Speed Scheduling Research Articles

Hierarchical Energy Management Strategy for Plug-in HEVs Based on Historical Data and Real-Time Speed Scheduling

Hierarchical Energy Management Strategy for Plug-in HEVs Based on Historical Data and Real-Time Speed Scheduling

Neural Network‐based Method to Compute Recommended Cruise Level and Optimal Climb Location

AbstractSystem engineering towards utilizing machine learning solutions in the aviation domain is a fast‐progressing discipline. This paper tries to bring out the system engineering and implementation aspects of ML solution as an improvement in a sub‐system of an existing aviation system. The flight management systems (FMSs) compute the recommended cruise level (RCL) considering the aircraft's gross weight, its speed schedule, and the forecast wind. The RCL increases with distance in the no wind conditions as the fuel get consumed, reducing the gross aircraft weight. The RCL profile may have frequent climbs and descents in the presence of winds, which is not desired for flight operations.The document proposes a neural network‐based method for computing an operationally acceptable cruise profile considering the wind and temperature weather forecast. The method recommends step climbs and step descents (optimal level and optimal step location) in such a way that they are separated by a large enough cruise phase. From system engineering point of view two guiding documents for such an implementation to look forward are “EASA Concept Paper: First usable guidance for Level 1 machine learning applications” [12] and “Process Standard for Development and Certification/Approval of Aeronautical Safety‐Related Products Implementing AI” [13].Two neural networks have been created to estimate wind compensated ECON cruise speeds and required time‐fuel to cover the next 500NM distance. The RCL profiles are generated for various test wind scenarios for Airbus A220 aircraft. It has been recorded the proposed neural network‐based method is better at recommending a cruise altitude and it is 9X faster than the existing method to calculate the RCL and optimal climb location. The neural network‐based design also helped in reducing the memory footprint by more than 100 times as there is no requirement of storing the performance database files required by the existing system. Only the parameters of the trained neural networks are required to be stored. Most importantly, the RCL computed by the proposed neural network‐based method yields a smaller operational cost than that determined by the existing method. The proposed method is designed to replace the existing traditional subsystem to give an overall improved performance while the functionalities of other subsystems remain unaffected.

Integrated optimization of speed schedule and energy management for a hybrid electric cruise ship considering environmental factors

Severe global energy crisis and greenhouse effect have promoted the rapid growth of hybrid electric propulsion systems (HEPSs) applied to cruise ships. However, the conventional methodologies, which optimize sailing speed and energy management separately, restrict the improvement of the ship energy efficiency. This study proposes an integrated optimization of speed schedule and energy management considering changes in wind, waves, and drifting ice during the voyage. First, a refined energy flow model of the HEPS against the resistance from wind, waves, and drifting ice in addition to the hydrostatic pressure is established. Second, the integrated optimization problem of speed schedule and energy management is formulated to pursue minimal fuel consumption. Third, the complex optimization problem is solved by a low computation load algorithm, in which the K-means algorithm and physical turning points divide the target route into several segments considering environmental characteristics, the particle swarm optimization algorithm optimizes the speed in each segment, and the equivalent fuel consumption minimization strategy optimizes the energy management during sailing. Finally, two case studies of a cruise ship sailing in the Drake Passage validate the effectiveness of the proposed method. The results show that the integrated optimization significantly reduces fuel consumption compared with the conventional separation optimization in terms of either speed schedule or energy management, as well as the conventional heuristic strategy. Furthermore, the integrated optimization demonstrates advantages in reducing greenhouse emissions caused by fuel and electricity consumption of the HEPS.

