Intelligent energy management of hybrid electric vehicles is feasible with a priori information of route and driving conditions. Model predictive control (MPC) with finite horizon road grade preview has been proposed as a viable predictive energy management approach. We propose that our novel distance constrained-adaptive concurrent dynamic programming (DC-ACDP) approach can provide better energy management than MPC without any road grade information in context of an extended range electric vehicle (EREV). In this article, we have evaluated and compared the MPC and DC-ACDP energy management strategies for a real-world driving scenario. The simulations were conducted for a 160 km drive with road grade variation between + 4 % and –1%. Results show that the DC-ACDP approach is near-optimal and improves overall energy consumption by a maximum of 4.25%, in comparison to the simple MPC with a finite horizon road grade preview implementation. Additionally, a higher value for energy storage system state of charge (SOC) tracking penalty p 2 results in the net energy consumption for MPC to converge toward that of DC-ACDP. A combination of the MPC and DC-ACDP approach is also evaluated with only 1.25% maximum improvement over simple MPC.

Utility companies seek to increase energy efficiency and productivity and try to reduce peak loads. This often involves consumer-side demand management in residential areas using dynamic time-of-use (ToU) tariff. Such strategies work if the consumer-side response is at least partly automated using some real-time optimization strategy. Our paper proposes a consumer-side optimization and control framework for scheduling the electric appliances in a smart household and preserving a thermal comfort level through an electric heating system. Our framework consists of two optimization components interacting with each other. The first optimization component schedules the home appliances based on a mixed integer programming approach. An electric vehicle (EV) is considered as a special home appliance with an energy storage capability. The second optimization component is the model predictive control (MPC) strategy for the electric heating system, such that the input constraints are defined by the scheduling results of the first component. Due to outside temperature variations, the input constraints may impede the MPC to maintain the required thermal comfort, which triggers a rescheduling event for the first component. The efficiency of the framework is presented in multiple simulations for scenarios with different consumer behaviors.

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%.

Despite improved commissioning practices, malfunctions or degradation of building systems still contribute to increase up to 20% the energy consumption. During operation and maintenance stage, project and building technical managers need appropriate methods for the detection and diagnosis of faults and drifts of energy performances in order to establish effective preventive maintenance strategies. This paper proposes a hybrid and multilevel fault detections and diagnosis (FDD) tool dedicated to the identification and prioritization of corrective maintenance actions helping to ensure the energy performance of buildings. For this purpose, we use dynamic Bayesian networks (DBN) to monitor the energy consumption and detect malfunctions of building equipment and systems by considering both measured occupancy and the weather conditions (number of persons on site, temperature, relative humidity (RH), etc.). The hybrid FDD approach developed makes possible the use of both measured and simulated data. The training of the Bayesian network for functional operating mode relies on on-site measurements. As far as dysfunctional operating modes are concerned, they rely mainly on knowledge extracted from dynamic thermal analysis simulating various operational faults and drifts. The methodology is applied to a real building and demonstrates the way in which the prioritization of most probable causes can be set for a fault affecting energy performance. The results have been obtained for a variety of simulated situations with faults deliberately injected, such as increase in heating preset temperature and deterioration of the transmission coefficient of the building's glazing. The limitations of the methodology are discussed and are translated in terms of the ability to optimize the experiment design, control period, or threshold adjustment on the control charts used.

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.

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.

The energy that is needed for operating a self-powered device is provided by the energy excess in the system in the form of kinetic energy, or a combination of regenerative and renewable energy. This paper addresses the energy exchange issues pertaining to regenerative and renewable energy in the development of a self-powered dynamic system. A rigorous framework that explores the supply and demand of energy for self-powered systems is developed, which considers uncertainties and optimal bounds, in the context of optimal uncertainty quantification. Examples of regenerative and solar-powered systems are given, and the analysis of self-powered feedback control for developing a fully self-powered dynamic system is discussed.

This paper presents a multivariable Newton-based extremum seeking control (ESC) scheme for efficient operation of a chilled-water plant. A modelica -based dynamic simulation model of the chilled-water plant consists of one screw chiller and one counter-flow cooling tower was adopted for evaluation of proposed two-input Newton-based ESC controller. The ESC controller takes the total power of the chiller compressor, the cooling-tower fan, and the condenser water (CW) pump as feedback signal and discovers the optimum outputs of cooling-tower fan speed and the condenser-loop water flow rate to maximize the power efficiency in real time with the cooling load being satisfied. Remarkable energy saving is observed for several testing conditions.

