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Energy consumption in commercial buildings is significantly affected by the performance of heating, ventilation, and air-conditioning (HVAC) systems, which are traditionally operated using centralized controllers. HVAC control requires adjusting multiple setpoints such as chilled water temperatures and supply air temperature (SAT). Supervisory control framework in a distributed setting enables optimal HVAC operation and provides scalable solutions for optimizing energy across several scales from homes to regional areas. This paper proposes a distributed optimization framework for achieving energy efficiency in large-scale building energy systems. It is highly desirable to have building management systems that are scalable, robust, flexible, and are low cost. For addressing the scalability and flexibility, a modular problem formulation is established that decouples the distributed optimization level from local thermal zone modeling level. We leverage a recently developed generalized gossip algorithm for robust distributed optimization. The supervisory controller aims at minimizing the energy input considering occupant comfort. For validating the proposed scheme, a numerical case study based on a physical testbed in the Iowa Energy Center is presented. We show that the distributed optimization methodology outperforms the typical baseline strategy, which is a rule-based controller to set a constant supply air temperature. This paper also incorporates a software architecture based on the VOLTTRON platform, developed by the Pacific Northwest National Laboratory (PNNL), for practical implementation of the proposed framework via the BACnet system. The experimental results show that the supervisory control framework proposed in this paper can save energy by approximately 11%. [ABSTRACT FROM AUTHOR]

Hybrid electric vehicles (HEV) offer improved fuel efficiency compared to their conventional counterparts at the expense of adding complexity and at times, reduced total power. As a result, HEV generally lack the dynamic performance that customers enjoy. To address this issue, the paper presents a HEV with electric all-wheel drive (eAWD) capabilities via the use of a torque vectoring electric rear axle drive (TVeRAD) unit to power the rear axle. The addition of TVeRAD to a front wheel drive HEV improves the total power output. To further improve the handling characteristics of the vehicle, the TVeRAD unit allows for wheel torque vectoring (TV) at the rear axle. A bond graph model of the proposed drivetrain model is developed and used in cosimulation with carsim. The paper proposes a control system, which utilizes slip ratio optimization to allocate control to each tire. The optimization algorithm is used to obtain optimal slip ratio targets to at each tire such that the targets avoid tire saturation. The Youla parameterization technique is used to develop robust tracking controllers for each axle. The proposed control system is ultimately tested on the drivetrain model with a high fidelity carsim vehicle model for validation. Simulation results show that the control system is able to maximize vehicle longitudinal performance while avoiding tire saturation on a low µ surface. More importantly, the control system is able to track the desired yaw moment request on a high-speed double-lane change (DLC) maneuver through the use of the TVeRAD to improve the handling characteristic of the vehicle. [ABSTRACT FROM AUTHOR]

Redundancy is a useful feature in dynamic systems which can be exploited to enhance performance in various tasks. In this work, redundancy will be utilized to minimize the energy consumption of a linear manipulator, while in some cases an additional task of end-effector tracking will also be required and achieved. Optimal control theory has been extensively used for the optimization of dynamic systems; however, complex tasks and redundancy make these problems computationally expensive, numerically difficult to solve, and in many cases, ill-defined. In this paper, evolutionary bilevel optimization for the problem is presented. This is done by setting up an upper level optimization problem for a set of decision variables and a lower level one that actually calculates the optimal inputs and trajectories. The upper level problem is solved by a genetic algorithm (GA), whereas the lower level problem uses classical optimal control. As a result, the proposed algorithm allows the optimization of complex tasks that usually cannot be solved in practice using standard optimal control tools. In addition, despite the use of penalty functions to enforce saturation constraints, the algorithm leads to global energy minimization. Illustrative examples of a redundant x-y robotic manipulator with complex overall tasks will be presented, solved, and discussed. [ABSTRACT FROM AUTHOR]

The need for less fuel consumption and the trend of higher level of autonomy together urge the power optimization in multihybrid autonomous vehicles. Both the multivehicle coordination control and the hybrid powertrain energy management should be optimized to maximize fuel savings. In this paper, we intend to have a computationally efficient framework to optimize them individually and then evaluate the overall control performance. The optimization is conducted in series. First is at the multivehicle system's level where the distributed locally optimal solution is given for vehicles with nonlinear dynamics. Second, the powertrain management optimization is conducted at the hybrid powertrain level. We provide an analytical formulation of the powertrain optimization for each hybrid vehicle by using Pontryagin's minimum principle (PMP). By approximating the optimal instantaneous fuel consumption rate as a polynomial of the engine speed, we can formulate the optimization problem into a set of algebraic equations, which enables the computationally efficient real-time implementation. To justify the applicability of the methodology in real-time, we give directions on numerical iterative solutions for these algebraic equations. The analysis on the stability of the method is shown through statistical analysis. Finally, further simulations are given to confirm the efficacy and the robustness of the proposed optimal approach. An off-road example is given in the simulation, although the framework developed can be applied to on-road scenario as well. [ABSTRACT FROM AUTHOR]

