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Voltage rise caused by high levels of distributed generation is manifesting as voltage regulation challenges for many electricity network service providers. In this environment it would be ideal to reduce supply voltage magnitudes, however, many network operators are hesitant to do so due to concerns related to consumer appliance performance at reduced supply voltage magnitudes. Voltage regulation requirements are defined by network standards and network service providers must ensure voltages remain within specified limits. Through an evaluation of domestic appliance performance when supplied at various voltage magnitudes, this paper examines the impact of varying voltage levels on residential appliances. Equipment energy demand, operation and actuation were monitored for each applied voltage magnitude. While no equipment failures were recorded, appliance behaviour varied significantly with applied voltage magnitude. Individual appliance conservation voltage reduction (CVR) factors have also been established. The results highlight the importance of good voltage regulation and provide substantiated appliance performance figures for future studies. The outcomes of this paper allow electricity network service providers to understand the implications of supply voltage magnitude on domestic appliance performance, whether it be understating of the impact of higher voltage magnitudes caused by distributed generation or implications of reducing voltage magnitudes to provide headroom for distributed generation integration. [ABSTRACT FROM AUTHOR]

This paper studies the three-dimensional (3D) trajectory optimization problem for unmanned aerial vehicle (UAV) aided wireless communication. Existing works mainly rely on the kinematic equations for UAV’s mobility modeling, while its dynamic equations are usually missing. As a result, the planned UAV trajectories are piece-wise line segments in general, which may be difficult to implement in practice. By leveraging the concept of state-space model, a control-based UAV trajectory design is proposed in this paper, which takes into account both of the UAV’s kinematic equations and the dynamic equations. Consequently, smooth trajectories that are amenable to practical implementation can be obtained. Moreover, the UAV’s controller design is achieved along with the trajectory optimization, where practical roll angle and pitch angle constraints are considered. Furthermore, a new energy consumption model is derived for quad-rotor UAVs, which is based on the voltage and current flows of the electric motors and thus captures both the consumed energy for motion and the energy conversion efficiency of the motors. Numerical results are provided to validate the derived energy consumption model and show the effectiveness of our proposed algorithms. [ABSTRACT FROM AUTHOR]

The traction transformer of a high-speed railway is long-term subjected to the effects of impulse traction loads, such that the aging status of a multilayer insulation paper between high and low voltage windings is non-uniformly distributed due to the non?uniform distribution of temperature. To enhance the use of the frequency domain dielectric spectroscopy (FDS) method in engineering, this paper studies the effects of non-uniform thermal aging on the frequency domain dielectric spectrum of multilayer insulation paper. Based on the experimental designs and preparation of experimental materials, the frequency domain dielectric spectrum of a multilayer insulation paper with uniform and non-uniform thermal aging distribution was tested. Afterwards, the effects of non-uniform thermal aging on the frequency domain dielectric spectrum of multilayer insulation paper were discussed and analyzed based on the tested results. The results show that: 1) The effects of the non-uniform distribution of DP (cellulose structure) can be ignored. The non-uniform distribution of the aging byproducts (except moisture) has limited effects on the tested results, which is far less than the effects of moisture. The non-uniform distribution of moisture is the dominant factor and affects the results. 2) In terms of insulation paper with a defined aging status, its order in the multilayer insulation paper does not affect the tested results. 3) The tested results of the insulation samples with a non-uniform aging distribution can no longer reflect the moisture content of the wettest section, and the evaluated moisture content is slightly higher than the average moisture content of the multilayer insulation paper. [ABSTRACT FROM PUBLISHER]

In this paper, the nature of thermally upgraded Kraft electrical insulating papers and their characterization by measurement of nitrogen content is discussed. A historical context and explanation of the basics of the various chemical methods used to measure nitrogen content in electrical papers is presented. The Kjeldahl and Dumas methods for quantifying nitrogen content were compared using six insulating papers in different laboratories in an effort to correlate the results obtained with both techniques. The results provide an insight into the viability of the Dumas combustion method as a proposed industry standard test for measuring the nitrogen content of paper. Moreover, data on the accuracy, reproducibility, and repeatability of the Dumas method is provided for the analysis of thermally upgraded electrical papers. [ABSTRACT FROM PUBLISHER]

One of the key design problems of wireless sensor networks is to reduce energy consumption and improve node survival. Because the battery in sensor nodes is difficult to replace and the energy is limited, the design of energy-saving routing protocol becomes very important. Aiming at the problems of limiting node energy and shortening network lifetime in wireless sensor networks, a WSNs routing protocol based on improved archimedes optimization algorithm (IAOAR) is proposed in this paper. Firstly, in the cluster establishment phase, the protocol defines different fitness functions according to the energy, number and distance of sensor nodes to select the appropriate initial cluster. Then the fitness value of the node is calculated, and the weight is dynamically updated according to the number of iterations to improve the optimization ability of AOA algorithm. Virtual force is introduced to adjust the selected cluster head position to ensure the selection of optimal cluster heads, reduce the transmission energy consumption of common sensor nodes. In the data transmission stage, combined with the improved ant colony algorithm, the shortest transmission path to the sink node is constructed to reduce the energy consumption of long-distance data transmission at the cluster head. The simulation results showed that compared with LEACH, E-LEACH, LEACH-ANT and MAXLEACH protocols, the network life cycle of the protocol proposed in this paper was improved by 140.55%, 107.09%, 99.24% and 96.76% respectively. It effectively reduced the speed of node death, balances the network energy consumption and prolonged the network lifetime. [ABSTRACT FROM AUTHOR]

The paper proposed a novel intelligent transmission surface (ITS) aided transmitter in an intelligent reflection surface (IRS) assisted multiuser multiple-input multiple-output (MIMO) network. The ITS deployed in the transmitter architecture can reduce the power consumption in signal beamforming at the base station (BS), and the IRS can help the information transfer from the ITS-aided transmitter to the users. We first maximize the weighted sum rate (WSR) of the users by jointly designing the beamforming vector at the BS and the phase shifts of ITS and IRS. To solve this non-convex optimization problem, we propose an effective algorithm in which the Lagrangian dual transform, the alternative optimization (AO) algorithm and the quadratic transform (QT) method are adopted to simplify the objective function. Then, the bisection search and the alternating direction method of multipliers (ADMM) algorithm are considered to design the optimal beamforming vector and phase shifts of ITS and IRS, respectively. Furthermore, the paper explores the energy efficiency (EE) maximization problem to emphasize the value of the ITS-assisted transmitter in terms of power savings. Finally, we compare the simulation results to various state-of-the-art techniques to see how much better the proposed algorithm is in terms of WSR and EE. [ABSTRACT FROM AUTHOR]