Joint Efficient UAV Trajectory and Velocity Optimization for IoT Data Collection Using a New Projection Algorithm

Unmanned aerial vehicle (UAV)-assisted networking and communications are increasingly used in different applications, especially in the data collection of distributed Internet of Things (IoT) systems; its advantages include great flexibility and scalability. However, due to the UAV’s very limited battery capacity, the UAV energy efficiency has become a bottleneck for longer working time and larger area coverage. Therefore, it is critical to optimize the path and speed of the UAV with less energy consumption, while guaranteeing data collection under the workload and time requirements. In this paper, as a key finding, by analyzing the speed–power and the speed–energy relationships of UAVs, we found that there should be different speed selection strategies under different scenarios (i.e., fixed time or fixed distance), which can lead to much-improved energy efficiency. Moreover, we propose CirCo, a novel algorithm that jointly optimizes UAV trajectory and velocity for minimized energy consumption. CirCo is based on an original projection method, turning a 3D problem (GN locations and transmission ranges on the 2D plane, plus the minimum transmission time requirements on the temporal dimensions) into a 2D problem, which could help to directly find the feasible UAV crossing window, which greatly reduces the optimization complexity. Moreover, CirCo can classify the projected conditions to calculate the optimal path and speed schedule under each category, so that the energy consumption of each situation can be fine-regulated. The experiments demonstrate that CirCo can save as much as 54.3% of energy consumption and 62.9% of flight time over existing approaches.

Wind turbine rotor speed design optimization considering rain erosion based on deep reinforcement learning

Rain erosion is one of the most detrimental factors contributing to wind turbine blade (WTB) coating fatigue damage especially for utility-scale wind turbines (WTs). To prevent rain erosion induced WTB coating fatigue damage, this paper proposes a deep reinforcement learning (DRL)-based optimization method for finding the optimal rotor speed under different rain intensities and wind speeds. First, an efficient physics-based model for predicting WTB coating fatigue damage considering the comprehensive blade coating fatigue mechanism, rain intensity distribution, and wind speed distribution is presented. Then, a WT rotor speed design optimization problem is constructed to search for the optimal rotor speed under different rain intensity and wind speed conditions. To address the challenge of optimizing the efficiency, the original design optimization problem is converted into a DRL-based design optimization model. A hybrid reward is proposed to enhance the DRL agent trained by a deep deterministic policy gradient algorithm. Finally, the proposed DRL-based design optimization method is utilized to guide the optimal rotor speed scheduling of a 5-MW WT under given wind speed and rain intensity conditions. The results show that the proposed method could extend the predicted WTB blade coating fatigue life by 2.55 times with a minor reduction in the energy yield (0.027%) compared to the original rotor speed schedule that only considers maximum power capture. The computational time of the proposed method is reduced significantly compared to that of the traditional gradient and evolutional design optimization methods.

A cutting speed scheduling method with kinematic constraints for complex curved surface turning

In the rapid development of high-end equipment, the complex curved surface part is growing in demand. To improve the machining efficiency of the complex curved surface turning, this study proposes a cutting speed scheduling method with kinematic constraints. Based on the calculation of feature points of the desired curve, the desired curve is sectioned into segments. The feasible kinematic parameters of the drive axis in each segment are derived, then the cutting speed is scheduled with the S-curve motion profile. The contrast experiments show that the surface roughness of the part machined with the proposed cutting speed scheduling method satisfied the requirement, meanwhile, compared with the constant spindle speed cutting and the constant cutting speed turning, the machining time of the proposed method is respectively reduced by 32.54% and 21.46%. The contribution of this study is significant for the development of complex curved surface part turning.

Dynamic Bus Scheduling of Multiple Routes Based on Joint Optimization of Departure Time and Speed

Dynamic bus scheduling is a rational solution to the urban traffic congestion problem. Most previous studies have considered a single bus line, and research on multiple bus lines remains limited. Departure schedules have been typically planned by making separate decisions regarding departure times. In this study, a joint optimization model of the bus departure time and speed scheduling is constructed for multiple routes, and a coevolutionary algorithm (CEA) is developed with the objective function of minimizing the total waiting time of passengers. Six bus lines are selected in Shenyang, with several transfer stations between them, as a typical case. Experiments are then conducted for high-, medium-, and low-intensity case of smooth, increasing and decreasing passenger flow. The results indicate that combining the scheduling departure time and speed produces better performances than when using only scheduling departure time. The total passengers waiting time of the genetic algorithm (GA) group was reduced by approximately 25%–30% when compared to the fixed speed group. The total passengers waiting time of the CEA group can be reduced by approximately 17%–24% when compared to that in the GA group, which also holds true for a multisegment convex passenger flow. The feasibility and efficiency of the constructed algorithm were demonstrated experimentally.

