Unmanned aerial vehicles (UAVs) hold significant potential for sensing services in a large scope of area, thanks to their wide coverage and adaptable deployment. Considering the complex environment dynamics and limited sensing range, navigating multiple UAVs in a distributed way becomes challenging to implement cooperative data sensing and transmission tasks. In this paper, we optimize the trajectory design of UAVs by jointly considering the collected data volume, geographical fairness and limited energy reserve during their service period. To achieve the long-term serving objective, a memory augmented multi-agent deep reinforcement learning approach is presented to ensure energy-efficient distributed trajectory design with partial observations. Specifically, the intrinsic criterion is developed to enhance UAV spatial exploration when reaching the boundary of explored regions. Then, to address the information loss caused by incomplete observations, the spatial-temporal memory augmented actor-critic architecture is designed to extract historical contextual features for multi-UAV cooperative navigation. Furthermore, the prioritized experience replay mechanism is incorporated to enhance important experience exploitation for UAV collaboration. Extensive simulations using two real-world datasets in Shenzhen and Beijing demonstrate that the proposed method outperforms the state-of-the-art methods in terms of data collection ratio, geographical fairness, and energy consumption ratio.

Accurate velocity prediction is crucial for predictive torque distribution in dual-motor electric vehicles (EVs). This paper proposes a surrounding vehicle-aware predictive torque distribution strategy to enhance energy efficiency. A Transformer-based velocity predictor is developed by integrating the historical velocities of ego vehicle and surrounding vehicles with relative distance, achieving 49% MAE and 48% RMSE reductions compared to a single-vehicle prediction baseline. The predicted velocity sequence is embedded into a model predictive control framework to optimize front/rear motor torque distribution, generating 8.2-16.1% more high-efficiency motor working points while maintaining battery state-of-charge (SOC). Simulation results demonstrate 0.35% improvement in end-of-cycle SOC and smoother current profiles under real driving cycles, validating the effectiveness of spatiotemporal interaction modeling for energy-saving torque distribution.

Accurately estimating the State of Health (SOH) of batteries in field applications is critical for timely maintenance and secondary utilization. Although considerable studies are conducted using data-driven techniques, these methods often face challenges in interpretability and integrating physical knowledge. To address this issue, this paper proposes an accurate SOH estimation method using a physics-informed neural network (PINN) with spatio-temporal feature extraction. The proposed model utilizes multi-sensor data as an input and employs a spatio-temporal attention mechanism to automatically extract effective features from both the time step dimension and the sensor dimension. Subsequently, PINN is utilized to regulate the convolutional neural network training process and oversee the degradation trajectory of SOH estimation. By integrating the attention mechanism and physical information, the model achieves higher accuracy and more interpretable predictions. The proposed method is validated on a field dataset with 20 on-road vehicles. Experimental results indicate that the proposed method achieves a root mean square error of 1.599%, which is a relative reduction of 42.32% compared to the baseline model.

Mobile crowdsensing (MCS) by human participants and unmanned aerial vehicles (UAVs) is an emerging air-ground collaborative data collection paradigm by navigating a group of UAVs to collaborate with human participants to provide large-scale and fine-grained sensing services. In this paper, we aim to optimize the trajectory design of UAVs by jointly considering the collected data volume, geographical sensing fairness, and limited energy reserve during the serving period. To achieve the long-term serving objective, we propose a human participants distribution prediction based multi-agent deep reinforcement learning method for efficient UAV navigation to collaborate with human participants in performing MCS tasks. Specifically, we first introduce a region division based human participant spatial distribution prediction method to help UAVs to collaborate with human participants by the predictive mobility flows. Then, we present the multi-agent proximal policy optimization (MAPPO) based method for efficient UAV navigation decision-making. Extensive simulations and trajectory visualization using the real-world mobility dataset in KAIST show that the proposed method consistently outperforms the state-of-the-art in terms of the energy efficiency when varying the number of UAVs and human participants.

