Variable Speed Research Articles

Effects of carbon versus plastic ankle foot orthoses on gait outcomes and energy cost in patients with chronic stroke.

To compare the walking performances of hemiplegic subjects with chronic stroke under 3 conditions: with a new standard carbon fibre ankle foot orthosis (C-AFO), with a personal custom-made plastic AFO (P-AFO), and without any orthosis (No-AFO). Randomized, controlled crossover design. Fifteen chronic patients with stroke (3 women and 12 men, 59 [10] years, 13 [15] years since injury). Patients performed 3 randomized sessions (with C-AFO, P-AFO, no-AFO), consisting of a 6-min walk test (6MWT) with VO2 measurement and a clinical gait analysis. Energy cost (Cw), walking speed, spatio-temporal, kinetic, and kinematic variables were measured. No significant differences were found between the C-AFO and P-AFO conditions. Distance and walking speed in the 6MWT increased by 12% and 10% (p < 0.001) and stride width decreased by -8.7% and -13% (p < 0.0001) with P-AFO and C-AFO compared with the No-AFO condition. Cw decreased by 15% (p < 0.002), stride length increased by 10% (p < 0.01), step length on affected leg increased by 8% (p < 0.01), step length on contralateral leg by 13% (p < 0.01), and swing time on the contralateral leg increased by 6% (p < 0.01) with both AFO compared with the No-AFO condition. The use of an off-the-shelf composite AFO (after a short habituation period) in patients with chronic stroke immediately improved energy cost and gait outcomes to the same extent as their usual custom-made AFO.

Evaluation of a multiphase cascaded H-bridge inverter for induction motor operation

Multi-phase systems are becoming more popular for applications requiring high power and precise motor control, even if single-phase AC power is still frequently utilized in households and some enterprises. While both systems have benefits over single-phase, there are trade-offs associated with each. Because of its balanced operation and effective power transfer, the three-phase (3-Φ) system is the most widely used multi-phase system. Nevertheless, different phase values can be investigated for particular applications where reducing torque ripple and harmonic content is essential. Using odd numbers of phases (such as 5-Φ) that are not multiples of three is one method. This design has the ability to reduce torque ripple by producing a more balanced magnetic field as compared with even-numbered phases. But adding more phases also makes the system design and control circuitry more complex. Systems with five phases (5-Φ) provide a compromise between performance and complexity. Applications such as electric ship propulsion, rocket satellites, and traction systems may benefit from their use. Nevertheless, choosing a multi-phase system necessitates carefully weighing the requirements unique to each application, taking into account elements like cost, power transmission, control complexity, and efficiency. The increasing popularity of electric vehicles and renewable energy technologies has led to the need for inverters in current electric applications. Conventional inverters provide square wave outputs, which cause the drive system to become noisy and cause harmonics. Multi-phase multilevel inverters can be used to enhance inverter functioning and produce an improved sinusoidal output. This study focuses on an induction motor drive powered by a five-phase multilevel cascaded H-Bridge inverter. With less torque and current ripples in the motor rotor, the power conversion harmonics are reduced and the switching components of the inverter are under less stress. However, in comparison to traditional inverters, it does require a greater number of legs. Because the switches needed for the cascaded H-Bridge inverter are less expensive in five-phase systems, they are favoured over higher phase orders. Furthermore, the suggested inverter removes 5th order harmonics, something that is not possible with traditional inverters. A five-phase induction motor appropriate for variable speed driving applications is also suggested by this research. Lastly, utilizing pulse width modulation (PWM) converters and an FPGA controller, an experimental study is carried out to assess the dynamic performance of the suggested induction motor drive. Particular attention is paid to the In-Phase Opposition Disposition (IPD) PWM technique.

Impact of Intersection Left Turn Guide Lines Configuration on Novice Drivers’ Behavior

Novice drivers often face challenges such as misjudgment, inappropriate steering control, distraction, and insufficient speed control when making left turns at intersections, leading to safety hazards. Installing intersection guide lines offers a solution by providing clear path directions, mitigating safety concerns associated with novice drivers’ left-turn actions. This study explored the impact of intersection guide line configurations on the driving behavior of novice drivers during left turns, utilizing large, medium, and small typical intersections to create six categories of left-turn simulation scenarios in a driving simulator. Data on vehicle trajectory, steering angle, steering speed, and eye-tracking were collected and analyzed. The study revealed that guide line arrangement significantly influences novice drivers’ left-turning behavior, enhancing path guidance while reducing trajectory and steering angle fluctuations, speed variations, and driver attention dispersion. This improvement in stability is particularly notable as intersection size and the number of left-turn lanes increase. The study’s findings offer robust theoretical support and guidance for the development and widespread adoption of intersection guide lines.