Integrated Decision on Route Planning and Speed Scheduling ofContainer Liners Considering Emission Control Areas

Integrated Decision on Route Planning and Speed Scheduling ofContainer Liners Considering Emission Control Areas

Hybrid Estimation of Distribution Algorithm for Solving Three-Stage Multiobjective Integrated Scheduling Problem

Aiming at reducing the total energy consumption of three stages processing-transportation-assembly in the assembly manufacturing industry, a three-stage multiobjective integrated scheduling problem with job batch transportation considering the energy consumption (3sMISP_JBTEC) is proposed, and a comprehensive energy consumption model of multistage of 3sMISP_JBTEC with an improved turn off/on strategy in the processing stage and considering speed in the transportation stage is formulated. Then, a hybrid estimation of distribution algorithm with variable neighborhood search (HEDA_VNS) is developed to solve the scheduling problem. In the HEDA_VNS, the reasonable coding/decoding rules and speed scheduling scheme (SSS) are designed. Moreover, two local search strategies are designed to further enhance the performance of HEDA_VNS. Among them, three types of neighborhood search strategies are devised in Local Search I to improve the search efficiency while retaining the structure of the original high-quality solution. A variable neighborhood hybrid operation based on the speed scheduling set is designed in Local Search II to further improve the quality of the solution while balancing the optimization goals. Finally, simulations and comparisons show the efficiency of the proposed HEDA_VNS.

Methodology of developing mathematical-ANN hybrid model based mill setup model for hot strip rolling

Hot strip mill, one of the most important units of an integrated steel plant, is operated by mill setup model. The conventional mill setup models calculate thermal, reduction and speed schedules of the material being rolled using mathematical models derived from fundamental principles of heat transfer and plastic deformation. However, such mill setup models often compute inaccurate schedules leading to quality issues and operational problems. This paper describes a novel technique of developing a hybrid model by integrating mathematical models with artificial neural network (ANN) model. The trained hybrid models use a multivariable optimization algorithm to calculate the thermal, reduction and speed schedules during hot strip rolling. More than six hundred coils were successfully rolled in an industrial hot strip mill using the mill setup model developed under the present work. It is found that the mill setup model developed using the hybrid models is more accurate and faster than the mill setup models that use conventional mathematical models.

A newly developed corner smoothing methodology based on clothoid splines for high speed machine tools

Continuous linear commands are widely executed in computer numerical control (CNC) machining. The tangential discontinuity at the junction of consecutive segments restricts the machining efficiency and deteriorates the surface quality. Corners of linear segments have been successfully blended by inserting parametric splines. There still exists challenges when the common methods are employed in the line-segment commands due to part of the following restrictions: (1) the stringent computation for iteratively calculating the arc-length; (2) the unwanted feedrate fluctuation; (3) the oversize contour deviation for separately completing curve fitting and velocity planning. A novel smoothing method based on a clothoid pair to synchronously accomplish planning of geometry blending and speed scheduling is proposed, the spline parameter of which is arc-length-parameterized. The arc-length, curvature extreme, and geometric shape of the transition curve are analytically expressed by the transition length. On these bases, the transition curve and the velocity profile are concurrently constructed based on the predefined approximation error, the reachable velocity, and normal kinematic constraints in the look-ahead stage. Then, a real-time interpolation scheduling is developed to overcome the crossing difficulties between the linear and parametric segments. Compared with existing methods, the proposed method can analytically calculate the length of transition curves for the arc-length-parameterized expression form. Furthermore, the feedrate fluctuation is eliminated in the fine interpolation. Moreover, the overlarge contour derivation produced by corner smoothing is significantly avoided. It is friendlier to the CNC system for the on-line executing smooth motion since more computing resources can be released to handle other tasks, smoother motion can be achieved and higher contour accuracy can be obtained. The experimental results also demonstrate its practicability and reliability.