Although smart home has received wide attention in recent years, numerous scholars focus more on energy optimization strategy than energy dispatch hardware device (named energy router). Meanwhile, this energy router should have several features, i.e., high renewable energy utilization, energy multi-port and low volume. Thus, this paper designs a nine-port energy router regarding smart home and proposes a multimode hierarchical management strategy for this energy router. First, for the multi-port demand of wind, solar, storage and utilization, this paper presents a nine-port energy router to improve the renewable energy consumption and power supply flexibility. In addition, to reduce the volume of the energy router, a non-isolated AC/DC hybrid topology is constructed through embedding the integrated power electronic converters, which achieves the miniaturization of the energy router. In order to improve the renewable energy utilization rate, the decentralized module control is proposed for the components of energy router to provide the voltage and frequency support for system, and realizes the power sharing of distributed generations (DGs). Furthermore, the power exchange control with three-mode switching is proposed to guarantee the global energy flow balance under complex conditions. Eventually, the feasibility of the energy router is verified by the simulation and experiments. [ABSTRACT FROM AUTHOR]

This paper presents a new approach for solving the optimal merging control problem for hybrid electric vehicles (HEVs) under a connected environment. To achieve a reduction in energy consumption and save travel time, this paper focuses on deriving a decentralized feedback control law that provides not only the optimal velocity trajectory for merging but also a torque distribution strategy for the HEV powertrain. For this purpose, a distance domain-based optimal control problem is first proposed to avoid a free end-time cost function formulation that usually arises due to considering the minimization of traveling time. Then, the vehicle dynamics take into account the constraint of the optimization problem to evaluate the energy consumption at the power device level instead of the acceleration, unlike the common method used for reducing energy consumption in previous studies of merging control with linear models. The proposed optimization problem is solved by Pontryagin’s maximum principle, and a traffic-in-the-loop powertrain simulation platform with a real-world emulated traffic scenario and high-fidelity HEV powertrain model is constructed to eliminate the randomly generated merging scenario. Finally, the simulation results obtained on the platform are demonstrated to validate the effectiveness of the proposed decentralized merging control law. [ABSTRACT FROM AUTHOR]

The opening up of the railway to competition in Europe has been accompanied by many technical challenges never before seen in the railway field. One of these challenges concerns precise energy metering. As European railways have been a monopoly until now, the current systems can only measure the energy consumption of a single operator. In accordance with European law, the installation of an energy meter has been mandatory in new or refurbished rolling stocks since 2014. Nevertheless, these energy meters are still not fully reliable, with misreadings and missing readings being common occurrences. To address the problem of energy metering data reliability, this paper presents a method for validating the reading measurement based on a physical model and validates it in two different case studies. For the issue of energy metering data availability, different estimators are developed, and their performance are shown with the aim of calculating the missing consumptions for both case studies. Finally, the paper suggests how these methods may be implemented in other types of trains. [ABSTRACT FROM AUTHOR]

Energy management strategy (EMS) is the key to the performance of fuel cell / battery hybrid system. At present, reinforcement learning (RL) has been introduced into this field and has gradually become the focus of research. However, traditional EMSs only take the energy consumption into consideration when optimizing the operation economy, and ignore the cost caused by power source degradations. It would cause the problem of poor operation economy regarding Total Cost of Ownership (TCO). On the other hand, most studied RL algorithms have the disadvantages of overestimation and improper way of restricting battery SOC, which would lead to relatively poor control performance as well. To solve these problems, this paper establishes a TCO model including energy consumption, equivalent energy consumption and degradation of power sources at first, then adopt the Double Q-learning RL algorithm with state constraint and variable action space to determine the optimal EMS. Finally, using hardware-in-the-loop platform, the feasibility, superiority and generalization of proposed EMS is proved by comparing with the optimal dynamic programming and traditional RL EMS and equivalent consumption minimum strategy (ECMS) under both training and unknown operating conditions. Results prove that the proposed strategy has high global optimality and excellent SOC control ability regardless of training or unknown conditions. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS) is regarded as a promising technology with great potential to boost wireless networks. Affected by the “double fading” effect, however, conventional passive RIS cannot bring considerable performance improvement when users are not close enough to RIS. Recently, active RIS is introduced to combat the double fading effect by actively amplifying incident signals with the aid of integrated reflection-type amplifiers. In order to reduce the hardware cost and energy consumption due to massive active components in the conventional fully-connected active RIS, a novel hardware-and-energy efficient sub-connected active RIS architecture has been proposed recently, in which multiple reconfigurable electromagnetic elements are driven by only one amplifier. In this paper, we first develop an improved and accurate signal model for the sub-connected active RIS architecture. Then, we investigate the joint transmit precoding and RIS reflection beamforming (i.e., the reflection phase-shift and amplification coefficients) designs in multiuser multiple-input single-output (MU-MISO) communication systems. Both sum-rate maximization and power minimization problems are solved by leveraging fractional programming (FP), block coordinate descent (BCD), second-order cone programming (SOCP), alternating direction method of multipliers (ADMM), and majorization-minimization (MM) methods. Extensive simulation results verify that compared with the conventional fully-connected structure, the proposed sub-connected active RIS can significantly reduce the hardware cost and power consumption, and achieve great performance improvement when power budget at RIS is limited. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) provides a novel computing paradigm to satisfy the increasing computation requirements of mobile applications. In MEC-enabled intelligent transportation systems (ITS), the latency-sensitive computing tasks are offloaded to RSUs for execution, reducing the transmission latency compared with the cloud solutions. However, the repetitive executions of the same tasks whose outputs are dependent on the inputs lead to the extra system latency, an alternative is to cache the required services on RSUs in advance. The service requirements of latency-sensitive computing tasks are satisfied by jointly considering computation offloading and service caching. Besides, the digital twin (DT) is utilized to construct the virtual world reflecting the physical world in real-time to efficiently make offloading strategies. In this paper, a computation offloading and service caching method using decision theory in ITS with DT, named CODT, is proposed. Specifically, the computation offloading and service caching in ITS is modeled first with DT. Then, a mixed-integer nonlinear programming (MINLP) problem is formulated to minimize the system latency. Afterward, the decision theory is used to analyze the utilities of offloading strategies in different states of RSUs and make the optimal strategy. Finally, extensive simulations based on the real-world datasets demonstrate that the proposed CODT outperforms other baselines. [ABSTRACT FROM AUTHOR]