In this article, we address the problem of synthesizing communication networks for unmanned aerial vehicles (UAVs) in the presence of resource constraints. UAVs can be deployed as backbone nodes in ad hoc networks that can be central to civilian and military applications. The cost of operation of the network depends on the resources that are used such as the total power consumption associated with the network and the number of communication links in the network. The objective of the problem is to synthesize a communication network that maximizes connectivity subject to the cost of operation being within the specified budget for the resources. It is known that algebraic connectivity is a measure of robust connectivity and hence, it is chosen as an objective for optimization. We pose the network synthesis problem as a mixed-integer semidefinite program (MISDP): (1) provide an algorithm for computing optimal solutions using cutting plane methods; (2) develop lower bounds by posing the problem as a binary semidefinite program; and (3) construct feasible solutions using heuristics and estimate their quality. The network synthesis problem is a nondeterministic polynomial--time (NP)-hard problem. We provide some computational results to corroborate the performance of the proposed algorithms.

In redundant manipulation systems, the end-effector path does not completely determine the trajectories of all the individual degrees of freedom (dof) and the additional dofs can be used to enhance the performance in some sense. The paper deals with utilizing the redundancy to minimize energy consumption. A full linear electromechanical model is used, and the exact energy consumption is calculated. The optimization includes also displacement limits via penalty functions that are included in the cost function. The optimal trajectory is feasible in the sense that it can be obtained by a finite input voltage and all the velocities are continuous. The solution is based on projections that separate the system and the input into two parts. One that is completely determined by the end-effector path and the other that is free for optimization. The important and delicate issue of boundary conditions is resolved accordingly. Simulation results show that redundancy, even with limited joint motion, can lead to a considerable reduction in energy consumption.

In this study we present a procedure for the design and implementation of a control strategy to optimize energy use within a light weight hydraulic hybrid passenger vehicle. The hydraulic hybrid utilizes a high pressure accumulator for energy storage which has superior power density than conventional battery technology. This makes fluid power attractive for urban driving applications in which there are frequent starts and stops and large startup power demands. A dynamic model of a series hydraulic hybrid powertrain is presented along with the design of a model predictive control based energy management strategy. Model predictive control was chosen for this study because it uses no future information about the drive cycle in its design. This increases the flexibility of the controller allowing it to be directly applied to a variety of drive cycles. Using the model predictive framework, a holistic view of the powertrain was taken in the design of the control strategy, and the impact of each actuator’s efficiency on overall efficiency was evaluated. A hardware-in-the-loop experiment using an electro-hydraulic powertrain testbed was then used to validate the dynamic model and control performance. Through a simulation study in which each actuator’s efficiency was given varying levels of priority in the objective function, it was found that overall system efficiency could be improved by allowing for small sacrifices in individual component performance. In fact, the conventional wisdom of using the additional degrees of freedom within a hybrid powertrain to optimize engine efficiency was found to yield the lowest overall powertrain efficiency. In this work we present a rigorous framework for the design of an energy management strategy. The design method improves the powertrain’s operational efficiency by finding the best balance between optimizing individual component efficiencies. Furthermore, since the design of the control strategy is built upon an analysis of individual components, it can be readily extended to other architectures employing different actuators.

The high energy consumption of market-ready active suspension systems is the limiting factor in the competition with semi-active devices. The variable geometry active suspension is an alternative with a significantly lower power consumption. However, previous designs suffer from packaging problems, nonlinear stiffness characteristics, and failsafe issues. This paper discusses the feasibility of a recently presented, new design, variable geometry actuator, which has a fixed spring and constant stiffness. An actuator model is derived that includes the electric motor and friction characteristics. Using this model, a cascaded controller is developed and the steady-state and dynamic properties are evaluated. The simulation results are validated with prototype tests. The results show a good correspondence between simulations and measurements. Furthermore, a 10 Hz bandwidth can be easily obtained. It is concluded that the electromechanical low-power active suspension design is feasible and that the model gives a fairly accurate representation of both the steady-state and dynamic characteristics of the prototype.

As the heating, ventilating, and air conditioning (HVAC) systems accounts for a major sector of energy consumption for commercial buildings, there has been a greater demand for improving the efficiency of such systems. The air-side economizers have been developed as a class of energy-saving HVAC devices that may increase the energy efficiency by taking advantage of outdoor air during cool or cold weather. However, many economizers do not operate in the expected manner and waste even more energy than before installation, mostly due to the unreliable sensors and actuators in practice. Better control strategy is needed for optimal and robust operation. In this paper, an extremum-seeking control (ESC) based self-optimizing strategy is proposed to minimize the energy consumption, with the feedback of chilled water supply command rather than the temperature and humidity measurements. The mechanical cooling load is minimized by seeking the optimal outdoor air damper opening in real time. Such scheme does not need temperature and humidity sensors, and depends much less on the knowledge of the economizer model. Simulation was performed on a MODELICA based transient model of a single-duct air-handling unit developed with DYMOLA and AIRCONDITIONING LIBRARY . The simulation results demonstrated the potential of using ESC to achieve the minimal mechanical cooling load in a self-optimizing manner. In addition, an antiwindup ESC scheme is proposed to handle the ESC windup issue due to actuator (damper) saturation. The simulation results validated the effectiveness of the proposed antiwindup ESC.