Leakage-Aware Battery Lifetime Analysis Using the Calculus of Variations

Due to non-linear factors such as the rate capacity and the recovery effect, the shape of the battery discharge curve plays a significant role in the overall lifetime of the batteries. Accordingly, this paper proposes a simple heuristic battery-aware speed scheduling policy for periodic and non-periodic real-time tasks in Dynamic Voltage Scaling (DVS) systems with non-negligible leakage/static power. A set of comprehensive analysis has been conducted to compare the battery efficiency of the proposed policies with an optimal solution, which could be derived via the Calculus of Variations (CoV). These evaluations have taken into account both periodic and non-periodic tasks in DVS-based systems. Our experiments have shown a maximum of 7% difference between the optimal solution and the simple heuristic speed scheduling for realistic settings of the battery model. By considering the calculated optimal speed scheduling for different tasks (with different utilizations), a two-phase algorithm has been proposed, in which a speed approximation function is being calculated offline based on curve fitting, while the best execution speed is applied online. The results show a maximum of 17.7% and 11.3% battery charge saving for non-periodic and periodic tasks in comparison to the baseline critical frequency method, respectively.

A pseudo-linear time algorithm for the optimal discrete speed minimizing energy consumption

We consider the classical problem of minimizing off-line the total energy consumption required to execute a set of n real-time jobs on a single processor with a finite number of available speeds. Each real-time job is defined by its release time, size, and deadline (all bounded integers). The goal is to find a processor speed schedule, such that no job misses its deadline and the energy consumption is minimal. We propose a pseudo-linear time algorithm that checks the schedulability of the given set of n jobs and computes an optimal speed schedule. The time complexity of our algorithm is in ${\mathcal {O}}(n)$ , to be compared with ${\mathcal {O}}(n\log (n))$ for the best known solution. Besides the complexity gain, the main interest of our algorithm is that it is based on a completely different idea: instead of computing the critical intervals, it sweeps the set of jobs and uses a dynamic programming approach to compute an optimal speed schedule. Our linear time algorithm is still valid (with some changes) with arbitrary (non-convex) power functions and when switching costs are taken into account.

Offspeeding: Optimal energy-efficient flight speed scheduling for UAV-assisted edge computing

Millions of Internet of Thing (IoT) devices have been widely deployed to support applications such as smart city, industrial Internet, and smart transportation. These IoT devices periodically upload their collected data and reconfigure themselves to adapt to the dynamic environment. Both operations are resource consuming for low-end IoT devices. An edge computing enabled unmanned aerial vehicle (UAV) is proposed to fly over to collect data and complete reconfiguration computing tasks from IoT devices. Distinct from most existing work, this paper focuses on flight speed scheduling that allocates proper flight speed to minimize the energy consumption of the UAV with a practical energy model, under the constraints of individual task execution deadlines and communication ranges. We formulate the Energy-Efficient flight Speed Scheduling (EESS) problem, and devise a novel diagram to visualize and analyze this problem. An optimal energy-efficient flight speed scheduling (Offspeeding) algorithm is then proposed to solve the offline version of the EESS problem. Utilizing Offspeeding and the optimal properties obtained from the theoretical analysis, an online heuristic speed scheduling algorithm is developed for more realistic scenarios, where information from IoT devices keeps unknown until the UAV flies close. Finally, simulation results demonstrate our online heuristic is near optimal. This research sheds light on a new research direction, e.g., deadline driven UAV speed scheduling for edge computing with a practical propulsion energy model.

Optimal Speed Scheduling with Arrival Time Constraint for Solar-Augmented Aircraft

Improvements in solar cell weight and performance have enabled the development of small solar-powered aircraft that operate at low altitudes. Unlike high-altitude pseudosatellites, these aircraft must contend with significant stochastic variations in wind and solar energy. This paper examines speed scheduling for aircraft equipped with solar panels, but which do not necessarily supply their entire energy budget from solar power. Soaring speed-to-fly theory is extended to incorporate both solar and updraft energy. When used to optimize shadow crossings for solar aircraft, it allows pure solar flight with 20% greater cloud coverage. The requirement for pure solar flight is relaxed and the use of stored energy onboard allows derivation of a speed to fly with an arrival time constraint, allowing the exploitation of stochastic energy while satisfying a flight plan. The speed schedule is tested in Monte Carlo simulations, and shows the ability to satisfy an arrival time constraint while reducing energy consumption by approximately 2% and the variance in final energy state by approximately 3%. The algorithm is also tested in flight on a small unmanned aerial system, satisfying an arrival time condition within 1% of the nominal flight time.