Real-time Wells turbine simulation on an oscillating-water-column wave energy converter physical model

Due to its simplicity and low cost, the Wells turbine is the most common choice for driving oscillating-water-column (OWC) wave energy converters (WECs) power take-off system. This turbine is characterized by a flow rate that is a linear function of the pressure head and inversely proportional to the rotational speed before the onset of hard stall conditions. The Wells turbine has been simulated in wave tanks using porous plugs, where the flow rate exhibits linear behaviour. However, numerical and experimental results have shown that rotational speed variations significantly influence the performance of OWC WECs. This work aims to develop a novel real-time simulator of a Wells turbine for use in physical models of OWC power plants. The proposed simulator consists of a diaphragm whose diameter is adjusted in real-time as a function of the pressure head measured in the laboratory and the rotational speed calculated by a hardware-in-the-loop model. In this way, it is possible to reproduce the full behaviour of the Wells turbine before and after a hard stall. Experimental results demonstrate the effectiveness of the simulator. A performance analysis was conducted to understand the device’s potential and compare the damping that Wells and impulse turbines introduce.

Adaptive passive fault tolerant control of DFIG-based wind turbine using a self-tuning fractional integral sliding mode control

Controlling variable wind speed turbine (VWT) system based on a doubly-fed induction generator (DFIG) is a challenging task. It requires a control law that is both adaptable and robust enough to handle the complex dynamics of the closed control loop system. Sliding mode control (SMC) is a robust control technology that has shown good performance when employed as a passive fault-tolerant control for wind energy systems. To improve the closed control loop of VWT based on DFIG with the aim of improving energy efficiency, even in presence of nonlinearities and a certain range of bounded parametric uncertainties, whether electrically or mechanically, an adaptive passive fault tolerant control (AP-FTC) based on a self-tuning fractional integral sliding mode control law (ST-FISMC) developed from a novel hyperbolic fractional surface is proposed in this paper. ST-FISMC introduces a nonlinear hyperbolic function into the sliding manifold for self-tuning adaptation of control law, while fractional integral of the control law smooths discontinuous sign function to reduce chattering. Additionally, this work introduces an adaptive observer, developed and proved based on a chosen Lyapunov function. This observer is designed to estimate variations in electrical parameters and stator flux, ensuring sensorless decoupling in indirect field- oriented control (SI-FOC) of DFIG. Lyapunov theory is also used to prove stability of states vectors in closed control loop with presence of bounded parameters uncertainties or external disturbances. Simulation results show that the proposed approach offers better performance in capturing optimal wind energy, as well as the ability to regulate active/reactive power and high resilience in presence of occurring parameter uncertainties or external disturbances.

Simulation-driven unsupervised fault diagnosis of rolling bearing under time-varying speeds

Simulation models incorporating fault mechanisms can acquire sufficient samples with rich fault information, providing an effective solution to deep learning-driven bearing fault diagnosis in case of sample scarcity. However, the simulation models in the previous studies are mainly designed for constant speed conditions and cannot generate effective source data aligning with the variable speed conditions. Moreover, the fault impacts of bearing exhibit time-varying characteristics under variable speed conditions, causing the obstacle of the fault feature representation of the deep learning model. Therefore, this article investigates an analytical fault simulation model-driven unsupervised fault diagnosis of rolling bearing under time-varying speeds. The simulation model can customize high-quality data that match the specific variable speed conditions. The proposed network can extract robust discriminable feature representation by designing multiscale enhanced temporal convolution transformer network and enables the feature alignment of the target and simulation samples under a pseudolabel guided domain adaptation training strategy. The effectiveness and superiority of the proposed method in addressing the bottleneck problems of bearing unsupervised fault diagnosis under variable speed conditions, including condition matching data generation and time-varying feature representation, is demonstrated using two different bearing datasets.