This paper presents a computation-efficient stochastic dynamic programming algorithm for solving energy storage price arbitrage considering variable charge and discharge efficiencies. We formulate the price arbitrage problem using stochastic dynamic programming and model real-time prices as a Markov process. Then we propose an analytical solution algorithm using a piecewise linear approximation of the value-to-go function. Our solution algorithm achieves extreme computation performance and solves the proposed arbitrage problem for one operating day in less than one second on a personal computer. We demonstrate our approach using historical price data from four price zones in New York Independent System Operator, with case studies comparing the performance of different stochastic models and storage settings. Our results show that the proposed method captures 50% to 90% of arbitrage profit compared to perfect price forecasts. In particular, our method captures more than 80% of arbitrage profit in three out of the four price zones when considering batteries with more than two-hour duration and realistic degradation cost. [ABSTRACT FROM AUTHOR]

The multi-hop protocols are proved effective in the railway disaster wireless monitoring system. However, farther transmission distance with the larger data will decline the valid lifetime and reliability of the system. Most existing studies focused primarily on the communication protocols optimization, and some works tried to utilize the limited computation ability at the network-level or node-level, which are insufficient for the stiff disaster information monitoring demands. This paper presents an adaptive hybrid computation and communication strategy to fully taking advantage of the sensor processing ability, and improve the energy efficiency at the link-level. Furthermore, an adaptive optimization model is designed to meet the different monitoring demands of the system, and the valid lifetime is improved accordingly. Numerical examples with various operational scenarios are developed to demonstrate the superiority and practicality of the proposed protocol in the lifetime improvement, energy consumption minimization and equalization compared with other outstanding protocols. [ABSTRACT FROM AUTHOR]

This paper proposes a cooperative optimal power split (COPS) method for a group of intelligent electric vehicles with battery/supercapacitor hybrid energy storage systems (HESSs). To achieve good performance, the proposed COPS method is made up of the upper and lower layers: the upper layer aims at obtaining the optimal power demand sequence and sending it to the lower layer; the lower layer attempts to optimize the power split for HESS. Firstly, to ensure the performance of the proposed method in practice, the resistance of the battery and supercapacitor packs as well as the DC/DC converter efficiency are not assumed to be constants, but modeled well in the paper. Secondly, to achieve the optimal power demand sequence, an upper power demand sequence optimization problem is formulated based on distributed model predictive control (DMPC), in which energy demand is incorporated into the cost function. Thirdly, after receiving the optimal power demand sequence, a hybrid power split optimization strategy is presented to obtain the optimal power split, in which the lower HESS power split optimization problem is formulated based on DMPC to decrease battery aging process and energy consumption. Finally, an improved particle swarm optimization algorithm with multiple dynamic populations is used to solve the formulated upper and lower optimization problems. Simulation results demonstrate that, compared with the benchmark, the proposed COPS method can significantly extend battery lifespan and slightly decrease energy consumption. [ABSTRACT FROM AUTHOR]

In this paper, a traction motor for maximum power 76kW electrical vehicle (EV) was designed, and the design results were verified through experiments. One of the important design objectives of the traction motor for EV is to improve energy efficiency. The energy efficiency of a traction motor affects the mileage of electric vehicles (EVs). Among the design factors influencing energy efficiency, shape parameters include torque per rotor volume (TRV), torque density (TD) and shape ratio (SR). These shape parameters have an impact on the motor's phase back EMF, inductance, winding resistance and core loss resistance, eventually affecting the energy efficiency in the entire operating range of the motor. In this paper, a New European Driving Cycle (NEDC) was applied to calculate the energy efficiency of the traction motor for EV. Changes in motor parameters such as phase back electromotive force (EMF), inductance, winding resistance and core loss resistance were identified according to variations in shape parameters. In addition, the motor characteristics according to the changes of these parameters were confirmed, and shape parameters to improve energy efficiency were selected. Since the traction motor designed in this paper adopted a water cooling method, thermal analysis was performed by reflecting the water cooling method in the thermal equivalent circuit (TEC). In order to confirm the reliability of the analysis method and design, a load test was conducted on the entire operating range to calculate energy efficiency, and a temperature test was performed to compare the test results with the analysis results. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) is an emerging architecture for accommodating latency sensitive virtualized services (VSs). Many of these VSs are expected to be safety critical, and will have some form of reliability requirements. In order to support provisioning reliability to such VSs in MEC in an efficient and confidentiality preserving manner, in this paper we consider the joint resource dimensioning and placement problem for VSs with diverse reliability requirements, with the objective of minimizing the energy consumption. We formulate the problem as an integer programming problem, and prove that it is NP-hard. We propose a two-step approximation algorithm with bounded approximation ratio based on Lagrangian relaxation. We benchmark our algorithm against two greedy algorithms in realistic scenarios. The results show that the proposed solution is computationally efficient, scalable and can provide up to 30 percent reduction in energy consumption compared to greedy algorithms. [ABSTRACT FROM AUTHOR]

Hybrid automatic repeat request (HARQ) is a key enabler for ultrareliable communications. This paper optimizes transmit power for the initial transmission and the subsequent retransmissions of HARQ with either incremental redundancy or Chase combining, aiming to minimize the expected energy consumption given the target outage probability and the target latency. The main challenge is due to the fact that the outage probability is a complicated function of the power variables which are nested in successive convolutions. The existing works mostly use a classic upper bound to approximate the outage probability by assuming unbounded transmit power, then convert the original problem to a geometric programming (GP) problem. In contrast, we propose a novel and much tighter upper bound by taking the practical power limit into consideration. The new bound and the resulting new GP method are further extended to a broader group of channel models with various fading, multiple antennas, and multiple receivers. As shown in simulations, the GP method based on the new bound significantly outperforms the existing strategies that either fix transmit power or optimize power by the classic bounding technique. [ABSTRACT FROM AUTHOR]