Simultaneous optimization of mixed photon energy beams in volumetric modulated arc therapy.

Despite the availability of multiple energy photon beams on clinical linear accelerators, volumetric modulated arc therapy (VMAT) optimization is currently limited to a single photon beam. The purpose of this work was to present a proof-of-principle study on an algorithm for simultaneous optimization of mixed photon beams for VMAT (MP - VMAT), utilizing an additional photon energy as an additional degree of freedom. The MP - VMAT optimization algorithm is presented as a two-step heuristic approach. First, a convex linear programming problem is solved for simultaneous optimization of nonuniform dual energy intensity maps (DEIMs) for an angular resolution of 36 equi-spaced beam segments. Subsequently, for a given gantry speed schedule, the second step aims to best replicate each DEIM by dispersing MP - VMAT apertures along with their corresponding intensities over their respective beam segment. This constitutes a nonlinear problem, which is linearized using McCormick relaxation. The final large-scale mixed integer linear programming (MILP) dispersion model ensures a contiguous and smooth transition of multileaf collimators (MLCs) from one beam segment to the next. To demonstrate the proof-of-principle, we first compared the quality of dose volume histograms (DVHs) of MP - VMAT to the ones calculated from 36 DEIMs following the step 1 of MP-VMAT model. Additionally, the MLCs motion violations were evaluated for the complete 360° gantry rotation for gantry speeds ranging from 1 to 6° per second. The quality of MP - VMAT plans were also compared to conventional single energy VMAT plans via DVH, homogeneity index (HI), and conformity number (CN) for two prostate cases. The MP - VMAT model resulted in a successful convergence of DVHs relative to the ones from DEIMs with HI and CN of 0.05 and 0.9, respectively, for 1 and 2° per second gantry speed schedules. In replicating the DEIMs, the MILP dispersion model was able to achieve optimality for almost all segments at 1° per second and for majority of segments at 2° per second. Although, DVHs quality was slightly inferior for 3° per second gantry speed, the target conformity of 0.9 and heterogeneity of 0.08 were achievable even for the suboptimal solutions. No violations of the MLC constraints were observed throughout the complete 360 degree arc rotation for any gantry speed schedule, thereby confirming MILP dispersion model. For the two prostate cases, the results showed MP - VMAT's ability to achieve substantial dose reduction in rectum and bladder while yielding similar target coverage compared to single energy VMAT. Bladder volume was mostly spared in low-to-intermediate dose region. Rectal volume sparing (3 % to 12 %) was observed in the intermediate (from 25 to 50Gy) dose region. We demonstrate the first formalism of a large-scale simultaneous optimization of mixed photon energy beams for VMAT. Dosimetric comparison of MP - VMAT to single energy VMAT demonstrated potential advantages of using mixed photon energy beams for prostate plans, thus providing an impetus for further testing on a large clinical cohort.

Appropriate mixing speeds of Rushton turbine for biohydrogen production from palm oil mill effluent in a continuous stirred tank reactor

Palm oil mill effluent (POME) is a potential source for biohydrogen. A continuous stirred tank reactor was used to study the effects of mixing speeds of a Rushton turbine on the biohydrogen production. The initial organic loading rate and volatile solids of influent were 55 g of chemical oxygen demand per liter of influent per day and 30.28 g of volatile solids per liter of influent, respectively. The retention time was 24 h and the turbine speeds were 10, 50, 100, and 150 rpm. The corresponding hydrogen yields were 2,669, 2,868, 3,413, and 3479 mL per liter of influent. The total maximum hydrogen yield at 150 rpm was equivalent to 6958 mL per liter of POME. A speed of 100 rpm was recommended for the constant speed operation as it gave the highest net energy gain of 23.96 kJ LPOME-1. Five varying speed schemes were studied to determine the maximum net energy gain. The varying speed schedule should be 100 rpm for the first 8 h and 10 rpm for the rest 16 h, which increased the net energy gain by 11% in comparison to the 100 rpm constant mixing speed. Feasibility analysis revealed possibility in industrial application.