With the rapid development of vehicle-to-everything technology, vehicular edge caching has emerged as a crucial component for managing frequently accessed content at the network’s edge. However, due to vehicles’ high mobility, it is challenging to determine where and which content needs to be cached. To address this issue, a mobility and context-aware precache strategy is proposed to proactively prefetch and replace content in two steps. First, by integrating the traffic features from vehicles and roads, a spatial-temporal informer-based model is designed to predict long-term vehicle trajectories. Subsequently, a proactive context-aware precache strategy is proposed. By analyzing the context of different cache types, the required content can be further accurately estimated according to the cache type and workload. Extensive simulations based on real-world mobility scenarios are conducted to validate the performance of the proposed method. The results show that the proposed method can improve prediction accuracy and cache hit rate by 34.56% and 18.89%, and reduce mean response time and total energy cost by 6.1% and 2.65% compared to the existing precaching methods.

For intelligent driving systems, predicting the lane change intentions of surrounding vehicles in advance is essential to improve safety and efficiency in dynamic traffic conditions. In this paper, a lane-changing intention identification method with driving style recognition is proposed to identify lane-changing intentions for intelligent vehicles, incorporating driving style recognition to enhance prediction accuracy. Firstly, a dynamic clustering framework integrating the Gaussian Mixture Model is introduced to identify the driving style of vehicles under different traffic conditions. Subsequently, a lane-changing intention recognition model based on bidirectional long short-term memory networks is proposed. By leveraging driving style enhancements, the model is refined to better simulate and comprehend driving behaviors on the road. Finally, the NGSIM dataset is used to train and evaluate the proposed prediction identification method. The results show that the accuracy is improved by 2.1% compared to state-of-the-art methods.

Thermal runaway of lithium-ion batteries is one of the key challenges hindering the development of electric vehicles. Realizing timely fault detection in battery systems is of great significance for preventing thermal runaways and safeguarding people’s lives and properties. As it is difficult to obtain fault battery datasets in the real world, there is a need to develop novel fault detection methods that can operate with normal data. In this paper, we propose an optimized Gated Recurrent Unit autoencoder architecture that integrates the sparse representation technique to detect battery faults in electric vehicles. Firstly, the Gated Recurrent Unit is employed to efficiently learn the information in battery data from normal electric vehicles. Then, the autoencoder utilizes the sparse representation technique to improve its ability to recognize abnormal data by learning a set of basis vectors that can sparsely represent normal data. Finally, the reconstruction errors between the original and reconstructed vectors are calculated in a sliding window and compared to the threshold to detect the fault. The effectiveness of the proposed method is verified on a real operating dataset including two normal electric vehicles and two faulty electric vehicles. The results show that it can provide early alarm time and reduce the probability of false alarms.

Under the background of dual carbon, lithium batteries are widely used in the energy field. However, range anxiety limits the popularity of electric vehicles. Optimization strategies for fast charging of lithium-ion batteries have been extensively studied to solve this problem. The huge parameter space of charging protocols and the complex aging mechanism of batteries limit the application of fast charging methods. The ability of reinforcement Learning to learn from the environment and adapt to the stochastic nature of battery behavior is a significant advantage. In this paper, we propose an innovative method for fast charging lithium-ion batteries using the Two-Delay Deep Deterministic Policy Gradient (TD3) algorithm and the Single Particle Model with Electrolyte model. The trained agent dynamically adjusts the charging current according to the state every 5 seconds to optimize the trade-off between fast charging and safety limits and can charge the state of charge (SOC) of the battery from 0.2 to 0.8 in 410 seconds, while protecting against overvoltage and overheating. Meanwhile, it still works well for different initial SOC.

It is a significant challenge to reconcile the competing interests of individual microgrid units when energy is shared in a multi-energy system. Moreover, the majority of these systems solely focus on power sharing, without adequately addressing the associated carbon emissions. This paper puts forward a sharing approach that makes use of multi-agent collaboration to optimise energy sharing in multi-energy systems and achieve low-carbon operation. Firstly, a system model is constructed to describe the interrelationship between each microgrid and the energy-sharing platform. Subsequently, a shared pricing mechanism will be devised to incentivise each microgrid to prioritise its participation in the local sharing market. The shared pricing mechanism is employed to construct the microgrid utility maximisation problem and transform it into a Markov game process. A multi-agent cooperative approach is put forth as a means of optimising the energy-sharing strategy. Ultimately, the simulation results demonstrate that this energy-sharing strategy can satisfy the operational constraints, maintain equilibrium between supply and demand, reduce energy costs, and promote the economic and low-carbon operation of the microgrid.