Development of a morphology-based wind speed model in the urban roughness sub-layer

Understanding near-surface wind variations in urban environments is essential for a wide range of applications, including urban weather forecasting and wind impact assessment. In this study, we introduced a novel model for mean wind speed profiles within the urban roughness sublayer based on theoretical analysis. To calibrate the model's coefficients, we conducted large-eddy simulations of airflow over a variety of idealized urban configurations. The resulting expression effectively captures wind speed variations across different urban morphologies. Subsequently, a regression modeling was employed to identify the relationships between building morphological parameters and these coefficients. This highlights the pivotal role of using building morphology to predict near-surface velocity profiles. The proposed model also yields significantly more accurate wind speed predictions within the urban canopy layer than traditional exponential profiles. The findings in the present study lay a more robust foundation for assessing urban wind conditions and improving urban-scale weather forecasts.

Fault signature extraction of rolling bearings under variable speed via time–frequency overlap group sparse representation

The high-fidelity extraction of the nonperiodic fault transients of rolling bearings under variable speed is of significant importance, while still a challenge for early-stage fault diagnosis of major equipment such as aero engines and wind turbines. A method termed continuous symmetric Laplace wavelet transform (CSLWT) enhanced time–frequency overlap group sparse (OGS) representation is proposed in this paper. A symmetric Laplace mother wavelet function is proposed which could satisfy the admissibility condition of wavelet decomposition and reconstruction, upon which the CSLWT is fabricated and adopted as the sparse representation dictionary to match the nonperiodic fault transients. Since the decomposition coefficients of nonperiodic fault impulses via the CSLWT exhibit group sparsity on the time–frequency plane with unknown intervals, the time–frequency OGS model is adopted and then solved for extraction of the nonperiodic fault transients. Moreover, a data driven, multi-objective optimization strategy is presented for determining the hyper-parameters of the sparse model. The correlated kurtosis of angular envelope signal (CK-AE) and harmonic-to-noise energy ratio of the order spectrum (HNR-OS) are introduced as the optimization objectives, and the Pareto front is solved based on the NSGA-II algorithm, upon which the acceptable solutions set is chosen from the Pareto front, and the proper hyper-parameters are further determined via proposed normalized solutions distance density sorting of the acceptable solutions set. The performance of the proposed method is validated via processing both simulated and experimental vibration signals, as well as compared with some tradition fault diagnosis methods.

Reconfigurable Robotic Exercising Companion

Regular exercise plays a crucial role in promoting overall well-being in today’s lifestyle. However, individuals often find it challenging to properly execute exercises, including maintaining correct postures and appropriate movement speeds. Robotic companions have emerged as potential solutions to assist and motivate users during exercise sessions. This research paper proposes a novel robot companion designed for exercise scenarios using a reconfigurable robot. In contrast to existing non-reconfigurable robotic companions, the use of a reconfigurable robot provides added flexibility in generating emotions. The system incorporates a module that utilizes fuzzy logic to evaluate the correctness of exercise performance based on posture variations and movement speeds. The robot generates emotions and provides feedback to users based on the exercise correctness score. The robot expresses emotions through reconfigurations, motion patterns, and variations in robot speed. This emotion-based feedback could be helpful for creating engaging and interactive exercise experiences. Apart from emotion generation, the robot utilizes vocal cues as feedback. Experimental results validate the effectiveness of the proposed system in evaluating exercise correctness and demonstrating meaningful emotion transitions. The findings of this work contribute to the development of innovative robotic companions for improving exercise adherence and overall well-being.

Optimization of Sailing Speed for Inland Electric Ships Based on an Improved Multi-Objective Particle Swarm Optimization (MOPSO) Algorithm

Sailing speed is a critical factor affecting the ship’s energy consumption and operating costs for a voyage. Inland waterways present a complex navigation environment due to their narrow channels, numerous curved segments, and significant variations in water depth and flow speed. This paper constructs a model of a ship’s energy consumption based on an analysis of ship resistance and the energy transfer relationship of ships. The K-means clustering algorithm is introduced to divide the Yangtze River waterway into multiple segments based on the similarity of navigation environments. Considering the constraints of the ship’s main engine and the desired arrival time, a multi-objective particle swarm optimization (MOPSO) algorithm, improved with cosine decreasing inertial weight and Gaussian random mutation, is employed to optimize segmented navigation speeds to achieve different goals. Finally, four cases are studied with a fully electric ship navigating the reaches of the Yangtze River. The results indicate that the optimized speed can reduce ship energy consumption by up to 6.18% and significantly reduce ship energy consumption and operational costs under different conditions.