The unmanned aerial vehicle (UAV) technique provides a potential solution to scalable wireless edge networks. This paper uses two UAVs, with accelerated motions and fixed altitudes, to realize a wireless edge network, where one UAV forwards downlink signals to user terminals (UTs) distributed over an area while the other one collects uplink data. The conditional average achievable rates, as well as their lower bounds, of both the uplink and downlink transmission are derived considering the active probability of UTs and the service queues of two UAVs. In addition, a problem aiming to maximize the energy efficiency of the whole system is formulated, which takes into account communication related energy and propulsion energy consumption. Then, we develop a novel multi-agent Q-learning (MA-QL) algorithm to maximize the energy efficiency, through optimizing the trajectory and transmit power of the UAVs. Finally, simulation results are conducted to verify our analysis and examine the impact of different parameters on the downlink and uplink achievable rates, UAV energy consumption, and system energy efficiency. It is demonstrated that the proposed algorithm achieves much higher energy efficiency than other benchmark schemes. [ABSTRACT FROM AUTHOR]

Due to the lack of improper access control policies and decentralized access controllers, security and privacy-aware peer-to-peer (P2P) energy trading among electric vehicles (EVs) and the smart grid is challenging. Most of the solutions reported in the literature for P2P energy trading are based upon centralized controllers having various security flaws resulting in their limited applicabilities in real-world scenarios. To handle these issues, in this paper, we propose a P2P energy trading scheduling scheme called as P2P Energy Trading Scheduling (PETS) using blockchain technology. PETS is based on real-time energy consumption monitoring for balancing the energy gap between service providers (SPs), i.e., smart grids and service consumers, i.e., EVs. In PETS, the Stackelberg game theory-based 1-leader multiple-followers scheme is proposed to depict the interactions between EVs and the SP. The selection of the leader among all SPs is made using a second-price reverse auction. As per the announced energy price by the leader, EVs manage energy consumption by minimizing their energy bills. In PETS, on the leader’s side, we propose the Genetic algorithm to maximize its profit. In contrast, on the followers’ side, i.e., EVs, we use the Stackelberg Equilibrium to minimize their energy bills. Simulation results demonstrate that the proposed PETS scheme outperforms the existing state-of-the-art schemes using various performance evaluation metrics. Specifically, it reduces the peak-to-average ratio (PAR) by 12.5% of EVs’ energy load in comparison to the existing state-of-the-art scheme. [ABSTRACT FROM AUTHOR]

As a flexible and reliable way for distributed energy consumption and integration, the converters-dominated microgrid has attracted more and more attention recently. Owing to the low inertia and high nonlinearity of power converters, the islanded microgrid under large-signal disturbances is easily suffer to transient stability problems. To support the stable operation of microgrid, grid-forming converters are extensively investigated and applied. In this paper, an improved equal area criterion (IEAC) is proposed for the transient stability analysis of a simple microgrid equipped with both grid-following and grid-forming converters. Firstly, a simplified second-order model of the microgrid is established, which contains a nonlinear damping term relying on the power angle. Then, the limitation of conventional equal area criterion (EAC) for transient stability analysis is revealed, which completely ignores the unfavorable influence of negative damping and may lead to erroneous judgement. To fill this gap, an improved equal area criterion is proposed to derive the transient stability conditions for islanded microgrid, which could improve the estimation accuracy of stability boundaries. Moreover, impacts of system parameters and controller parameters on transient stability are studied, which provides useful guidelines for the parameter optimization of grid-forming and grid-following converters. Eventually, simulation and hardware-in-loop experiments are conducted to verify the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

With the new feature of multi-energy coupling and the advancement of the energy market, Energy Internet (EI) has higher requirements for the efficiency and applicability of integrated energy response. This paper proposes an indirect multi-energy transaction (IMET) to promote multi-energy collaborative optimization in local energy market (LEM) and improve energy utilization through personalized responses from We-Energies (WEs). Firstly, an indirect customer-to-customer multi-energy transaction is modeled for local multi-energy coupling market which can satisfy privacy, preference and autonomy of users. The efficiency of energy matching can be promoted through the participation of conversion devices. In addition, multi-time scale hybrid trading mechanism is constructed with the consideration of the transmission speed of different energy sources. Meanwhile, energy transaction process is built as a Markov decision process (MDP) with deep reinforcement learning algorithm so that the system modeling error can be successfully avoided. Furthermore, a distributed training structure is utilized to obtain more experience for a wider range of scenarios. The results of numerical simulations demonstrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a multi-vehicle multi-task non-orthogonal multiple access (NOMA) assisted mobile edge computing (MEC) system with passive eavesdropping vehicles is investigated. To heighten the performances of edge vehicles, we propose a vehicle grouping pairing method, which utilizes vehicles near the MEC to assist edge vehicles as full-duplex relays. For promoting transmission security, we employ artificial noise to interrupt eavesdropping vehicles. Furthermore, we derive the approximate expression of the secrecy outage probability of the system. The combined optimization of vehicle task division, power allocation, and transmit beamforming is formulated to minimize the total delay of task completion for edge vehicles. Then, we design a power allocation and task scheduling algorithm based on the genetic algorithm to solve the mixed-integer non-linear programming problem. Numerical results demonstrate the superiority of our proposed scheme in terms of system security and transmission delay. [ABSTRACT FROM AUTHOR]

Plane spiral orbital angular momentum (PSOAM) mode-groups (MGs) and multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) serve as two emerging techniques for achieving high spectral efficiency (SE) in the next-generation networks. In this paper, a PSOAM MGs based multi-user MIMO-NOMA system is studied, where the base station transmits data to users by utilizing the generated PSOAM beams. For such scenario, the interference between users in different PSOAM mode groups can be avoided, which leads to a significant performance enhancement. We aim to maximize the energy efficiency (EE) of the system subject to the constraints of the total transmission power and the minimum data rate. This designed optimization problem is non-convex owing to the interference among users, and hence is quite difficult to tackle directly. To solve this issue, we develop a dual layer resource allocation algorithm where the bisection method is exploited in the outer layer to obtain the optimal EE and a resource distributed iterative algorithm is exploited in the inner layer to optimize the transmit power. Besides, an alternative resource allocation algorithm with Deep Belief Networks (DBN) is proposed to cope with the requirement for low computational complexity. Simulation results verify the theoretical findings and demonstrate the proposed algorithms on the PSOAM MGs based MIMO-NOMA system can obtain a better performance comparing to the conventional MIMO-NOMA system in terms of EE. [ABSTRACT FROM AUTHOR]