This paper presents a study of the energy-efficient operation of all-electric vehicles leveraging route information, such as road grade, to adjust the velocity trajectory. First, Pontryagin's maximum principle (PMP) is applied to derive necessary conditions and to determine the possible operating modes. The analysis shows that only five modes are required to achieve minimum energy consumption: full propulsion, cruising, coasting, full regeneration, and full regeneration with conventional braking. Then, the minimum energy consumption problem is reformulated and solved in the distance domain using dynamic programming to find the optimal speed profiles. Various simulation results are shown for a lightweight autonomous military vehicle. The sensitivity of energy consumption to regenerative-braking power limits and trip time is investigated. These studies provide important information that can be used in designing component size and scheduling operation to achieve the desired vehicle range. [ABSTRACT FROM AUTHOR]

In this paper, a novel analytical coupled trajectory optimization of a seven degrees-of-freedom (7DOF) Baxter manipulator utilizing extremum seeking (ES) approach is presented. The robotic manipulators are used in network-based industrial units, and even homes, by expending a significant lumped amount of energy, and therefore, optimal trajectories need to be generated to address efficiency issues. These robots are typically operated for thousands of cycles resulting in a considerable cost of operation. First, coupled dynamic equations are derived using the Lagrangian method and experimentally validated to examine the accuracy of the model. Then, global design sensitivity analysis is performed to investigate the effects of changes of optimization variables on the cost function leading to select the most effective ones. We examine a discrete-time multivariable gradient-based ES scheme enforcing operational time and torque saturation constraints in order to minimize the lumped amount of energy consumed in a path given; therefore, time-energy optimization would not be the immediate focus of this research effort. The results are compared with those of a global heuristic genetic algorithm (GA) to discuss the locality/globality of optimal solutions. Finally, the optimal trajectory is experimentally implemented to be thoroughly compared with the inefficient one. The results reveal that the proposed scheme yields the minimum energy consumption in addition to overcoming the robot's jerky motion observed in an inefficient path. [ABSTRACT FROM AUTHOR]

In this paper, an optimization procedure is presented by response surface method to optimize the temperature and velocity of drying air and thickness of the moist object inside the dryer. The optimization procedure is performed to determine the minimum drying time and energy consumption as responses. A two-dimensional (2D) numerical solution is accomplished to analyze coupled heat and mass transfer occurring during drying of an apple slice. The air flow and the moist object are solved conjugate, while the heat and mass transfer equations are solved coupled together using lattice Boltzmann method (LBM). Beside this, a sensitivity analysis is executed to calculate the sensitivity of the responses (drying time and energy consumption) to the control factors. Results reveal that the real optimized parameters for the minimum drying time and energy consumption are temperature (T = 80 °C), velocity (V = 0.10404 m/s), and thickness ratio (TR = 0.1). The results of numerical solution are compared to the experimental results, presenting a reasonable agreement. This analysis could be useful in food drying. [ABSTRACT FROM AUTHOR]

Traditionally viewed as mere energy consumers, buildings have adapted, capitalizing on smart grid technologies and distributed energy resources to efficiently use and trade energy, as evident in net-zero energy buildings (NZEBs). In this paper, we examine the opportunities presented by applying net-zero to building communities (clusters). This paper makes two main contributions: one, it presents a framework for generating Pareto optimal operational strategies for building clusters; two, it examines the energy tradeoffs resulting from adaptive decisions in response to dynamic operation conditions. Using a building cluster simulator, the proposed approach is shown to adaptively and significantly reduce total energy cost. [ABSTRACT FROM AUTHOR]

Disassembly is a very important step in recycling and maintenance, particularly for energy saving. However, disassembly sequence planning (DSP) is a challenging combinatorial optimization problem due to complex constraints of many products. This paper considers partial and parallel disassembly sequence planning for solving the degrees-of-freedom in modular product design, considering disassembly time, cost, and energy consumption. An automatic self-decomposed disassembly precedence matrix (DPM) is designed to generate partial/parallel disassembly sequence for reducing complexity and improving efficiency. A Tabu search-based hyper heuristic algorithm with exponentially decreasing diversity management strategy is proposed. Compared with the low-level heuristics, the proposed algorithm is more efficient in terms of exploration ability and improving energy benefits (EBs). The comparison results of three different disassembly strategies prove that the partial/parallel disassembly has a great advantage in reducing disassembly time, and improving EBs and disassembly profit (DP). [ABSTRACT FROM AUTHOR]