Lightweight pyramid attention residual network for intelligent fault diagnosis of machine under sharp speed variation

Deep learning based diagnostic methods have obtained great success in intelligent fault diagnosis of machines. However, most of the existing methods are developed for fault diagnosis task under stable working conditions, and cannot handle the data generated under sharp speed variation favorably. Besides, the large model parameters and high computing costs of these methods fail to meet the requirement of small memory and fast-precise prediction in practical applications. To address these challenges, a lightweight pyramid attention residual network (PARNet) is proposed in this paper. First, an efficient pyramid squeeze convolution module is proposed to capture multi-scale information from raw signals. Then, a soft squeeze-and-excitation attention is designed to establish long-range channel dependency, and fuse features of different scales. Based on the above improvements, a pyramid attention residual block is proposed as basic feature learning unit, and a concise CNN-based model PARNet is built using PAR block. Extensive experiments in two case studies are conducted to validate the effectiveness and adaptability of PARNet under different speed variation conditions. The test results demonstrate the superiority of the proposed method for machine fault diagnosis and reliability assessment in terms of recognition accuracy and model parameter complexity, compared with other advanced diagnostic methods.

Development and experimental validation of a hydraulic design and control philosophies for 5th generation district heating and cooling networks

Sector coupling and energy sharing are key elements in effectively decarbonising the thermal grid. 5th Generation District Heating and Cooling (5GDHC) provides a system for combining heating and cooling via an ambient temperature network. A main design challenge for 5GDHC is establishing a control regime that allows flow bidirectionality and energy synergies between heating and cooling. This work develops and experimentally validates a 5GDHC system wide hydraulic design and two fitting control strategies on a small scale. One has fixed grid return temperatures (TGridFix) while the other free floating grid temperatures (TGridFloat). Both feature decentralised variable speed pumping and a centralised passive balancing unit aimed at alleviating control instabilities arising from pump hunting. Experiments showed that TGridFix demonstrates slightly higher electrical consumption for the booster heat pump (Seasonal Coefficient of Performance of 3.84 compared to 4.16 for 20 h of operation) due to a mismatch between the evaporator and the grid temperature difference. Overall, TGridFix can lead to low prosumer interaction and better system wide predictability. Further considerations on generalisability of findings and full-scale implementations are highlighted. This works presents a set of detailed and experimentally validated control philosophies for 5GDHC systems, elucidating a key system implementation challenge.

A new approach to instantaneous power based control of DFIG using active disturbance rejection control

This paper presents the design and control of a wind energy converter system that uses a doubly fed induction generator. It introduces a model that incorporates instantaneous active and reactive power as state variables to represent both the stator and rotor sides of the doubly fed induction generator. Subsequently, a linear active disturbance rejection-based control regulator is proposed for the control loops. The parameters of the linear active disturbance rejection controllers (ADRC) are adjusted by two methods: the single parameter tuning approach and the genetic algorithm (GA) method. The performance of the suggested controller is ultimately compared with a tuned proportional-integral (PI) control regulator in the MATLAB environment. In order to examine the performance of the system, various test scenarios are taken into account. These scenarios include different percentages of load change in both sub-synchronous and super-synchronous modes of operation, faults with different fault periods are considered in both modes of operation. The conventional PI controller-based approach requires 35 control cycles to reach steady state under load change conditions and 50 control cycles under symmetrical fault conditions, regardless of whether the system is operating in sub synchronous or super synchronous mode. The single parameter-based tuned ADRC approach only requires 15 control cycles for load change and 20 control cycles for symmetrical fault scenarios. In contrast, for both the load change and symmetrical fault scenarios, the ADRC with GA-based parameter tuning reaches steady state in just 5 control cycles. Furthermore, a case involving wind speed variation is presented to demonstrate the maximum power point tracking capability of the proposed control structure. Similarly, for the robustness test of the control regulator, variations in resistance and inductance are introduced to observe their impact on the response of the control regulators. Finally, a real-time hardware in loop setup is presented to validate the control strategy during the load change in the sub-synchronous mode of operation of the doubly fed induction generator.