6G wireless networks have raised increasing attention with computation-sensitive services such as AI Internet of things (AIoT) and mobile augmented reality/virtual reality (AR/VR) applications. Mobile edge computing (MEC) provides rich computation resources for user equipments (UE) at the edge of networks. Aided by MEC servers, computation-intensive applications that are commonly modeled as Directed Acyclic Graphs (DAG) can be performed locally and offloaded to MEC servers to enhance execution efficiency. However, it is a key issue to efficiently provide low latency with limited energy. In this paper, we investigate a multiobjective task scheduling problem in MEC-aided 6G network. Then, an improved multiobjective cuckoo search (IMOCS) algorithm is proposed to deal with a DAG-based task scheduling problem, which aims to reduce the execution latency and energy consumption of UE. Particularly, the proposed IMOCS algorithm is based on the single-objective cuckoo search algorithm and Pareto dominance. An external archive is used to record nondominated solutions, whose update strategy improves the quality of solutions by the aid of fast nondominated sorting and crowding distance sorting. Simulation results demonstrate that IMOCS algorithm outperforms other four benchmark algorithms, which can provide optimal task scheduling policy for MEC severs in 6G networks. [ABSTRACT FROM AUTHOR]

The aim of the paper is to define a limit performance of highly efficient battery electric quadricycles for urban mobility. The vehicle is employed for an energy efficiency competition in which urban concepts compete. A multi-physics (thermo-electro-mechanical) Tank To Wheels (TTW) model has been developed and validated. Given the information on the track route (track map and elevation) and on the throttle input command, the model computes the vehicle power demand and cruising speed. The model is validated by means of both indoor and outdoor experimental tests. The validated model is employed for the optimization of the transmission gear ratio and of the driving strategy to minimize the overall energy consumption on a given track. Design variables are related to the transmission gear ratio and to the throttle command profile. The algorithm aims to minimize the energy consumption, including constraints on the maximum current provided by the battery and on the maximum time available to complete the lap. Comparison with some common driving strategies confirmed the effectiveness of the proposed solution, with a foreseen 9% reduction of the vehicle overall energy demand. The single seater ultra-efficient electric vehicle, compared to other urban quadricycles, has a battery capacity ten times lower and a 50% longer driving range. [ABSTRACT FROM AUTHOR]

Renewable energy is an advisable choice to reduce fuel consumption and $\rm CO_{2}$ emission. Therein, wind energy and solar energy are the most promising contributors to reach this goal. Although the hybrid wind/solar system has been widely studied, the real-time current sharing based on their maximum capacities is rarely achieved in terms of seconds. Based on this, this paper proposes an accurate current sharing and voltage regulation approach in hybrid wind/solar systems, which is based on distributed adaptive dynamic programming approach. Firstly, the equivalent wind/solar model is built, which is an indispensable preprocessing to achieve the complementary between wind energy and solar energy. Therein, the wind energy and solar energy can output relative current according to their respective capacity ratio, which ensure the maximum utilization ratio of renewable energy source. Furthermore, current sharing and voltage regulation problem is switched into optimal control problem. Under this effect, each source agent aims to obtain the optimal control variable and achieve accurate current sharing/voltage regulation. Moreover, an adaptive dynamic programming approach based on Bellman principle is proposed. It can achieve accurate current sharing and voltage regulation. Finally, the simulation results are provided to illustrate the performance of the proposed adaptive dynamic programming approach. [ABSTRACT FROM AUTHOR]

Energy consumption is expected to be reduced while maintaining high productivity for container handling. This paper investigates a new energy-efficient scheduling problem of automated container terminals, in which quay cranes (QCs) and lift automated guided vehicles (AGVs) cooperate to handle inbound and outbound containers. In our scheduling problem, operation times and task sequences are both to be determined. The underlying optimization problem is mixed-integer nonlinear programming (MINLP). To deal with its computational intractability, a customized and efficient genetic algorithm (GA) is developed to solve the studied MINLP problem, and lexicographic and weighted-sum strategies are further considered. An $\epsilon $ -constraint algorithm is also developed to analyze the Pareto frontiers. Comprehensive experiments are tested on a container handling benchmark system, and the results show the effectiveness of the proposed lexicographic GA, compared to results obtained with two commonly-used metaheuristics, a commercial MINLP solver, and two state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Massive Multi-Input Multi-Output (MIMO) and Coordinated MultiPoint (CoMP) technologies in Cloud-RAN (C-RAN) architecture become inevitable trend due to the advent of next-generation mobile applications, which are traffic-intensive, such as ultra high definition (UHD) video. In this paper, we study a joint beam activation and user scheduling problem in a 5G cellular network with massive MIMO and CoMP utilizing orthogonal random beamforming technique. This paper aims to minimize total Remote Radio Heads’ (RRHs’) energy expenditure in a dynamic C-RAN architecture while ensuring finite service time for all user traffic arrivals in the communication coverage. We leverage Lyapunov drift-plus-penalty framework to transform an original long-term average problem into a series of per-slot modified problems. Since the provided per-slot problem is combinatorial and nonlinear optimization problem, we are inspired by a greedy algorithm to design energy and delay guaranteed joint beam activation and user scheduling policy, namely BEANS. We prove that the proposed BEANS ensures finite upper bounds of average RRH energy consumption and average queue backlogs for all traffic arrival rates within constant ratio of capacity region and all energy-delay tradeoff parameters. These proofs are the first attempt to theoretically demonstrate guarantees of energy and queue bounds in a framework consisting of possibly negative submodular objective function and non-matriod constraints. Finally, via extensive simulations, we compare the capacity region and energy-queue backlog tradeoff of BEANS with optimal and existing algorithms, and show that BEANS attains up to 65% of energy saving for the same average queue backlog compared to the algorithms which do not take traffic dynamics and energy consumption into considerations. [ABSTRACT FROM AUTHOR]

