal routes

Optimising route comfort indices for neonatal transfers by road

The risk of severe brain injuries in sick premature infants increases when transferred between hospitals. Causality is uncertain, but stress levels are elevated during ambulance journeys; potentially due to excessive levels of noise and vibration. It has been proposed that reducing these levels would reduce the risk, with one prospective method being comfort-optimised navigation. An Android app was developed that logs noise level, Inertial Measurement Unit (IMU) and location data during journeys, sampling at the fastest rates possible depending on the hardware and firmware. The smartphone used during development was found to sample noise levels accurate to 0.3 dB up to 80 dB(A) and accelerations accurate to 10\% up to 40~Hz, although considerable jitter was present in the IMU sampling. Recorded data were shown to be repeatable for multiple passes over the same stretch of road (acceleration interquartile range (IQR): 0.14ms^{-2}; noise IQR: 2.8 dB). Data were influenced by both supplementary audio and the smartphone model so an initial idea of gathering data through public engagement was determined unsuitable. Controlled collection of data was planned, utilising the neonatal ambulances operated by CenTre Neonatal Transport (CenTre). A new smartphone model was identified that was capable of sampling accelerations at a sufficient rate to comply with the "Evaluation of human exposure to whole-body vibration" standard, ISO 2631. This model also had greater processing power than the previous model used during initial testing, resulting in reduced jitter, and was found to provide more accurate accelerations (within 5% up to 55 Hz). Logging of periods before and after each journey was added along with meta-data describing each journey. Journeys performed by CenTre were recorded over the course of 12 months. Recorded variables were supplemented by calculation of ISO-weighted vibration parameters. The final dataset comprises 1,487 journeys over 81,901 km and 1,318 hours. Strong similarities between meta-data and officially reported transport data suggested there was no bias in the journeys that the staff recorded. Roads driven between Nottingham City Hospital (NCH) and Leicester Royal Infirmary (LRI) were chosen as a case study. Data from 588 journeys contributed towards the analysis. A range of metrics, derived from previous studies and adult standards, were used to assess the roads of the NCH to LRI network. Both speed and road classification were found to influence vibration and noise level, however the influence could not be separated due to the inherent link between both parameters. All routes involved either use of motorway or a concrete A-road, with the latter producing worse vibration. Although individual road sections varied, differences were reduced between the routes. Assessments were also performed of the metrics at each of the 42 hospitals (36 departing; 38 arriving) present in the data. Results were similar between hospitals, but differed between loading and unloading phases. High magnitude shocks were more abundant during the loading phases, whereas low impact vibrations were more frequent during unloading. Both phases registered greater shocks than those found during journeys. In summary, this work provides a low-cost method of obtaining large amounts of data describing the ambulance environment without requiring any technical knowledge to operate. The theory that the physical environment could be altered through routing has also been confirmed. The data collected during this work could be utilised in the future to aid determination of neonatal responses and subsequently establish optimal routes