Energy Optimal Control Research Articles

Stochastically predictive co-optimization of the speed planning and powertrain controls for electric vehicles driving in random traffic environment safely and efficiently

With inevitable random disturbance in traffic scenarios, electric vehicles (EVs) may face the driving safety issue, while, if operated over cautiously, the frequent speed variation deteriorates the energy economy of the EV. This conflict provokes a desire to understand the energy consumption behavior of EVs in a stochastic driving environment and reveal the corresponding energy optimal control policy. For addressing these issues, this paper develops a chance constraint stochastic model predictive control (CC-MPC) method for simultaneously optimizing the speed planning and the powertrain energy management strategy, which cooperates with a bi-level prediction model for estimating the future driving environment. Validated by massive car-following cases in the urban traffic flow, the proposed CC-MPC increases the success rate (no constraint violation) to 87%, while the deterministic MPC methods only achieve a success rate of 27%. Although the proposed CC-MPC method generates a sensitive driving style to variations of the driving environment, the conflict between energy economy and driving safety has been successfully removed. Validations suggest that when safety probability is 0.9, the success rate is 84% with only 0.8% deterioration in energy economy compared with the energy consumption resulting from the MPC with perfect knowledge of the leading vehicle speed.

Energy-Optimal Structures of HVAC System for Cleanrooms as a Function of Key Constant Parameters and External Climate

This article presents approximating relations defining energy-optimal structures of the HVAC (Heating, Ventilation, Air Conditioning) system for cleanrooms as a function of key constant parameters and energy-optimal control algorithms for various options of heat recovery and external climates. The annual unit primary energy demand of the HVAC system for thermodynamic air treatment was adopted as the objective function. Research was performed for wide representative variability ranges of key constant parameters: cleanliness class—Cs (ISO5÷ISO8), unit cooling loads—q˙j (100 ÷ 500) W/m2 and percentage of outdoor air—αo (5 ÷ 100)%. HVAC systems are described with vectors x¯ with coordinates defined by constant parameters and decision variables, and the results are presented in the form of approximating functions illustrating zones of energy-optimal structures of the HVAC system x¯* = f (Cs, q˙j, αo). In the optimization procedure, the type of heat recovery as an element of optimal structures of the HVAC system and algorithms of energy-optimal control were defined based on an objective function and simulation models. It was proven that using heat recovery is profitable only for HVAC systems without recirculation and with internal recirculation (savings of 5 ÷ 66%, depending on the type of heat recovery and the climate), while it is not profitable (or generates losses) for HVAC systems with external recirculation or external and internal recirculation at the same time.

Performance Evaluation of a New Active Suspension System by using Energy Optimal Control Theory

Performance Evaluation of a New Active Suspension System by using Energy Optimal Control Theory

Model Predictive Control for Automotive Climate Control Systems via Value Function Approximation

Among the auxiliary loads in light-duty vehicles, the air conditioning system is the single largest energy consumer. For electrified vehicles, the impact of heating and cooling loads becomes even more significant, as they compete with the powertrain for battery energy use and can significantly reduce the range or performance. While considerable work has been made in the field of optimal energy management for electrified vehicles and optimization of vehicle velocity for eco-driving, few contributions have addressed the application of energy-optimal control for heating and cooling loads. This letter proposes an energy management strategy for the thermal management system of an electrified powertrain, based on Model Predictive Control. Starting from a nonlinear model of the vapor compression refrigeration system that captures the dynamics of the refrigerant in the heat exchangers and the power consumption of the system, a constrained multi-objective optimal control problem is formulated to reduce energy consumption while tracking a desired thermal set point. An efficient implementation of MPC is proposed for real-time applications by introducing a terminal cost obtained from the approximation of the global optimal solution.

Energy-Optimal Control of Intelligent Track Inspection Trains: Design and Experiment

Energy-Optimal Control of Intelligent Track Inspection Trains: Design and Experiment

Energy-Optimal Control of Intelligent Track Inspection Trains: Design and Experiment

Energy-Optimal Control of Intelligent Track Inspection Trains: Design and Experiment

Energy-Optimal Control of Intelligent Track Inspection Trains: Design and Experiment

Energy-Optimal Control of Intelligent Track Inspection Trains: Design and Experiment

Optimal Energy Control of Battery Hybrid System for Marine Vessels by Applying Neural Network Based on Equivalent Consumption Minimization Strategy