Research on engine dynamic torque modeling for torsional vibration analysis of vehicle powertrain

In order to improve the accuracy of dynamic torque model of engine, a simulation model of quasi-transient engine dynamic torque under all operating conditions was established considering the influence of throttle opening. Firstly, the calculation model of engine output torque and cylinder pressure is established according to the kinematic relation of crank linkage mechanism. Secondly, BP neural network is used to establish the mapping relationship model of throttle opening, rotational speed, and cylinder pressure ratio data. In order to solve the contradiction between calculation accuracy and efficiency of engine cylinder pressure interpolation, a quadratic interpolation method is proposed, which refines data to improve interpolation accuracy, and improves calculation efficiency by online adjacent interpolation, and completes the construction of dynamic torque simulation model of engine in all working conditions. Finally, the experimental data of dynamic torque characteristics of the engine at different speeds are used to verify the accuracy of the model calculation. By using the verified engine model, the dynamic operating characteristics of variable valve opening and variable speed are simulated and analyzed. The results show that the model established in this paper can effectively reflect the dynamic characteristics of the engine, and the model is suitable for the simulation of the torsional vibration of the vehicle powertrain under steady and unsteady conditions.

Thermal and electrical performance analysis of a lightweight micro CHP system for recreational vehicles

For comfortable use of a Recreational Vehicle (RV), there is a need to supply electrical energy to recharge the batteries and supply the installed equipment, as well as thermal energy, for hot water and ambient heating. A conventional solution for supplying electrical energy is the installation of photovoltaic modules combined with connection to an electrical grid, when possible. Water heating is generally done using a passage gas heater or hybrid gas/electric storage heater. Imported vehicles have gas heating and many other vehicles have diesel heaters. To develop an alternative to currently available energy supply methods, this work proposes the creation of a cogeneration system for RVs. From a literature review, a research gap was identified in relation to micro CHP devices for RVs. In the development of this work, a micro CHP prototype was created, with 980 W of maximum electrical power, consisting of a single-cylinder internal combustion engine, an automotive alternator and heat exchangers for heat recovery. The prototype was enclosed and instrumented, allowing to evaluate the thermal power obtained in water flowing through exhaust gases, lubricating oil and cooling air heat recovery systems, in addition to measuring the electrical power of the alternator. Operational tests were carried out with 46 different test conditions, combining variations in the engine speed (N) and in electrical load. The prototype electrical efficiency reached 10.18 % ±0.2 % at 3008 rpm with 83 % load. The maximum electrical power was 0.746 kW±0.015 kW at 3637 rpm and 100 % load. The maximum thermal energy recovery rate was 3.267 kW±0.039 kW. Utilization factor reached 65.57 % ±0.33 % at 3208 rpm with 63 % load. Compared to traditional means of providing electrical power and thermal power for RVs, the prototype proved to be more advantageous when the demand is for electrical power and space heating. However, for conditions in which the demand is for electrical power and water heating, or just electrical power, the current solutions available are still more efficient.

GIS-Based Assessment of Best Route in Complex Traffic Environment: A Case of Kolkata Municipal Corporation

In an emergency like an urban fire, road accident, or critical condition of patients, the shortest possible route to reach the destination is one of the most sought-after issues in transportation studies. However, the physical distance of the shortest route becomes longer distance than the travel time distance due to traffic congestion in metropolitan cities like that of Kolkata. So, the selection of an alternative route is essential to organize the journey logically. A well-structured road network provides multiple options to road users for selection of the best route from the shortest and alternative route according to their demand. So, the present study aims to identify the structural characteristics of road networks by graph theory and the selection of the best route using the GIS technique. It has been found that the road network of Kolkata is characterized by complex structure and grid configuration of the network. Selection of the best route also ranges from the shortest route to alternative route due to spatial variation of traffic speed on different roads in peak hours. The current study identifies the optimal route in terms of travel time to reach the required destinations during peak hour duration in the city of Kolkata.