How to prolong network lifetime has become an important issue in the design of large scale wireless sensor networks (WSNs). In this paper, a novel power saving scheme for conventional WSNs via simultaneous wireless information and power transfer (SWIPT) based relays is proposed. Unlike conventional relays based communications need extra energy supply for data forwarding, the relay applied in this paper works in an energy-free manner with the support of SWIPT. The power saving model with both power splitting (PS) and time switching (TS) based SWIPT are proposed. Then, we formulate the joint power allocation and splitting problems of SWIPT relay assisted WSNs as non-convex constrained optimization problems. Since the formulated problems are non-convex, semi-positive definite programming (SDP) algorithms are proposed to find the joint optimization on power allocation and splitting with low complexity. The simulation results show that the PS model has more advantages than the traditional direct communication model in long distance transmission. Under the same information receiving strategy, the PS model outperforms the TS model. [ABSTRACT FROM AUTHOR]

The heterogeneous fog radio access networks (Fog-RAN), the integration of fog computing, and traditional heterogeneous radio access networks can be implemented through the next-generation wireless communication networks. However, most of the solutions are limited to the spectrum efficiency optimization, and cross-tier interference existing in the fog access points (F-APs) could affect the network performance seriously. In this paper, the user association, resource allocation (including bandwidth and power), and caching deployment are investigated in the heterogeneous Fog-RAN to consider energy efficiency and cross-tier interference mitigation. Specifically, the user association, resource allocation, and caching strategy are formulated as a non-convex optimization problem and then transformed into a convex problem, which can be solved by a proposed algorithm based on the concept of the alternating direction method of multipliers (ADMM). Then an ADMM-based algorithm is proposed to enhance the energy efficiency of the Fog-RAN. Compared with the current solutions, simulation results illustrate the proposed algorithm’s convergence and effectiveness. [ABSTRACT FROM AUTHOR]

The guest editors introduce the Top Picks and Honorable Mentions from the 2016 computer architecture conferences. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate a mobile edge computing (MEC) network, where the user has some computational tasks to be assisted by multiple computational access points (CAPs) through offloading. We consider practical communication scenarios with limited spectrum resources, and the cochannel interference arising from the aggressive reuse of frequency severely degrades the system offloading performance. To enhance the system performance, we provide three CAP selection criteria to choose one best CAP among multiple ones. Specifically, criterion I maximizes the computational capability at the CAP, criterion II minimizes the interfering power, while criterion III maximizes the instantaneous channel gain of data link. In time-varying channel environments, the CAP selection may be outdated, which deteriorates the system performance. For the three criteria, we evaluate the system outage probability in the outdated channel state information (CSI) by taking into account the latency, energy consumption and data rate, and provide the analytical and asymptotic expressions of outage probability, from which we obtain some critical insights on the system design. Simulation results are finally demonstrated to verify the proposed studies. In particular, criterion III under the perfect CSI can achieve the system whole diversity order coming from multiple CAPs. [ABSTRACT FROM AUTHOR]

Computation offloading is an effective method in mobile edge computing (MEC) to relieve user equipment (UE) from the limited computation resource and battery capacity. Meanwhile, simultaneous wireless information and power transmission (SWIPT) can be applied to MEC to extend the operating time of the equipment. However, in multi-user network environment, diverse computation task requirements and changeable network channel states make it challenging to obtain offloading strategy timely and accurately. To address the issue, we propose an intelligent computation offloading scheme (iCOS) based on enhanced priority deep deterministic policy gradient (EPDDPG) algorithm to minimize the energy consumption of all the UEs by jointly optimizing the offloading decision, the central processing unit (CPU) frequency and the power split ratio in a dynamic SWIPT-MEC network. In particular, we improve the traditional fully-connected network structure to obtain both discrete and continuous action outputs, and accelerate neural network parameter updates by using prioritized experience tuples. Furthermore, we use dynamic voltage and frequency scaling (DVFS) technology to dynamically adjust the CPU frequency of local computing, and employ SWIPT technology to balance the charging and communication according to the obtained strategy. Simulation results show that the algorithm proposed in this paper can effectively reduce the energy cost of UEs, and complete more computation tasks within the delay limit. [ABSTRACT FROM AUTHOR]

The poor cruising range of electric vehicle (EV) is a problem preventing its popularity. To tackle this problem, methods such as battery technology, energy-based motion control technology are developed. This paper proposes a new solution from the perspective of path planning. Such a solution is called 3-D multi-object path planning method (3D-M method), in which both the energy consumption and distance are considered. The 3D-M method mainly realizes multi-object path planning by an energy consumption estimation model (ECEM) and a distance-integrated estimation model (DIEM). The ECEM can estimate the energy consumption between the neighbour position and the destination on the 3-D map, using a novel slope energy model considering energy consumption characteristic of the EV. The DIEM can estimate the integrated distance which includes the corresponding 2-D distance and 3-D distance, respectively. In the planning process, the outputs of ECEM and DIEM are combined to determine the cost of a path. In addition, a chaos-based multi-object optimizer (CBMOO) is used to search the optimal weights for the 3D-M method. The simulation experiments prove that the proposed method can generate an optimal path which saves much energy in comparison with the path provided by the distance-based method. [ABSTRACT FROM AUTHOR]

In the era of Connected and Autonomous Vehicles, platooning has the potential to increase roadway capacity and reduce energy consumption. However, as vehicles try to form platoons they may also expend extra energy. Also, depending on its position within a platoon, the energy savings of each vehicle can be different. Thus, optimizing and quantifying the savings that may be gained from platooning is challenging. In this paper, we develop a simulation-optimization framework to tackle this challenge of quantifying energy savings from platooning. Our optimization model determines vehicle-to-platoon assignments given the current location, speed, and destination of all the vehicles and platoons on the freeway. The simulation model takes these platooning decisions from the optimization model and implements them. Vissim is used to simulate the actions taken by all the vehicles and platoons and capture the energy expended by each vehicle over its entire trip duration. To quantify the energy savings, the system is simulated with and without platooning. That is, the optimization model is turned off when assessing the performance of the system without platooning. In addition to the simulation-optimization framework, an accurate energy consumption model is developed in this paper, which is inspired by the work of Tadakuma and colleagues. The energy consumption model utilizes a hybrid prediction formula for aerodynamic drag reduction in multi-vehicle formations unifying both physical mechanisms and existing empirical study data. Our results show that a system wide savings of about 3% can be realized over 160 kilometers when platoons are formed strategically. [ABSTRACT FROM AUTHOR]