In truck platoons with a bidirectional-leader topology, variations in channel conditions result in unreliability and high latency in vehicle-to-vehicle (V2V) communications. This paper proposes an adaptive relay selection strategy based on Q-learning (QL). The strategy ensures that all vehicles in the platoon receive safety messages from the lead vehicle quickly and reliably. Firstly, relay selection is modeled as a Markov decision process (MDP). The lead vehicle and the relays act as intelligent agents. Agents make decisions adaptively based on real-time state observations in a dynamic communication environment. Secondly, a reward function is designed based on platoon topology and channel state information statistics (CSI). The purpose is to drive the proposed strategy to learn the optimal strategy for message transmission under different environments. Lastly, the simulation results demonstrate the effectiveness and robustness of the proposed algorithm. In various channel attenuation environments, the strategy has been demonstrated to enhance the packet delivery ratio (PDR) for the platoon tail and significantly increase the platoon’s throughput.

The integration of advanced communication and information technologies in smart grids has led to enhanced efficiency and reliability but also introduced security vulnera-bilities, prompting the need for robust cyber attack detection methods. Traditional approaches struggle to capture evolving attack patterns and handle high-dimensional data, highlighting the necessity for more sophisticated approaches. A neighbor-hood reconstruction-based smart grid attack detection scheme based on subgraphs is proposed. By leveraging Graph Neural Networks (GNNs), the challenge of capturing complex inter-dependencies among grid nodes is addressed. This approach employs unsupervised learning principles, training the model solely on normal data and utilizing the reconstruction error of node features to detect attacks. Additionally, by subgraph sampling and feature suppression, the model's ability to utilize neighborhood information is enhanced, thereby further improving detection effectiveness. Simulation results on IEEE 30-bus and IEEE 118-bus power system demonstrate the feasibility of the method, achieving a detection accuracy of 96.67% and 97.46%, respectively.

Accurate modeling of open circuit voltage hysteresis for LiFePO4batteries is crucial for establishing an advanced battery model. However, existing hysteresis modeling methods often yield suboptimal results due to inadequate parameterization. This paper proposes an optimal modeling method for open circuit voltage hysteresis based on the Prandtl-Ishlinskii model and an associated parameterization method. First, an asymmetric operator with cubic envelope functions is designed to enhance the classical Prandtl-Ishlinskii model, which originally features a symmetric and linear operator. This modification enables the proposed model to accurately capture intricate hysteresis. Second, a hierarchical parameterization method is proposed to identify optimal parameters. Specifically, an improved grey wolf optimizer is employed to determine the operator-related parameters. Then, the remaining parameters are calculated using the least squares algorithm, enhancing computational efficiency. Finally, the proposed model is validated on the experimental hysteresis data from three distinct scenarios. The modeling error of the proposed model decreased by 66.57 % and 32.51 % compared with two other benchmark models.

Model predictive control is a real-time energy management method for hybrid energy storage systems, whose performance is closely related to the prediction horizon. However, a longer prediction horizon also means a higher computation burden and more predictive uncertainties. This paper proposed a predictive energy management strategy with an optimized prediction horizon for the hybrid energy storage system of electric vehicles. Firstly, the receding horizon optimization problem is formulated to minimize the battery degradation cost and traction electricity cost for the electric vehicle operation. Then, the optimal control sequence is solved to obtain the power allocation between the battery and the supercapacitor. Furthermore, the effect of different horizons on the optimization results is analyzed under diverse operating conditions, determining the optimal horizon to balance the system costs and computation burden. Compared with the short horizon, the optimal horizon can achieve 5.2%$\sim$8.5% performance improvement with the acceptable computation time approaching 1 s.