Horizontal rearrangement frequency domain chirplet transform: algorithm and applications

Abstract Based on the idea of optimal time–frequency analysis (TFA) theory, a time–frequency post-processing method called horizontal rearrangement frequency domain chirplet transform (HRFCT) is proposed. The proposed method is designed to analyze non-stationary transient signals with nonlinear group delay (GD), aiming at obtaining energy-concentrated and accurate time–frequency representation (TFR). First, based on the image characteristics of the constructed amplitude function, the selection criteria of GD are defined, that is, the local maximum of amplitude function and the local minimum of absolute value of derivative of amplitude function are calculated. Next, according to the calculated GD, the coefficients on the GD trajectory are adaptively extracted, and the coefficients that cause the TFR to be blurred are discarded. Finally, the analysis result of a two-component simulation signal demonstrates the satisfactory performance of the HRFCT, and compared with four advanced TFA methods, the competitiveness of the HRFCT is verified. Additionally, the application of the HRFCT in the processing of constant and variable speed bearing signals highlights its prospects in transient signal analysis and rotating machinery condition monitoring.

Frontoparietal-Striatal Network and Nucleus Basalis Modulation in Patients With Parkinson Disease and Gait Disturbance.

Neural computations underlying gait disorders in Parkinson disease (PD) are multifactorial and involve impaired expression of stereotactic locomotor patterns and compensatory recruitment of cognitive functions. This study aimed to clarify the network mechanisms of cognitive contribution to gait control and its breakdown in patients with PD. Patients with PD were instructed to walk at a comfortable pace on a mat with pressure sensors. The characterization of cognitive-motor interplay was enhanced by using a gait with a secondary cognitive task (dual-task condition) and a gait without additional tasks (single-task condition). Participants were scanned using 3-T MRI and 123I-ioflupane SPECT. According to gait characteristics, cluster analysis assisted by a nonlinear dimensionality reduction technique, t-distributed stochastic neighbor embedding, categorized 56 patients with PD into 3 subpopulations. The preserved gait (PG) subgroup (n = 23) showed preserved speed and variability during gait, both with and without additional cognitive load. Compared with the PG subgroup, the mildly impaired gait (MIG) subgroup (n = 16) demonstrated deteriorated gait variability with additional cognitive load and impaired speed and gait variability without additional cognitive load. The severely impaired gait (SIG) subgroup (n = 17) revealed the slowest speed and highest gait variability. In addition, group differences were found in attention/working memory and executive function domains, with the lowest performance in the SIG subgroup than in the PG and MIG subgroups. Using resting-state functional MRI, the SIG subgroup demonstrated lower functional connectivity of the left and right frontoparietal network (FPN) with the caudate than the PG subgroup did (left FPN, d = 1.21, p < 0.001; right FPN, d = 1.05, p = 0.004). Cortical thickness in the FPN and 123I-ioflupane uptake in the striatum did not differ among the 3 subgroups. By contrast, the severity of Ch4 density loss was significantly correlated with the level of functional connectivity degradation of the FPN and caudate (left FPN-caudate, r = 0.27, p = 0.04). These findings suggest that the functional connectivity of the FPN with the caudate, as mediated by the cholinergic Ch4 projection system, underlies the compensatory recruitment of attention and executive function for damaged automaticity in gait in patients with PD.

A two-step bearing fault diagnosis strategy under variable speed based on symplectic geometry modal decomposition and practical fault feature extraction framework

Abstract Strong noise interference can lead to failure of bearing fault diagnosis techniques. This paper proposes a two-step fault diagnosis strategy to address the challenge of weak fault feature extraction in bearing fault diagnosis using acoustic or vibration data at varying speed. Firstly, the paper introduces a short-time symplectic modal decomposition (stSGMD) method that utilizes fractional Fourier transform. This method involves signal processing with short-time windowing to extract fault-sensitive components. The window is then expanded to obtain the complete component through fractional Fourier narrow-band filtering based on energy concentration in the fractional Fourier domain. A novel entropy index, standard deviation discrete entropy, is introduced to quantify the intensity of fault shocks in non-stationary signal and is used to select components in the stSGMD. Subsequently, a fault feature extraction framework called global objective deconvolution (GOD) is presented for extracting instantaneous fault features at varying speed. This method establishes a global objective matrix for the extraction process. The GOD is utilized to deconvolute the complete fault-sensitive component, followed by envelope order analysis for demodulating the fault feature order. Numerical simulations and experimental studies on acoustics and vibration are performed. The results demonstrate that stSGMD improves the demodulation capability of SGMD, while GOD effectively extracts fault features. It is expected that the presented method will be effectively utilized for fault feature extractions in bearings operating under linear variable speed conditions.

The green cold vehicle routing problem with traffic congestion and variable speed

The green cold vehicle routing problem with traffic congestion and variable speed