Regarding the continuing increase of anthropogenic carbon emissions in the power system with growing energy consumption, researchers have focused on managing the demand side to achieve low-carbon transition. However, such a low-carbon transition cannot be achieved effectively without the proper incentive scheme. Moreover, it is desired to improve the computation quality of carbon tracing tools and extend their applications by state-of-art techniques. To fulfil these existing research gaps, this paper proposes a low-carbon optimal scheduling model with demand response (DR) based carbon intensity control. It aims to reduce the dependence of customers on energy sources with high carbon intensities while decreasing reasonable energy consumption. Likewise, a data-driven approach conducted with the Bayesian interfere regression is proposed to carry out the carbon emission flow (CEF) model to cope with the drawbacks of the conventional emission calculation. Moreover, the storage emission and relevant dynamic impacts to the system are fully considered. The proposed data-driven approach is tested on a series of IEEE systems with different sizes, its high accuracy and efficiency are proved by the simulation results. Further, simulation results indicate that the proposed scheduling model can effectively achieve carbon emission mitigation. [ABSTRACT FROM AUTHOR]

In this paper, we consider a mobile edge computing (MEC)-based secure computation offloading system, and design a practical multi-eavesdropper model including two specific scenarios of non-colluding and colluding eavesdropping. Furthermore, we design a requirement satisfaction model by exploring practical variations in user request patterns for security provisioning, delay reduction and energy saving. Based on these, we propose a satisfaction-maximized secure computation offloading (SMax-SCO) scheme, and then formulate an optimization problem aiming at maximizing users’ requirement satisfactions subject to secrecy offloading rate, tolerable delay, task workload and maximum power constraints. Since the optimization problem is nonconvex, we present an efficient successive convex approximation (SCA)-based algorithm to obtain suboptimal solutions. We demonstrate that the proposed SMax-SCO scheme achieves a significant improvement in security performance and requirement satisfaction compared with existing schemes. Moreover, we conclude that SMax-SCO can resist eavesdropping attacks of multiple eavesdroppers and even colluding eavesdroppers. [ABSTRACT FROM AUTHOR]

This paper investigates the task offloading problem in a hybrid intelligent reflecting surface (IRS) and massive multiple-input multiple-output (MIMO) relay assisted fog computing system, where multiple task nodes (TNs) offload their computational tasks to computing nodes (CNs) nearby massive MIMO relay node (MRN) and fog access node (FAN) via the IRS for execution. By considering the practical imperfect channel state information (CSI) model, we formulate a joint task offloading, IRS phase shift optimization, and power allocation problem to minimize the total energy consumption. We solve the resultant non-convex optimization problem in three steps. First, we solve the IRS phase shift optimization problem with the sequential rank-one constraint relaxation (SROCR) algorithm and semidefinite relaxation (SDR) algorithm for a given power- and computational resource allocation. Then, we exploit a differential convex (DC) optimization framework to determine the power allocation decision that minimizes the total energy consumption. Given the IRS phase shifts, the computational resources, and the power allocation, we propose an alternating optimization algorithm for finding the jointly optimized results. The simulation results demonstrate the effectiveness of the proposed scheme as compared with other benchmark schemes, and the energy efficient offloading strategy for the proposed fog computing system can be chosen according to the asymptotic form of the effective signal-to-interference-plus-noise ratio (SINR). [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSNs), finding the best transmission route from each node to the mobile sink is not a simple task because of the dynamic characteristics of sink node and cluster transmission. In order to balance energy consumption and reduce collisions in wireless transmissions, this paper proposes a Dynamic Spanning Tree with Mobile Sink (DSTMS) routing algorithm. Firstly, based on the Low Energy Adaptive Clustering Hierarchy Protocol (LEACH), a multi-layer transmission framework with dynamic minimum spanning tree (MST) is constructed to optimize data transmission route. In this framework, the selected rendezvous points, according to the motion parameters of mobile sink, build the dynamic rendezvous layer to constraint the hierarchical transmission of DSTMS. In addition, considering some factors of transmission energy consumption and residual energy distribution,the weight factor based on energy efficient function is introduced to evaluate the cost of transmission path within the MST. Moreover, the proposed algorithm not only reduces network energy consumption caused by frequent location updates of mobile sink, but also reduces data transmission congestion by constructing dynamic hierarchical transmission strategy. The simulation results show that the DSTMS algorithm can effectively prolong the network life, balance the network load and reduce collisions in wireless transmissions. [ABSTRACT FROM AUTHOR]

The development of security-aware mobile devices using wide-input–output (IO) nonvolatile memory (NVM) is hindered by high peak current, large area overhead for high read bandwidth (BWR), and considerable energy consumption for data movement between NVM and logic blocks. Furthermore, data stored in NVM are vulnerable to reverse-engineering attacks. This work presents a high BWR security-aware near-memory-computing spin-transfer torque magnetic random-access memory (STT-MRAM) macro using a multi-bit current-mode sense amplifier (MB-CSA) to reduce peak current and energy consumption for wide-IO access, a near-memory shift-and-rotate functionality (NSRF) in conjunction with the MB-CSA to reduce area overhead and enable the completion of read and logic operations within a single cycle, and a reverse-engineering-proof XOR–based memory data protector to protect data stored in NVM against reverse-engineering attacks. A 1-Mb 1024-b read STT-MRAM macro with data protector fabricated using foundry embedded 22-nm STT-MRAM. This work achieved 42.67 GB/s for BWR and 0.23 pJ/b. Inclusion of the NSRF circuit reduced area overhead by 33.3% while increasing latency by only 170 ps. [ABSTRACT FROM AUTHOR]

In modern datacenters, huge energy consumption is a significant problem that remains to be solved. Previous works reduce the system energy consumption by switching the idle servers to a low-power state. However, the workload demands on servers change dynamically and mainly depend on the real-time workload status. To maintain the system energy efficiency, when providing servers with some servers reserved, the status of workloads should be carefully considered. Generally, the status of workloads is characterized by some key factors sampled from the workloads. However, under dynamic workload demands, the accuracy of these sampled values varies. To accurately assess the status of server workloads, in this paper, we propose a Dynamic Time Scale based server Provision (DTSP) method, which takes the variability of workloads into consideration when providing servers for workload demands. To obtain accurate factor values indicating real-time workload, DTSP samples several key workload factors, including the coefficient of variation of arrival intervals, the request arrival rates of current workload and previous workload, and the mean service time of current requests, with a dynamic compatible rate. With these sampled factors, DTSP can accurately estimate the demands of workloads on servers and provide appropriate numbers of servers for the dynamic workloads. Extensive experiments demonstrate that taking the workload variability into consideration, DTSP can significantly promote the energy efficiency of a datacenter. [ABSTRACT FROM AUTHOR]