This paper introduces an optimal energy control method whose rule-based control employs the equivalent consumption minimization strategy as the design standard to support a neural network technique. Using the proposed control method, the output command values for each power source based on the load of the ship and the state of charge of the battery satisfy the target of energy optimization. Based on the rules, the load of the ship and the state of charge of the battery were the input in the neural network, and the outputs of two generators were recorded as the output values of the neural network. To optimize the weights of the neural network and reduce the error between the predicted values and results, the Bayesian regularization method was employed, and a single hidden layer with 20 nodes, 2 input layers, and 2 output layers were considered. For the hidden layer, the tansigmoid function was applied, and for the activation functions of the output layers, linear functions were adopted considering the correlation between the input and output data used for training the neural network. The propulsion motor was fitted with a speed controller to ensure a stable speed, and a torque load was applied on the propulsion motor. To verify the accuracy of the neural network learning, a generator–battery hybrid system simulation was conducted using MATLAB Simulink, and the neural network learned values were compared with the generator output command values obtained based on the load of the ship and the battery state of charge. Additionally, it was confirmed that the generator command values were consistent with the neural network learned values, and the stability of the system was maintained by controlling the speed, voltage, and current control of the propulsion motor under various loads of the ship and different battery charge statuses.

A data augmentation methodology for machine learning modelling of distribution power grid: Application on optimal storage sizing and control.

The growth of distributed energy generations and electric vehicle charging stations in the low voltage grid brings out new challenges for distribution system operators. Actual methods of distribution network modelling are computationally expensive and omit aging of network physical components. This paper proposes a method of data augmentation for data-driven modelling. The methodology is divided into four parts: data generation, data-driven modelling, power flow boundaries estimation, and finally the application on optimal energy storage sizing and control.

Energy optimal control of mobile manipulators subject to compensation of external disturbance forces

This study proposes a new class of controllers for mobile manipulators subject to both undesirable forces exerted on the end-effector and unknown friction forces coming from joints directly driven by the actuators as well as undesirable forces resulting from the kinematic singularities appearing on the mechanism trajectory. Based on the suitably defined task space non-singular terminal sliding manifold (TSM) and the Lyapunov stability theory, we derive a class of estimated extended transposed Jacobian controllers which seem to be effective in counteracting the unstructured forces. Due to a redundant nature of the tasks to be accomplished, our controllers also a involve useful criterion function (energy consumption) in optimally tracking a desired trajectory. Moreover, in order to eliminate (or to alleviate) undesirable chattering effects the proposed control laws include second order sliding techniques. The numerical computations, which are carried out for a mobile manipulator consisting of a platform of (2, 0) type and a holonomic manipulator of two revolute kinematic pairs, illustrate the performance of the proposed controllers and simultaneously their minimizing properties. Numerical comparison with other control algorithms well-known in the literature is also given.

Функциональная структура комплекса систем управления движением поездов метрополитена

The main architectural aspects of metro train traffic control systems are presented. The structure of train traffic control processes is presented with the example of the State Unitary Enterprise “Moscow Metro” and the main tasks that need to be solved to achieve high levels of automation of train traffic control in subways are described. A functional structure of a complex of metro train traffic control systems is presented, covering the levels of organization and planning of train traffic, operational traffic control and direct executive systems. The relationship between objects of automation and remote control of train movement, devices of the operational level and systems of organization and planning of movement are shown. The necessity of re-equipping the metro with means of ensuring transport safety and linking them with traffic control systems such as: means of informing passengers, providing the possibility of promptly informing passengers both during normal operation of the transport system and in case of emergency situations, means of communication “passenger – control center (situation center)” is shown. The latter ensures interaction between passengers and operational personnel of the subway, and if necessary, video surveillance equipment that provides enhanced remote control of the current situation in the interior of rolling stock cars and on platforms. The use of complex systems for controlling the movement of trains in subways increases the efficiency of their use by increasing the throughput and carrying capacity, accurate fulfillment of the traffic schedule and the possibility of its rapid recovery in case of failures. At the same time, traffic safety increases by reducing the likelihood of dangerous train convergence , and also the energy consumption for train traction is reduced due to the choice of energy-optimal train control modes and the optimal distribution of travel time along the line by the criterion of minimum energy consumption for the duration of travel along the tracks

Energy Optimization and Effective Control of Reactive Distillation Process for the Production of High Purity Biodiesel