In the future 6G intelligent transportation system, the edge server will bring great convenience to the timely computing service for connected vehicles. To guarantee the quality of service, the time-critical services need to be migrated according to the future location of the vehicle. However, predicting vehicle mobility is challenging due to the time-varying of road traffic and the complex mobility patterns of vehicles. To address this issue, a spatial-temporal awareness proactive service migration strategy is proposed in this paper. First, a spatial-temporal neural network is designed to obtain accurate mobility by using gated recurrent units and graph convolutional layers extracting features from spatial road traffic and multi-time scales driving data. Then a proactive migration method is proposed to guarantee the reliability of services and reduce energy consumption. Considering the reliability of services and the real-time workload of servers, the migration problem is modeled as a multi-objective optimization problem, and the Lyapunov optimization method is utilized to obtain utility-optimal migration decisions. Extensive simulations based on real-world datasets are performed to validate the performance of the proposed method. The results show that the proposed method achieved 6% higher prediction accuracy, 10% lower dropping rate, and 10% lower energy consumption compared to state-of-the-art methods.

Battery degradation is a main hinder to extend the persistent lifespan of the portable heterogeneous computing device. Excessive energy consumption and prominent current fluctuations can lead to a sharp decline of battery endurance. To address this issue, a battery-aware workflow scheduling algorithm is proposed to maximize the battery lifetime and release the computing potential of the device fully. Firstly, a dynamic optimal budget strategy is developed to select the highest cost-effectiveness processors to meet the deadline of each task, accelerating the budget optimization by incorporating deep neural network. Second, an integer-programming greedy strategy is utilized to determine the start time of each task, minimizing the fluctuation of the battery supply current to mitigate the battery degradation. Finally, a long-term operation experiment and Monte Carlo experiments are performed on the battery simulator, SLIDE. The experimental results under real operating conditions for more than 1800 hours validate that the proposed scheduling algorithm can effectively extend the battery life by 7.31%-8.23%. The results on various parallel workflows illustrate that the proposed algorithm has comparable performance with speed improvement over the integer programming method.

High-reliability and low-latency communication is essential for timely information exchange in vehicle platooning. As a key enabler of this, the cellular vehicular-to-everything (C-V2X) network uses a sensing-based semi-persistent scheduling (SPS) protocol, where radio resources are reserved for a number of transmissions with reduced resource re-allocation and control overhead. However, consecutive access collisions may be caused by reservation conflict, which leads to long delay and threatens platoon’s stability and safety. In this paper, a coordinating resource reservation (CRR) protocol is proposed for vehicle platooning. By implementing error detection with coordination among platoon vehicles, the resource reservation is improved for reduced collisions and delay. Specifically, packet reception/loss information is sent out by platoon vehicles through their own packets. Such information is shared with transmitters and guides them to reserve new resources when access collision occurs. As a result, long delay is avoided while no extra feedback packet is introduced. Furthermore, Markov analysis is presented to evaluate the performance of SPS and the proposed CRR for vehicle platooning, providing the quantified performance gains. Finally, simulation results demonstrate the superiority of the proposed CRR in reducing packet loss and latency, compared with the legacy SPS and other state-of-the-art solutions.

The increasing number of connected vehicles exacerbates the scarcity of spectrum resources in vehicle-to-everything (V2X) communication. To optimize the utilization of wireless resources, it is crucial to allocate the limited spectrum blocks to each roadside unit (RSU) based on the real-time bandwidth demand of vehicles within their coverage. However, the complex mobility patterns of vehicles and dynamic traffic conditions make it challenging to accurately and promptly estimate the bandwidth demand. To address this issue, a spatial-temporal multi-attentional network (STMA-net) is designed to predict the future bandwidth demand of RSUs. Based on the predicted bandwidth demand, a prediction error-compensable proactive bandwidth allocation algorithm is proposed to adaptively allocate spectrum resources and narrow the discrepancy between predicted and actual demand. Experimental results with realistic traffic in Bologna demonstrate that the proposed STMA-net achieves 11.25% higher prediction accuracy compared to state-of-the-art methods. Furthermore, the proposed proactive bandwidth allocation method outperforms existing methods, providing the highest throughput and serving 5% more vehicles while reducing the service drop rate by an order of magnitude.

Accurate speed prediction plays a critical role in the predictive energy management of electric vehicles. This paper proposes a spatial-temporal data-driven speed prediction method for the predictive energy management of battery/supercapacitor electric vehicles. The proposed speed prediction method is performed using a long short-term memory network and validated on a real-world commuting data set in China. Different from existing prediction methods based only on speed and acceleration, we take spatial information as an additional input to improve speed prediction accuracy. The predicted future speed is then leveraged by a model predictive control-based energy management strategy to minimize the battery degradation cost. Quantitative comparisons illustrate that the proposed speed prediction method can reduce the root mean square error and mean absolute error by 10.01-19.15% compared with no spatial information prediction method. The more accurate prediction can further improve the optimality of the predictive energy management strategy, i.e., reduce the battery capacity loss and yield closer results to model predictive control with completely accurate prediction.