Massive machine type communications (mMTC) is one of main application cases in 5G, which is supposed to support communications of massive number of machine-type devices (MTDs). Distributed queue (DQ) is a variant of tree splitting protocol which combines an m-ary tree splitting algorithm with a set of simple smart rules, organizing every terminal in one out of two virtual queues. Theoretically, DQ allows access to infinite terminals and is stable under any traffic condition, which alleviates the unstable problem of slotted ALOHA, and is especially suitable for mMTC. However, its theoretical comprehensive performance analysis as well as related statistical characteristics is still missing, which severely restricts the full manifestation of its performance advantages. In view of this, the paper proposes a general performance analysis framework for DQ, with which full probability space of DQ evolution process is presented for the first time. To be more specific, probability distribution function (PDF), mean and variance of throughput, latency and energy consumption of DQ is analytically derived to comprehensively evaluate performance. Taking the IEEE 802.15.4 standard for mMTC as example, numerical results validate the accuracy of the proposed analysis framework and the stability of DQ, present effects of number of MTDs, number of contention slots (${m}$), and maximum number of transmissions (${L}$) on DQ in terms of aforementioned performance metrics. These results together provide good reference to find appropriate value of ${m}$ and ${L}$ to balance the performance metrics and enable more practical network optimization. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) have become an indistinguishable part of any Internet of Things (IoT) based system. In an IoT-based system, WSN monitors different physical objects and forwards the collected data to the base station or gateway node for further processing. Existing routing protocols designed for WSNs are not suitable for IoT-based systems. An intelligent and high-performance routing protocol is essential for real-time data analysis and decision-making. Therefore, this paper proposes a mobile sink-based intelligent data routing scheme for WSNs. Initially, Particle Swarm Optimization is used for optimal cluster formation. After cluster formation, the optimal number of rendezvous points are selected, and a data-gathering tour is planned for the mobile sink. Furthermore, a mobile sink-based intelligent data gathering scheme is proposed to collect the data from the cluster heads. Extensive simulations and mathematical analysis are performed to verify the effectiveness of the proposed scheme. The simulation results are compared with state-of-the-art algorithms in terms of stability period, network lifetime, throughput, average energy consumption, fairness index, data transmission latency, and message overhead. [ABSTRACT FROM AUTHOR]

This paper studies an intelligent reflecting surface (IRS)-assisted wireless-powered communication network (WPCN), where a hybrid access point (HAP) broadcasts energy signals to multiple devices for their energy harvesting in the downlink (DL) and then the devices use the harvested energy to transmit information signals to the HAP in the uplink (UL) with the help of an IRS. In particular, we propose three types of IRS beamforming configurations, namely fully dynamic IRS beamforming (FDBF), partially dynamic IRS beamforming (PDBF), and static IRS beamforming (SBF), to strike a balance between the system performance and signaling overhead as well as implementation complexity. Moreover, we adopt a practical non-linear energy harvesting (EH) model, and leverage a power-splitting (PS) EH receiver architecture with multiple rectifiers to avoid the input radio frequency power to get stuck into the saturation regime. We aim to minimize the transmit energy consumption at the HAP by jointly optimizing the DL/UL time allocation, the HAP/devices transmit power, the PS factor, and IRS phase shifts, subject to a set of minimum throughput requirements for individual devices. To address the resulting non-convex optimization problems, a successive convex approximation (SCA) based alternating optimization algorithm is proposed. Moreover, we study the case with the ideal linear EH model and two algorithms, namely SCA-based algorithm and semidefinite relaxation (SDR) algorithm, are proposed. Simulation results demonstrate the effectiveness of our proposed designs over various benchmark schemes and also unveil the importance of the joint design of IRS beamforming and PS rectifiers for achieving energy efficient WPCNs in practice. [ABSTRACT FROM AUTHOR]

Neighbor discovery (ND) protocols are used for establishing a first contact between multiple wireless devices. The energy consumption and discovery latency of this procedure are determined by the parametrization of the protocol. In most existing protocols, reception and transmission are temporally coupled. Such schemes are referred to as slotted, for which the problem of finding optimized parametrizations has been studied thoroughly in the literature. However, slotted approaches are not efficient in applications in which new devices join the network gradually and only the joining devices and a master node need to run the ND protocol simultaneously. For example, this is typically the case in IoT scenarios or bluetooth low energy (BLE) piconets. Here, slotless protocols that decouple reception and transmission can achieve significantly lower worst-case latencies than slotted ones. In this paper, we study slotless, BLE-like protocols, which schedule receptions and transmissions independently using periodic intervals (PI). For this class of protocols, optimal parameter values remain unknown. To address this, we propose an optimization framework for PI-based protocols, which translates any specified duty-cycle (and therefore energy budget) into a set of optimized parameter values. We show that the parametrizations resulting from one variant of our proposed scheme are optimal when one receiver discovers one transmitter, and no other parametrization or ND protocol – neither slotted nor slotless – can guarantee lower discovery latencies for a given duty-cycle in this scenario. Since the resulting protocol utilizes the channel more aggressively than other ND protocols, beacons will collide more frequently. Hence, due to collisions, the rate of successful discoveries gracefully decreases for larger numbers of devices discovering each other simultaneously. We also propose a scheme for configuring the BLE protocol (and not just BLE-like protocols). Though it is not clear whether the resulting parametrizations minimize the latencies of BLE, reasonably low worst-case latencies can be guaranteed. [ABSTRACT FROM AUTHOR]

This paper addresses the design of a torque vectoring architecture to control the four electrical machines in a four-wheel-drive (4WD) formula-type competition vehicle. The scheme includes a new yaw-rate controller and a novel optimal torque distribution algorithm. Two yaw-rate controllers are proposed: one based on $\mathcal {H}_\infty$ optimal control and another based on linear parameter varying (LPV) system concepts. Both controllers are designed using an extended bicycle model validated with experimental data. Simulation results shown the effectiveness of the proposed overall control scheme in terms of energy efficiency, cornering speed and stability no matter the high-demanding working conditions. Such an effectiveness is quantitatively demonstrated by means of several key performance indicators chosen to ease the comparison of the proposed approach with respect to other reported works. [ABSTRACT FROM AUTHOR]