An Automatic Tuning MPC with Application to Ecological Cruise Control

Model predictive control (MPC) is a powerful tool for planning and controlling dynamical systems due to its capacity for handling constraints and taking advantage of preview information. Nevertheless, MPC performance is highly dependent on the choice of cost function tuning parameters. In this work, we demonstrate an approach for online automatic tuning of an MPC controller with an example application to an ecological cruise control system that saves fuel by using a preview of road grade. We solve the global fuel consumption minimization problem offline using dynamic programming and find the corresponding MPC cost function by solving the inverse optimization problem. A neural network fitted to these offline results is used to generate the desired MPC cost function weight during online operation. The effectiveness of the proposed approach is verified in simulation for different road geometries

Lifetime cost analysis of concrete barriers and steel guardrails.

This study investigates the lifetime costs associated with concrete barriers and steel guardrails. We introduce a cost analysis methodology that incorporates critical factors such as construction costs, maintenance costs, exposure risks during maintenance activities, and the costs imposed to traveling public through the increased traffic and the crash outcomes. We integrate various parameters including economic factors, road geometry, general weather condition, and traffic mix to estimate a location-dependent cost for each type of barrier accurately. A software tool, named CalBarrier, was developed during this study to carry out the calculations and the comparison of lifetime cost of aforementioned barriers. An inherent strength of this research is its reliance on recent real data extracted from various databases of California Department of Transportation (Caltrans), ensuring precision and relevance in accounting for various influential factors. Drawing insights from Caltrans practices and interviews with their personnel, this study emphasizes the intricate decision-making process involved in mitigating safety risks and reducing operational expenses. Although our data originates from California, the methodology for life cycle cost analysis, and our software are applicable for regions with different socio-economic conditions by deploying user input costs, making our findings a valuable resource for other areas facing comparable challenges

Stature estimation and formulation of based on ulna length in Kurdish racial subgroup

Measuring stature is useful for forensic and anthropometrical sciences. The present study was conducted to calculate the stature from ulna length among Kurdish racial subgroup living in Iran. In this study, 50 females aged 19-24 were recruited. The ulna length of subjects was taken independently on left and right sides using a digital sliding caliper. The height was measured between vertex and floor. The height (Y) was also estimated by linear regression formulas from the length of right (X1) or left side ulna (X2). For right side, Y1 = 59.48 + 4.005 X1 ± 4.09295 (R=0.753); for left side, Y2 = 63.44 +3.887 X2 ± 4.24106 (R=0.731). The derived formulae are population specific and are designed for use in forensic and anthropometric skeletal analysis of Kurdish racial subgroup. These data provide a scientific basis for further investigations on racial subgroups living in Iran

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

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

Control of Micro-Hybrid Boosting

Powertrain hybridization has been shown to greatly improve vehicle fuel economy, with significant additional component costs. A novel type of low voltage hybrid device, called a power split supercharger (PSS), has the potential to provide hybrid functionality at reduced system cost. The PSS is configured with a supercharger, a planetary gear set and a motor. The electric motor in the PSS system can be used in two discrete modes. It can drive the supercharger at variable speed and provide flexible boost pressure to the engine, or it can be employed to directly supply/receive torque to/from the crankshaft like a typical parallel hybrid powertrain. This work presents modeling, control design, optimization, and analysis of the PSS in fuel economy improvement of a light duty vehicle. The low-level controller for the air charge management of a twincharged engine with a PSS has to coordinated three actuators, throttle, wastegate, and the PSS in the nonlinear air path of the engine in a fraction of second to ensure fast engine torque control. A decentralized controller that uses the throttle to control intake manifold pressure and employs both the PSS and wastegate to control the boost pressure, in a master-slave configuration, is adopted. The controller was validated on a high fidelity GT-Power engine model and shown to effectively reduce the response time of the engine during critical transients to less than 0.5 second. During large torque requests, the supervisory energy management system in a vehicle equipped with a PSS must decide whether to use the electric motor to drive the supercharger or supply the motor torque directly to the crankshaft. An optimal control problem for energy management of the PSS was formulated and solved over the standard EPA drive cycles using dynamic programming (DP). The DP solution provides the best selection of operating mode as well as the potential fuel economy benefit of the system. The results show that the optimal controller often selects the parallel hybrid mode over the supercharging mode to minimize the fuel consumption of the vehicle. Moreover, the PSS provides 75% of full hybridization benefit and is as efficient as a two motor solution. An online energy management system that minimizes the equivalent fuel consumption of the engine and motor at each time instant was also developed and shown to have good agreement with the DP results. The simulation time trajectories were supplied to an engine dynamometer experimental setup, which demonstrated that the PSS and EGR combined improve the vehicle fuel economy by 35.5% over the FTP75 cycle compared to a baseline turbocharged engine. The fuel economy benefit of the PSS in combination with automation was also studied in this thesis. Automated vehicles can use a preview of the ahead traffic and plan their trajectory to minimize fuel consumption and maximize the benefit of a small hybrid system like the PSS. The fuel consumption minimization problem in a car following scenario for a vehicle equipped with a PSS was formulated and solved by sequential optimization of the velocity profile and energy management system. It was shown that with velocity smoothing a small hybrid system like the PSS can provide the fuel economy of a full hybrid powertrain at a lower cost.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/151702/1/snazari_1.pd