Energy consumption in five-axis machining of freeform surfaces can be considerably large for large-size parts. This paper presents a study on how to setup the workpiece in order to minimize the energy consumption without modifying the toolpath itself. For an arbitrary freeform workpiece, the way how it is setup on the working table highly affects the machine's kinematic behavior, which dominates the overall processing time and energy consumption. Taking into account the speed and acceleration limit of each axis of the machine, we first establish the energy consumption model as a function of the workpiece setup. However, this original model involves certain critical physically pertinent coefficients (such as the moment of inertial of a rotary table) which are usually unavailable in practice. Instead, by exploring insightful geometric characteristics of the five-axis machine, an alternative energy consumption model is established which is independent of those hard-to-obtain coefficients. A simple algorithm is then designed to optimize this model. Both computer simulations and physical cutting experiments demonstrate that, when compared with an arbitrary setup, the optimized workpiece setup is able to achieve a significant saving (as much as 50%) in both energy consumption and total machining time, both using a same tool path. [ABSTRACT FROM AUTHOR]

In this paper, a renewable-based integrated energy system is developed, analyzed, and optimized to achieve better peiformance. The present system is designed to be driven by concentrated solar thermal and biomass energies. Biomass fuel is used as the backup source of energy when the solar energy is not available. The system is designed to produce electricity, cooling, and hydrogen. The power output of the system is provided by solar-driven regenerative helium gas turbine during day time and from biomass gasification driven solid oxide fuel cell (SOFC) unit at night time. The fuel cell stack number is estimated as to provide the same net power. The system operates at energy and exergy efficiencies of 39.99% and 27.47%, respectively, at the optiinal point selected based on the optimization analysis. The parametric studies on performance and environmental impact assessment are peiformed to investigate the effects of several operating parameters on the system peiformance. [ABSTRACT FROM AUTHOR]

This paper considers multiobjective optimization under uncertainty (MOOUC) for the selection of optimal cutting conditions in advanced abrasive machining (AAM) processes. Processes considered are water jet machining (WJM), abrasive water jet machining (AWJM), and ultrasonic machining (USM). Decisions regarding the cutting conditions can involve optimization for multiple competing goals, such as surface finish, machining time, and power consumption. In practice, there is also an issue of variations in the ability to attain the performance goals. This can be due to limitations in machine accuracy or variations in material properties of the workpiece and/or abrasive particles. The approach adopted in this work relies on a strength Pareto evolutionary algorithm (SPEA2) framework, with specially tailored dominance operators to account for probabilistic aspects in the considered multiobjective problem. Deterministic benchmark problems in the literature for the considered machining processes are extended to include performance uncertainty and then used in testing the performance of the proposed approach. Results of the study show that accounting for process variations through a simple penalty term may be detrimental for the multiobjective optimization. On the other hand, a proposed fuzzy-tournament dominance operator appears to produce favorable results. [ABSTRACT FROM AUTHOR]

This paper deals with a high-pressure gas pipeline optimization, where the problem is to find the design properties of the pipelines and necessary compressor stations to satisfy customer requirements, using available supply gas and storage capacities. The considered objective function is the total annualized cost, including the investment and operating costs. The binary variables used to represent the flow direction of pipelines lead to a mixed integer nonlinear programming problem, solved by using the standard branch and bound solver in GAMS. The optimization strategy provides the main design parameters of the pipelines (diameters, pressures, and flow rates) and the characteristics of compressor stations (location, suction pressure, pressure ratio, station throughput, fuel consumption, and station power consumption) to satisfy customer requirements. [ABSTRACT FROM AUTHOR]

It is a difficult technical challenge to design thermoelectric power generation systems that work optimally over a broad dynamic range of thermal input power. Conventional systems are designed to work optimally for a nominal operating condition, while maintaining the ability to operate at off nominal and extreme operating conditions without damage to the system. For systems that operate in a narrow range of thermal power conditions, thermoelectric waste heat recovery system design is simplified. However, for applications that do have a wide range of operating conditions, designs typically exhibit overall average efficiencies that are reduced by approximately 20% or more compared with that achievable for the thermoelectric material operating at peak efficiency Both cars and trucks consume significant fuel at low mass flow rates. Since the ultimate goal of waste heat recovery systems is to minimize fuel consumption, it is critical that the recovery system be designed to operate near peak efficiency over the range of mass flow rates that make a significant contribution to overall power recovery. Such performance capability is especially important in city driving, and in hybrid vehicle applications. This paper describes a design concept that maximizes the performance for thermoelectric power generation systems in which the thermal power to be recovered is from a fluid stream (e.g., exhaust gas) subject to varying temperatures and a broad range of exhaust flow rates. The device is constructed in several parts, with each part optimized for a specific range of operating conditions. The thermoelectric system characteristics, inlet mass flow rates and fluid temperatures, and load and internal electrical resistances are monitored and generator operation is controlled to maximize performance. With this design, the system operates near optimal efficiency for a much wider range of operating conditions. Application of the design concept to an automobile is used to show the benefits to overall system performance. [ABSTRACT FROM AUTHOR]