Biodiesel is a promising renewable energy option that significantly reduces the emission of greenhouse gases and other toxic byproducts. However, a major challenge in the industrial scale production of biodiesel is the desired product purity. To this end, reactive distillation (RD) processes, which involve simultaneous removal of the byproduct during the transesterification reaction, can drive the equilibrium towards high product yield. In the present study, we first optimized the heat exchange network (HEN) for a high purity RD process leading to a 34% reduction in the overall energy consumption. Further, a robust control scheme is proposed to mitigate any feed disturbance in the process that may compromise the product purity. Three rigorous case studies are performed to investigate the effect of composition control in the cascade with the temperature control of the product composition. The cascade control scheme effectively countered the disturbances and maintained the fatty acid mono-alkyl ester (FAME) purity.

Energy optimal attitude control for a solar-powered spacecraft

In this article we aim to maximize the net energy a solar-powered spacecraft gains when performing a maneuver. The net energy can be defined as the integral of the power supplied by the solar panels minus the power used by the attitude control system, and is important since energy is a scarce resource in space. Previous research on optimal attitude control has focused on optimization with respect to other costs, such as time-optimal control and optimal attitude control with respect to the integral of the square of the input. The energy flow depends on both the power spent on actuation and the power received from the solar panels. Thus, the optimal attitude control problem should be formulated in such a way that the attitude of the spacecraft relative to the Sun during the maneuver is included in the calculations. This paper proposes a cost function based on net power to address this problem, introducing a new cost function that incorporates the incoming energy from the solar irradiance and the outgoing energy due to actuation. A simulation study comparing an optimal control solution of the proposed net power cost function using IPOPT in CasADi is presented for a 6U CubeSat equipped with solar cell arrays, where the net power based optimal control maneuver is shown to compare favorably to a sun-pointing PD controller.

Energy optimization and routing control strategy for energy router based multi-energy interconnected energy system

The multi-energy interconnected energy system (MEIES) consists of multiple energy hubs (EHs) connected through the energy router (ER). To realize the optimal operation of the MEIES, this paper proposes an energy optimization and routing control strategy for the neotype MEIES based on ER using the lower and upper layers. In the lower layer, the multi-energy conversion device and storage device in the energy local area network (E-LAN) are modeled in detail, and the minimum cost electricity-heat-gas-cool multi-energy optimization strategy is realized. In the upper layer, the electric, heating, and natural gas network's energy flow is normalized by strict mathematical derivation in the energy wide area network (E-WAN). The generalized energy loss model of a multi-energy network with the unified mathematical equation is obtained. Then, based on the loss model and fully considering the differences of national conditions and policies, the unified energy routing control strategy of minimum loss local absorption (MLLA) in the monopoly market and of minimum loss multipath transmission (MLMT) in the competitive market are proposed for different decision-makers. Moreover, two kinds of ER transactions of competitive market priority ranking methods are proposed to achieve the expected incentive goal by transferring the energy loss of the transaction from one to another, which are the priority determined by the quotation and transaction volume. Finally, the feasibility and effectiveness of the overall strategy are verified by simulation and comparison.

Energy Optimization for Adjustable-Speed Drive Applications: Increasing Efficiency to Cut Costs and Preserve Assets

Whether you believe there is a connection between carbon dioxide and global warming or not, reducing energy consumption by improving operating efficiency has the benefit of preserving natural resources, reducing production costs, and postponing investment in infrastructure, such as generating stations and transmission lines. Induction motors are a logical target since they are the primary machines in use and account for the vast majority of industrial loads. Loads driven by induction motors consume more than half the electrical energy generated. A small improvement in efficiency has a pronounced effect on overall energy consumption. When a process has a range of operating points, adjustable-speed drives (ASDs) are often employed to realize energy savings based on affinity laws. While this saves substantial energy, it can be further optimized with flux vector control and other techniques. This article examines the topic of optimal energy control, which results in additional energy savings.

Attitude control of flapping wing aircraft based on energy optimization and ESO

Aiming at the problem of insufficient endurance performance of flapping wing aircraft, a stable attitude control algorithm based on energy optimization and ESO (extended state observer) is designed, which effectively reduces the energy consumption in cruise phase. Firstly, the longitudinal dynamic model of flapping wing aircraft is established, and then the uncertain part of the system and various unknown external disturbances are taken as the total disturbance. ESO module is introduced to observe and track the total disturbance in real time. Therefore, the system is transformed into a series integral system through the total disturbance feedback, and then the energy optimal control law is designed on the base of the transformed system. The numerical simulation results show that, compared with the traditional PID control method, the designed energy optimal control method reduces the average energy consumption by 35.28%.