Erasure codes have been widely used to enhance data resiliency with low storage overheads. However, in geo-distributed cloud storage systems, erasure codes may incur high service latency as they require end users to access remote storage nodes to retrieve data. An elegant solution to achieving low latency is to deploy caching services at the edge servers close to end users. In this paper, we propose adaptive and scalable caching schemes to achieve low latency in the cloud-edge storage system. Based on the measured data popularity and network latencies in real time, an adaptive content replacement scheme is proposed to update caching decisions upon the arrival of requests. Theoretical analysis shows that the reduced data access latency of the replacement scheme is at least 50% of the maximum reducible latency. With the low computation complexity of our design, nearly no extra overheads will be introduced when handling intensive data flows. For further performance improvements without sacrificing its efficiency, an adaptive content adjustment scheme is presented to replace the subset of cached contents that incur the aforementioned performance loss. Driven by real-world data traces, extensive experiments based on Amazon Simple Storage Service demonstrate the effectiveness and efficiency of our design.

Caching has been considered as a promising solution to achieve low latency in distributed erasure coded storage systems. The previous research work categorizes all feasible caching decisions into a set of cache partitions, and then obtains the optimal solution by applying the market clearing price on each cache partition. While enjoying the ultimate performance of low data access latency, the optimal scheme suffers from high computation overheads when applied to large-scale storage systems. This paper presents SampleX, which constructs the sparsification of cache partitions through sampling to approximate the optimal caching scheme with substantially reduced computation complexity. Theoretical analysis guarantees the performance of SampleX. Furthermore, SampleX is implemented in a streaming fashion, capturing the characteristics of recent traffic for online cache content replacement. Trace-driven experimental results show that online SampleX is up to 95× faster than the state-of-the-art online scheme while only incurring a performance loss of 0.81%.

The braking system is the key part of trains, and its full life-cycle of health status is essential to ensure the safety of trains. How to accurately assess real-time health status throughout the full life-cycle of the train braking system is a challenge. In this paper, a digital twin-driven hybrid estimate method for health status of the braking system is proposed. Firstly, an equivalent model of the braking system is built in the digital twin platform. Then, a hybrid method of fusing model and data is proposed to assess the health status. Finally, a cloud digital twin experimental platform for health status assessment of the braking system is built, and the health status is shown by visualization framework. The experiments verify the effectiveness and practicality of the proposed scheme.

Reasonable power distribution between battery and supercapacitor in electric vehicles is a crucial problem to improve energy consumption and economy. An online energy management strategy based on model predictive control (MPC) is proposed in this paper. Firstly, a radial basis function neural network optimized by particle swarm algorithm is presented to generate the short-term future velocity, i.e., the reference trajectory of the MPC. Then, a cost function considering the battery degradation cost and the electricity cost is constructed and optimized within each prediction horizon while maintaining the state of charge of the supercapacitor. Simulation results on the UDDS driving cycle show that the total cost of the proposed strategy is reduced by 6.3% and 3.9% compared with the near-optimal rule-based strategy and the none optimized velocity predictor-MPC, respectively, indicating that the velocity prediction accuracy has a significant impact on the performance of real-time energy management.

With the development of renewable energy, much attention has been paid to improving energy efficiency. This paper proposes a residential microgrid scheduling method to improve the energy utilization rate of residential buildings. Firstly, the power cost model for residential users and the specific charge-discharge models of electric vehicles and energy storage equipment is constructed. Then the whole energy scheduling process is formulated as a multi-variable mixed integer linear programming (MV-MILP) problem, whose objective is to minimize the power cost of end-users. The differential evolution algorithm is adopted to solve the problem, and the scaling factor adaptation is further used to accelerate the convergence. Simulation results demonstrate the effectiveness of the proposed scheduling method.

