An improved data classification framework based on fractional particle swarm optimization

Particle Swarm Optimization (PSO) is a population based stochastic optimization technique which consist of particles that move collectively in iterations to search for the most optimum solutions. However, conventional PSO is prone to lack of convergence and even stagnation in complex high dimensional-search problems with multiple local optima. Therefore, this research proposed an improved Mutually-Optimized Fractional PSO (MOFPSO) algorithm based on fractional derivatives and small step lengths to ensure convergence to global optima by supplying a fine balance between exploration and exploitation. The proposed algorithm is tested and verified for optimization performance comparison on ten benchmark functions against six existing established algorithms in terms of Mean of Error and Standard Deviation values. The proposed MOFPSO algorithm demonstrated lowest Mean of Error values during the optimization on all benchmark functions through all 30 runs (Ackley = 0.2, Rosenbrock = 0.2, Bohachevsky = 9.36E-06, Easom = -0.95, Griewank = 0.01, Rastrigin = 2.5E-03, Schaffer = 1.31E-06, Schwefel 1.2 = 3.2E-05, Sphere = 8.36E-03, Step = 0). Furthermore, the proposed MOFPSO algorithm is hybridized with Back-Propagation (BP), Elman Recurrent Neural Networks (RNN) and Levenberg-Marquardt (LM) Artificial Neural Networks (ANNs) to propose an enhanced data classification framework, especially for data classification applications. The proposed classification framework is then evaluated for classification accuracy, computational time and Mean Squared Error on five benchmark datasets against seven existing techniques. It can be concluded from the simulation results that the proposed MOFPSO-ERNN classification algorithm demonstrated good classification performance in terms of classification accuracy (Breast Cancer = 99.01%, EEG = 99.99%, PIMA Indian Diabetes = 99.37%, Iris = 99.6%, Thyroid = 99.88%) as compared to the existing hybrid classification techniques. Hence, the proposed technique can be employed to improve the overall classification accuracy and reduce the computational time in data classification applications

Energy Regeneration and Environment Sensing for Robotic Leg Prostheses and Exoskeletons

Robotic leg prostheses and exoskeletons can provide powered locomotor assistance to older adults and/or persons with physical disabilities. However, limitations in automated control and energy-efficient actuation have impeded their transition from research laboratories to real-world environments. With regards to control, the current automated locomotion mode recognition systems being developed rely on mechanical, inertial, and/or neuromuscular sensors, which inherently have limited prediction horizons (i.e., analogous to walking blindfolded). Inspired by the human vision-locomotor control system, here a multi-generation environment sensing and classification system powered by computer vision and deep learning was developed to predict the oncoming walking environments prior to physical interaction, therein allowing for more accurate and robust high-level control decisions. To support this initiative, the “ExoNet” database was developed – the largest and most diverse open-source dataset of wearable camera images of indoor and outdoor real-world walking environments, which were annotated using a novel hierarchical labelling architecture. Over a dozen state-of-the-art deep convolutional neural networks were trained and tested on ExoNet for large-scale image classification and automatic feature engineering. The benchmarked CNN architectures and their environment classification predictions were then quantitatively evaluated and compared using an operational metric called “NetScore”, which balances the classification accuracy with the architectural and computational complexities (i.e., important for onboard real-time inference with mobile computing devices). Of the benchmarked CNN architectures, the EfficientNetB0 network achieved the highest test accuracy; VGG16 the fastest inference time; and MobileNetV2 the best NetScore. These comparative results can inform the optimal architecture design or selection depending on the desired performance of an environment classification system. With regards to energetics, backdriveable actuators with energy regeneration can improve the energy efficiency and extend the battery-powered operating durations by converting some of the otherwise dissipated energy during negative mechanical work into electrical energy. However, the evaluation and control of these regenerative actuators has focused on steady-state level-ground walking. To encompass real-world community mobility more broadly, here an energy regeneration system, featuring mathematical and computational models of human and wearable robotic systems, was developed to simulate energy regeneration and storage during other locomotor activities of daily living, specifically stand-to-sit movements. Parameter identification and inverse dynamic simulations of subject-specific optimized biomechanical models were used to calculate the negative joint mechanical work and power while sitting down (i.e., the mechanical energy theoretically available for electrical energy regeneration). These joint mechanical energetics were then used to simulate a robotic exoskeleton being backdriven and regenerating energy. An empirical characterization of an exoskeleton was carried out using a joint dynamometer system and an electromechanical motor model to calculate the actuator efficiency and to simulate energy regeneration and storage with the exoskeleton parameters. The performance calculations showed that regenerating electrical energy during stand-to-sit movements provide small improvements in energy efficiency and battery-powered operating durations. In summary, this research involved the development and evaluation of environment classification and energy regeneration systems to improve the automated control and energy-efficient actuation of next-generation robotic leg prostheses and exoskeletons for real-world locomotor assistance

Toward Holistic Energy Management Strategies for Fuel Cell Hybrid Electric Vehicles in Heavy-Duty Applications