Electrification of a Heavy-Duty CI Truck—Comparison of Electric Turbocharger and Crank Shaft Motor

A combustion engine-driven vehicle can be made more fuel efficient over some drive cycles by, for example, introducing electric machines and solutions for electrical energy storage within the vehicle’s driveline architecture. The possible benefits of different hybridization concepts depend on the architecture, i.e., the type of energy storage, and the placement and sizing of the different driveline components. This paper examines a diesel electric plug-in hybrid truck, where the powertrain includes a diesel engine supported with two electric motors, one supporting the crank shaft and one the turbocharger. Numerical optimal control was used to find energy-optimal control strategies during two different accelerations; the trade-off between using electrical energy and diesel fuel was evaluated using a simulation platform. Fixed-gear acceleration was performed to evaluate the contribution from the two electric motors in co-operation, and individual operation. A second acceleration test case from 8 to 80 km/h was performed to evaluate the resulting optimal control behavior when taking gear changes into account. A cost factor was used to relate the cost of diesel fuel to electrical energy. The selection of the cost factor relates to the allowed usage of electrical energy: a high cost factor results in a high amplification from electrical energy input to total system energy savings, whereas a low cost factor results in an increased usage of electrical energy for propulsion. The difference between fixed-gear and full acceleration is mainly the utilization of the electric crank shaft motor. For the mid-range of the cost factors examined, the crank shaft electric motor is used at the end of the fixed-gear acceleration, but the control sequence is not repeated for each gear during the full acceleration. The electric motor supporting the turbocharger is used for higher cost factors than the crank shaft motor, and the amplification from electrical energy input to total energy savings is also the highest.

Energy consumption optimization for AC drives position control

PurposeThe main purpose of this paper is to evaluate the two energy saving position control strategies for AC drives valid for a wide range of boundary conditions including an analysis of their energy expenses.Design/methodology/approachFor energy demands analysis, the optimal energy control based on mechanical and electrical losses minimization is compared with the near-optimal one based on symmetrical trapezoidal speed profile. Both control strategies respect prescribed maneuver time and define acceleration profile for preplanned rest-to-rest maneuver.FindingsPresented simulations confirm lower total energy expenditures of energy optimal control if compared with near-optimal one, but the differences are only small due to the fact that two energy saving strategies are compared.Research limitations/implicationsDeveloped overall control system consisting of energy saving profile generator, pre-compensator and position control system respecting principles of field-oriented control is capable to track precomputed state variables precisely.Practical implicationsEnergy demands of both control strategies are verified and compared to simulations and preliminary experiments. The possibilities of energy savings were confirmed for both control strategies.Originality/valueExperimental verification of designed control structure is sufficiently promising and confirmed assumed energy savings.

Energy Optimal Control of Motor Drive System for Extending Ranges of Electric Vehicles

The short driving range per charge of electric vehicle is a critical shortcoming hindering its popularization and application. This article proposes an in-vehicle energy optimal control (EOC) to improve the efficiency of the motor drive system. This article first builds a vehicle's longitudinal dynamics model and a motor drive system's efficiency model. Then, a Lagrange-Euler-based optimal control theory is formulated with detailed proof. Based on the vehicle dynamics model, a cost function is constructed by considering the energy consumption and speed demand. Combining the cost function and the Lagrange-Euler-based optimal control theory, an in-vehicle EOC is proposed. The idea of the in-vehicle EOC is mainly to make the motor drive system operate in the high efficiency region as much as possible, which results in longer driving ranges. The in-vehicle EOC is validated on a co-simulation platform and the results show it outperforms the model predictive control method.

Multi-objective Optimization of Vertical Profile Trajectory for Civil Aircraft

In order to further promote the sustainable development of civil aviation transportation industry, how to reduce the operating cost of civil aircraft has always been the focus of research. This paper establishes a performance optimization model of the aircraft on the vertical flight profile, which takes flight fuel cost and time cost as multi-objective functions and adopts energy state approximation method and optimal control theory. The trajectory of an aircraft in climbing, cruising and descent phases has been optimized using the established model and then simulated with the MATLAB software. The comparison results show that both fuel cost and time cost are significantly reduced through trajectory optimization.