The prediction of driving range is very important for electric bus, but there is usually a difficulty: battery aging affects the accuracy of driving range prediction. In order to solve this problem, this paper proposes a driving range prediction method for electric bus, which is robust to the battery aging effect. Firstly, we extract the features that affect the driving range from the real-world dataset, quantify the correlation between them and the driving range by grey correlation analysis. Then through the feature enhancement technology, the time window processing is used to mitigate the influence of battery aging, and the time information hidden in the historical period sequence is deeply excavated. On this basis, we establish the driving range prediction model based on k-nearest neighbors regression, where the key parameters are optimized with the particle swarm optimization algorithm. Numerous experimental results show that compared with the classical methods, the method proposed in this paper has higher prediction accuracy especially when the batteries undergo significant aging effects.

Estimating accurate torque and speed is critical to control the operation of permanent magnet synchronous motors (PMSM). But the temperature factors are usually neglected in existing studies, which degrades estimation accuracy. In this paper, a thermal-aware digital twin model is proposed for PMSM to estimate motor torque and speed with the motor temperature and d-q axis current and voltage. Firstly, the motor parameters related to torque and speed are extracted by the Spearman correlation coefficients. Moreover, the stator winding temperature is selected as the input feature. Secondly, a digital model based on BP neural networks (BPNN) is established to estimate torque and speed. Thirdly, the parameters of the BPNN model are optimized by the whale optimization algorithm to accelerate the convergence speed and avoid local optima. Finally, experimental results show that the mean square error (MSE) of the BPNN model considering the temperature factors is reduced by 8.3%, which verifies that there is an effect of temperature on the torque and speed estimation. The MSE of the proposed method is reduced by 11.7% on average, which confirmed the higher accuracy of the proposed method compared with the classical BPNN model.

Vehicle platooning utilizes automated driving and communication to let a group of vehicles travel closely, which improves road safety, traffic efficiency and fuel economy. In a platoon system, a critical task is to guarantee reliable communication among vehicles with efficient medium access control (MAC). This paper focuses on the feasibility of the distributed resource reservation MAC for communications among platoon vehicles using the cellular vehicle-to-everything (C-V2X) technology. For this purpose, a Markov chain-based model is proposed, which precisely estimates the network performance with different information flow topologies and system configurations. The state transition matrix is deduced and the stable state distribution is obtained. Given the information flow topology, we derive the probability that a platoon vehicle successfully delivers packets to all of the designated receivers. Finally, simulation results validate the analysis. To better implement the MAC protocol in practice, we also discuss the success probability for various information flow topologies in platoon communication.

Low-latency data service is an increasingly critical challenge for data center applications. In modern distributed storage systems, proper data placement helps reduce the data movement delay, which can contribute to the service latency reduction tremendously. Existing data placement solutions have often assumed the prior distribution of data requests or discovered it via trace analysis. However, data placement is a difficult online decision-making problem faced with dynamic network conditions and time-varying user request patterns. The conventional static model-based solutions are less effective to handle the dynamic system. With an overall consideration of data movement and analytical latency, we develop a reinforcement learning-based framework DataBot+, automatically learning the optimal placement policies. DataBot+ adopts neural networks, trained with a variant of$Q$-learning, whose input is the real-time data flow measurements and whose output is a value function estimating the near-future latency. For instantaneous decision making, DataBot+ is decoupled into two asynchronous production and training components, ensuring that the training delay will not introduce extra overheads to handle the data flows. Evaluation results driven by real-world traces demonstrate the effectiveness of our design.

Erasure codes have been widely considered as a promising solution to enhance data reliability at low storage costs. However, in modern geo-distributed storage systems, erasure codes may incur high data access latency as they require data retrieval from multiple remote storage nodes. This hinders the extensive application of erasure codes to data-intensive applications. This paper proposes novel caching schemes to achieve low latency in distributed coded storage systems. Assuming that future data popularity and network latency information are available, an offline caching scheme is proposed to explore the optimal caching solution for low latency. The proposed scheme categorizes all feasible caching decisions into a set of cache partitions, and then obtains the optimal caching decision through market clearing price for each cache partition. Furthermore, guided by the optimal scheme, an online caching scheme is proposed according to the measured data popularity and network latency information in real time, without the need to completely override the existing caching decisions. Both theoretical analysis and experiment results demonstrate that the online scheme can approximate the offline optimal scheme well with dramatically reduced computation complexity.