Analysis of the prospective vibrational energy harvesting of heavy-duty truck suspensions: A simulation approach

Automobiles are considered as a large-scale vibrational source what makes automotive is a target to harvest such a considerable kinetic energy otherwise being dissipated by traditional viscous shock absorbers. The power dissipation during the damping events can be partially regenerated based on linear or rotary electromagnetic energy-harvesting dampers. This paper analytically discusses the potential power content of a class-6 heavy-duty truck with respect to different driving circumstances. Considering variable speed schedules of heavy vehicles, 10 driving cycles are proposed in creating ISO based random road profiles with variable speed vectors including both aggressive and nonaggressive driving trips. Further, the laden and the unladen truck cases were considered in the analysis with respect to 6-DOFs truck suspension model. Additionally, the energy harvesting and truck comfort were investigated over the running conditions. Given the results, a potential power content of 71–434 W and 287–1733 are available for ISO road grades of C and D, respectively, for a fully-loaded truck. The results suggested that the higher power quantity is related to aggressive body-wheel relative movements in which such conditions are found in case of the aggressive driving events and the vehicles with greater mass such as the heavy trucks and off-road vehicles.

Vertical flight path segments sets for aircraft flight plan prediction and optimisation

ABSTRACTThe paper presents a method for constructing a set of vertical flight path segments, that would compose an aircraft's vertical flight envelope, by using an aircraft performance model. This method is intended to be used for aircraft flight plan prediction and optimisation algorithms. The goal is to reduce the volume of recurring segment performance computations currently required for flight plan prediction or optimisation. The method presented in this paper applies to a free-flight scenario. The flight-path segments composing the vertical flight envelope belong to one of the unrestricted climb, constant-speed level flight, step-climb and continuous descent segments, performed at the consigned climb, cruise and descent speed schedules and at the consigned air temperature values. The method employs an aircraft model using linear interpolation tables. Nine test scenarios were utilised to assess the performances of the resulting flight envelopes as a function of the number of cruise altitudes and descent flight paths. The set of evaluated performance parameters includes the range of total flight times and still-air flight distances, and the vertical profiles describing the minimum and maximum flight times, and still-air flight distances. The advantages of the proposed method are multiple. First, it eliminates the need for repetitive aircraft performance computations of identical vertical flight plan segments, and provides the means for quick retrieval of the corresponding performance data for use in the construction of a full flight plan. Second, the vertical flight path look-up structure and the vertical flight-path graph describe a set of vertical flight paths that consider an aircraft's and flight plan's configuration parameters and cover its maximum flight envelope. Third, the look-up structure and the graph provide the means for rapid and clear identification of the available options for constructing a flight-plan segment, as well as for detecting the points associated with changes in the flight phases, including climb, cruise, step-climb and descent.

Optimal En-Route Trajectory Planning based on Wind Information

In the aspect of Trajectory Based Operations (TBO), accurate trajectory predictions are essential for meeting future concepts defined in SESAR and NextGen visions. The 4D trajectories are results of generated flight plans by Airline Operation Centers to be executed by pilots and monitored by Air Traffic Controllers. Trajectory generation is a process with many input sources such as flight route, speed schedule, initial conditions and environmental conditions. However, the uncertainty in these states generates discrepancies between planned and actual trajectories. This paper distinctly investigates the wind uncertainty by analyzing the forecast data in airline’s operational flight plans and actual wind computed by aircraft’s onboard systems (i.e., Quick Access Recorder). The study goes through the methodology of speed and altitude optimization for cruise phase. The simulation studies reveal the impact of wind uncertainty and potential fuel saving benefits when the precise wind data is available. The case studies are based on historical planned and flown flights.