Vibration suppression of a large space structure is achieved by switching between different actuators in order to optimize control performance and to reduce energy consumption. The theoretical properties of the proposed control strategy are reported together with some experimental results.

The utility of many controlled dynamic systems is impaired by excessive power consumption and related hardware. In this paper, the potential for energy regeneration in a broad class of dynamic subsystems is considered. In doing so, we consider the potential for a dynamic subsystem to be self-contained and self-sustaining. The development begins with a definition for subsystem passivity and new definitions for subsystem regenerativity and activity. While diagnosis of a passive subsystem can be made simply through observation of the isolated subsystem, it is shown that the diagnosis of a subsystem as either regenerative or active, in general, requires the computation of the subsystem’s average power absorption. Consequently, the accuracy of such a diagnosis depends upon the level of knowledge regarding the overall system and its inputs. Simple mass suspension examples are provided to demonstrate the developed theory.

The development of an optimal output feedback controller for the Penn State electric ventricular assist device (EVAD) is addressed. The controller is designed to minimize the electric energy consumption of the EVAD, while utilizing the measured pusher plate position as the only feedback signal. The controller incorporates statistical information of the targeted patient population and optimizes the expected value of the system performance. The present paper shows that the optimal output feedback control scheme is asymptotically stable when the EVAD operates in a steady beat rate mode, and bounded-input/bounded-output stable when the beat rate undergoes a transient. Since the EVAD operates in either steady or transient beat rate modes, this optimal control scheme is stable.

The performance of AC induction motor drive systems as torque sensors is evaluated. Their potential for monitoring tool wear or breakage as well as machine component failure is discussed. A bond graph model of the motor and drive train is developed to determine the relation between electric motor power consumption and the applied torques and to identify the machine components that dominate this dynamic relation. The model, as applied to a CNC milling machine spindle system, shows (1) that the rotor input power is linearly related to both static and dynamic cutting torques under normal process conditions and (2) that the static sensitivity of the spindle system as a sensor increases and the bandwidth of the sensor decreases as the spindle speed is increased. The sensitivity and bandwidth changes are due to changes in system parameters caused by altering the gear ratios between the motor and spindle. Experiments show that the signal-to-noise ratio is affected by the motor torque vibration induced by a geometric irregularity in the drive belt. The vibration, however, does not affect the bandwidth of the sensor system. Power monitoring appears to be a viable torque sensing technique.

The load distribution problem for two coordinating industrial robots handling a single object is studied in this paper. When two industrial robots grasp a single object, the total number of applied torques is usually greater than six. Thus the joint torques of two robots for a required motion of the object is not unique. The redundant degrees of freedom may be used to optimize certain kind of performance. We first select the least energy consumption as the optimization criterion. Optimal algorithms without and with a bound on the joint torques are investigated. The results show that the algorithms are computationally complicated which are not suitable for real-time applications. Alternatively, optimal algorithms are then proposed for load distribution with minimum exerted forces on the object. The algorithms are obtained with much less computational time, which makes it attractive for real-time applications. The algorithms are applied to two PUMA 560 type robots as an illustration.

This paper addresses the development of an output feedback controller for the Penn State Electric Ventricular Assist Device (EVAD). The control law is designed to minimize the electric power consumption of the motor, while utilizing the measured pusher plate position as its only feedback signal. The control algorithm results in a suboptimal performance. The feedback gain function is calculated such that the expected value of the deviations between the suboptimal and full state feedback power consumption values is minimized. The system state initial conditions are treated as random variables with specified probability density functions. Numerical simulations indicate that the output feedback controller of the EVAD has a near optimum performance (the excessive electric power consumption is less than 1 percent), and a time shift manipulation of a single feedback gain function can drive the EVAD in various speeds with minimal energy losses.

Although Energy Management and Control Systems (EMCS) have since the early 1970’s contributed significantly to the reduction (20-40 percent) of energy use in buildings without sacrificing occupants’ comfort, their full capabilities have not been completely realized. This is in part due to their inability to quickly detect and compensate for failures in the Heating, Ventilation and Air Conditioning (HVAC) system. In fact, no matter how good the control scheme for the HVAC system might be, the presence of undetected faults can completely offset any expected savings. This paper presents a methodology for detecting faults in an HVAC system using a nonlinear mathematical model and an extended Kalman filter. The technique was implemented in a computer program and successfully used to detect “planted” faults in simulations of the air handler unit of an HVAC system. Test results are presented to demonstrate the effectiveness of the methodology.