The driving performance of electric vehicles seriously degrades due to the deterioration of lithium-ion batteries at low temperatures. Preheating lithium-ion batteries can effectively improve the driving range of electric vehicles at subzero temperatures. In this paper, an optimal pulse heating strategy is proposed for low-temperature heating of lithiumion battery. Firstly, this paper establishes a coupling model to describe the electro-thermal-aging behavior of battery. Secondly, the heating time and capacity loss jointly form a multi-objective optimization problem with the current constraint. The optimization problem is solved by using the particle swarm optimization(PSO) algorithm and the effect of weighting coefficient on heating performance is discussed to obtain the optimal pulse current. The results show that the proposed strategy can effectively reduce heating time without causing serious capacity reduction.

Supercapacitors have recieved increasing attentions in emerging portable power applications. The charging process of supercapacitors significantly affects the performance of both supercapacitors and chargers. Considering the charging time of supercapacitors is typically limited in practical applications, in this paper, we propose an optimal charging method for supercapacits with the limited charging time. Firstly, we analyze existing cell balancing and charging circuits, and adopt the switched resistor circuit. Then, we design a user-interactive optimal charging method for supercapacitors where the charging time can be specified by the users. The energy efficiency maximization of the proposed charging method is proved rigorously. A simulation charging platform has been established to verify the effectiveness of the proposed charging method. The simulation results show that the proposed charging method can effectively improve the energy efficiency under charging time constraints when compared with existing methods.

For autonomous vehicles and smart transportation services, information exchange and fusion with low latency and high reliability is critical. The 3rd Generation Partnership Project has released the cellular vehicle-to-everything (C-V2X) Mode 4 to enable direct vehicle-to-vehicle communications regardless of the cellular coverage. Mode 4 uses the sensing-based semi-persistent resource scheduling (SPS) to support autonomous resource selection by vehicles. However, channel access collisions lead to packet losses, especially in crowded scenarios. Thus, an accurate analytical model is essential to quantify the system performance, reveal how to mitigate collision and ensure system reliability and scalability. This paper focuses on the analytical modeling of the SPS and derives the access collision ratio considering both the sensed and hidden terminals in V2X. Extended simulations are conducted to verify the correctness of the analytical framework. In addition, we investigate the impact of system parameters on performance, which provides important guidelines for improving the system configuration.

Infrastructure-as-a-Service clouds in geo-distributed data centers offer various pricing options, including on-demand and reserved instances, which provide an elastic and cost-effective infrastructure to support High Performance Computing (HPC) applications. In this paper, we propose an instance reservation based cloud service framework, modeling the cost-minimizing reservation decision issue as an NP-hard integer programming problem for distributed data centers. To ease its computation complexity, two algorithms are proposed to minimize the HPC service cost with the worst-case performance guarantees: an offline heuristic-greedy algorithm, and a rolling-horizon based online algorithm when only short-term demand prediction is available. Facing fluctuating demands, instance reservation in a single data center may incur the highly underutilized capacity. To address this issue for further cost reduction, we extend the scheme with a novel cloud broker federation based resource sharing mechanism, reallocating already reserved but unused instances to computation-intensive and short-lived tasks for continuous execution without interruption. Extensive evaluations driven by large-scale trace-based datasets demonstrate that the proposed mechanism can effectively handle large volumes of service requests, saving considerable service costs with higher reservation resource utilization.

Traditional lighting systems suffer from the problem of low energy efficiency and low illumination quality due to its disappointing management. To address this issue, in this paper, a novel traffic-flow adaptive scheme of smart lighting systems is proposed on the basis of the cyber-physical cloud system. The cyber-physical cloud system consists of the digital twin and cyber-physical system. The operation of the lighting system is simulated in the counterpart twin system with the digital twin technology. The cyber-physical system realizes data collection, information interaction, analysis, and processing, as well as complex computation and remote control. The traffic adaptive scheme works according to the brightness sequence to improves the energy efficiency of the lighting system and provide higher illumination quality for drivers. Extensive simulation results verify the proposed control scheme could improve the energy efficiency of lighting systems.