Multimodal user interaction with in-car equipment in real conditions based on touch and speech modes in the Persian language

Nowadays, communication with in-car equipment is performed via a large number of buttons or a touch screen. This increases the need for driver’s visual attention and leads to reduce the concentration of drivers while driving. Speech-based interaction has been introduced in recent years as a way to reduce driver distractions. This input mode faces several technical challenges such as the need to memorize voice commands and the difficulties of canceling them. This paper focuses on presenting a multimodal user interface design based on touch and speech modes, for controlling five in-car devices (radio, CD player or music player, fan, heater, and driver-side window). The research is designed to collect a dataset of in-car voice commands in the Persian language in real conditions (in a real car and in the presence of background noises) to firstly create a dataset of Persian voice commands (due to lack of research in this area in Persian speaking countries) and secondly intending to solve the mentioned challenges. To evaluate the proposed user interface, 15 participants performed ten different tasks based on the speech and touch modes, with and without driving simulation. The evaluation results indicated that the speech input mode with and without driving simulation has had in average smaller number of clicks for performing tasks (0.2 and 0.6), smaller task completion time (7.37 and 3.3 seconds), smaller time intervals between clicks (8.2 and 5 seconds) and smaller driver’s distraction rate (25.08%) in comparison to the touch input mode, respectively. Moreover, using two different input modes in designing the in vehicle user interface leads to increased accessibility

Control and control-oriented modeling of PEM water electrolyzers: A review

As the most abundant element in the universe, hydrogen is a promising energy carrier for decarbonizing various economic sectors. Green hydrogen production from water electrolysis is critical to the success of this path with polymer electrolyte membrane (PEM) water electrolyzer (WE) as a key technology due to its quick dynamic response and high energy efficiency. Nevertheless, vigorous control algorithms are necessary to maximize the performance, efficiency, and useable lifetime of PEM WEs. This review attempts to collate the modeling frameworks relevant to controller design and provides a survey of various control techniques used in literature to overcome the challenges associated with the transient operation of PEM WEs. To better understand the underlying physics and the coupling between different subsystems, we first review control-oriented electrochemical, thermal, mass transport, and equivalent circuit models. We identify manipulable system variables and control knobs that can be employed for a better system operation in the next step, and finally, we discuss different controllers used in literature, including traditional control approaches, optimal control methods, and other advanced techniques such as nonlinear and neural network controllers

The effects of self-care education based on the family-centered empowerment model on functional independence and life satisfaction among community-dwelling older adults: A randomized controlled trial

Background: Self-care is an indicator of power and independence among older adults. Family can increase older adults’ motivation and desire for engagement in self-care activities. This study aimed to determine the effects of self-care education based on the family-centered empowerment model (FCEM) on functional independence and life satisfaction among community-dwelling older adults. Methods: This randomized clinical trial was conducted on 126 community-dwelling older adults   from local sociocultural centers in 2021. They purposively recruited and randomly allocated to control (n = 63) and intervention (n = 63) groups. The intervention group received FCEM-based self-care education in six 1.5-hour weekly sessions. The education focused on the physical, psychoemotional, social, and spiritual aspects of self-care and was based on the 4 steps of FCEM: perceived threat, problem-solving, educational participation, and evaluation. Katz Index of Independence in Activities of Daily Living (Katz ADL Index) and Zest Life Satisfaction Index were respectively used for independence and satisfaction assessments before and 8 weeks after the study intervention. The data were analyzed using SPSS version 16 and through the Kolmogorov-Smirnov, Mann-Whitney U, Wilcoxon, and chi-square tests. Results: The mean age was 67.57 ± 4.62 years in the intervention group and 67.08 ± 4.62 years in the control group. There were no significant differences between the intervention and control groups respecting the pretest mean scores of life satisfaction (16.54 ± 4.46 vs 16.68 ± 4.23; P = 0.963) and functional independence (4.78 ± 1.15 vs 5.11 ± 1.00; P = 0.107). The posttest mean score of functional independence was also insignificant (5.52 ± 0.692 vs 5.24 ± 0.911; P = 0.92) between the 2 groups. However, the Mann-Whitney U test showed that the posttest mean score of life satisfaction was significantly greater in the intervention group than in the control group (18.95 ± 4.36 vs 16.13 ± 4.41; P = 0.001). Conclusion: FCEM-based self-care education effectively improves life satisfaction among community-dwelling older adults