The increasing need to slow down climate change for environmental protection demands further advancements toward regenerative energy and sustainable mobility. While individual mobility applications are assumed to be satisfied with improving battery electric vehicles (BEVs), the growing sector of freight transport and heavy-duty applications requires alternative solutions to meet the requirements of long ranges and high payloads. Fuel cell hybrid electric vehicles (FCHEVs) emerge as a capable technology for high-energy applications. This technology comprises a fuel cell system (FCS) for energy supply combined with buffering energy storages, such as batteries or ultracapacitors. In this article, recent successful developments regarding FCHEVs in various heavy-duty applications are presented. Subsequently, an overview of the FCHEV drivetrain, its main components, and different topologies with an emphasis on heavy-duty trucks is given. In order to enable system layout optimization and energy management strategy (EMS) design, functionality and modeling approaches for the FCS, battery, ultracapacitor, and further relevant subsystems are briefly described. Afterward, common methodologies for EMS are structured, presenting a new taxonomy for dynamic optimization-based EMS from a control engineering perspective. Finally, the findings lead to a guideline toward holistic EMS, encouraging the co-optimization of system design, and EMS development for FCHEVs. For the EMS, we propose a layered model predictive control (MPC) approach, which takes velocity planning, the mitigation of degradation effects, and the auxiliaries into account simultaneously

Load-Sharing Management for Fuel Cell Hybrid Electric Vehicle (FCHEV) Based on Intelligent Controllers and Optimization Algorithms

This study proposes an intelligent controller for a hydrogen-powered plug-in fuel cell hybrid electric vehicle (FCHEV) that integrates a fuel cell (FC) with two energy storage systems, which are ultracapacitor (UC) and battery (BAT), which results in a high dynamic response along with maintaining efficient use of resources for energy storage. Moreover, this controller works on effectively managing the system power flow to reduce the amount of power needed for the FCHEV. An effective energy management system (EMS) has been developed that utilizes the fuzzy logic controller (FLC) and artificial neural networks (ANNs) to achieve the EMS requirements. Also, the proposed system operates these three power sources at high efficiency with their mechanism performance, meets load power demands efficiently, and uses less hydrogen. Furthermore, the Crow Search Algorithm (CSA) and Particle Swarm Optimization (PSO) methods are utilized to adjust the parameters of the wavelet neural network that is connected to the PI controller, called WNN-PI. The DC-DC converters control the output voltage of the FC and BAT for maintaining the DC-bus voltage constant at 300 volts. The state-of-charge (SOC) for the BAT and UC is also considered in this study. The proposed system is analyzed and evaluated using the MATLAB/Simulink environment, and two vehicle driving cycles were implemented using the ADVISOR Simulator

Challenges of micro/mild hybridisation for construction machinery and applicability in UK

In recent years, micro/mild hybridisation (MMH) is known as a feasible solution for powertrain development with high fuel efficiency, less energy use and emission and, especially, low cost and simple installation. This paper focuses on the challenges of MMH for construction machines and then, pays attention to its applicability to UK construction machinery. First, hybrid electric configurations are briefly reviewed; and technological challenges towards MMH in construction sector are clearly stated. Second, the current development of construction machinery in UK is analysed to point out the potential for MMH implementation. Thousands of machines manufactured in UK have been sampled for the further study. Third, a methodology for big data capturing, compression and mining is provided for a capable of managing and analysing effectively performances of various construction machine types. By using this method, 96% of data memory can be reduced to store the huge machine data without lacking the necessary information. Forth, an advanced decision tool is built using a fuzzy cognitive map based on the big data mining and knowledge from experts to enables users to define a target machine for MMH utilization. The numerical study with this tool on the sampled machines has been done and finally realized that one class of heavy excavators is the most suitable to apply MMH technology

Design of Energy Management Strategies for a Battery-Ultracapacitor Electric Vehicle

The battery pack is the most expensive component in electric vehicles. Electric vehicles are prone to accelerated battery degradation due to the high charging/discharging cycles and high peak power demand. One solution to this issue would be increasing the battery capacity to meet the high energy requests. However, increasing the battery size is not reasonable due to the high cost and volume. An alternative solution is integrating other energy storage systems into the vehicle powertrain. The additional energy storage system highlights an energy management strategy to distribute the power among onboard energy storage systems effectively. Energy management systems incorporate different strategies classified based on their computational time, implementability in real-time, and measurable performance to be optimized. This thesis considers the case study of Chevy Spark model year 2015 with a hybrid energy storage system including battery and ultracapacitor. First, an overview of diffrent energy storage systems is presented, followed by a review of different hybrid energy storage' configurations. Second, energy management strategies are categorized into three main classifications: rule-based, optimization-based, and data-based algorithms. Third, the selected vehicle model with an embedded rule-based energy management strategy is developed in MATLAB Simulink, and battery performance is validated against available real-world data. Optimal power distribution among battery and ultracapacitor is achieved through an offline global optimal algorithm in chapter 5 in a way to improve battery life. Finally, optimal results are used as a training dataset for an online data-based energy management strategy. Results prove the strategy's effectiveness by improving battery life by an average of 16% compared to the rule-based and 12% difference from the globally optimal strategy on various driving conditions. The proposed energy management strategy provides near-optimal performance while it is real-time implementable and does not need to have beforehand knowledge of driving cycles