The filter-based real-time energy management method has been proved practical and widely utilized in hybrid energy storage systems. However, the determination for the cutoff frequency of the energy-split filter is challenging. In this paper, an optimal filter-based energy management strategy is proposed for a battery/ultracapacitor electric vehicle to minimize the total energy consumption. A cost function of energy consumption for the cutoff frequency is established first. Considering the working condition of ultracapacitors, dynamic programming is adopted to obtain the optimal cutoff frequency series, i.e., the optimal energy distribution between batteries and ultracapacitors. Such an off-line optimization process is carried out under different driving cycles, e.g., urban and highway road conditions. Optimization results are used to determine the optimal cutoff frequency of a real-time filter-based energy management strategy. Simulation results indicate that the proposed strategy can minimize the total energy consumption of the hybrid energy storage system with ultracapacitors state of charge limitations being guaranteed. Compared with the existing real-time energy management strategies, the energy consumption is reduced 23.85% under aggressive acceleration conditions and 7.08% under urban conditions by the proposed strategy.

This paper proposes an optimal car-following strategy for pure electric vehicles (EVs) with the aim of keeping an expected headway of the leader and reducing vehicle battery loss. In particular, a car-following system model is established. The primary task of the automatic vehicle is to follow the trajectory of the preceding car and maintain an expected headway. Then, the paper analyzes the powertrain of the electric vehicle. The loss of battery life over a period of time is proportional to the acceleration, so it takes the battery life into consideration. The Q-learning algorithm is conducted for the optimal car-following strategy using system data instead of system dynamics information. It utilizes reward function and greedy strategy to select actions to train the following vehicle to achieve car-following safety. When there is no collision in these two cars, acceleration is considered into reward function to reduce battery loss. Finally, it is verified by simulation that the proposed car-following strategy can keep good tracking, maintain the expected headway from the preceding vehicle, and reduce battery loss.

Home energy management plays a key role in demand response for residential customers to reduce the total cost via scheduling household loads energy consumption. However, excessive energy consumption by customers will bring a great challenge to the stability of the grid. To address the challenge, a day-ahead multi-agent reinforcement learning method is proposed for home energy management under a peak power-limiting. We first formulate the total cost minimization problem as a Markov game, and then a novel household loads energy consumption scheduling algorithm is proposed based on Mutil-agent Deep Deterministic Policy Gradient (MADDPG). It is worth mentioning that the proposed algorithm can achieve cooperation between agents so that it can meet the peak power-limiting constraint. Simulation results are provided in this paper to show the effectiveness of the proposed method.

The high-speed solenoid valve is a crucial module in train braking system, which is an essential factor to ensure the safe operation of trains. How to estimate the health status of the high-speed solenoid valve accurately to improve the reliability of train braking system is a challenging issue. Most related work relies on accurate physical models or large amounts of historical data. To address this challenge, this paper proposes a hybrid data-fusion estimate method for the health status of train braking system. Firstly, the physical model of the high-speed solenoid valve is established, and physical indicators which represent the working performance are extracted. Then, the dynamic driving current is processed by ensemble empirical mode decomposition (EEMD) to calculate the information entropy. Physical indicators and information entropy indicators are combined into a feature vector, which can be reduced the dimension by the t-distributed stochastic neighbor embedding (T-SNE) algorithm. Finally, the feature vector is input into the probabilistic neural network (PNN) to estimate the health status of train braking system. The proposed method is implemented in the high-speed solenoid valve degradation dataset, which collected by the train brake system experiment platform. The result shows that it is better than other methods in the accuracy and calculation efficiency.

The vehicular service support with adaptability, real-time, and low delay is crucial for connected vehicles. However, due to limited coverage of mobile edge computing servers and data processing capability of connected vehicles, vehicular services need to be offloaded to the edge server and adaptively migrate as the connected vehicle moves. Aiming at the adaptive migration service, a deep Q-learning service migration decision algorithm is proposed in this paper. The proposed algorithm can dynamically adjust the vehicular service migration decision according to traffic information. Furthermore, a service migration framework consisting of neural networks is proposed in this paper to improve the adaptability and real-time performance of the algorithm. By using this framework, training and decision-making can be carried out simultaneously in different places. Finally, compared with the two existing algorithms, extensive simulations are conducted to verify the effectiveness of the proposed algorithm.

Cell balancing is crucial for charging supercapacitor cells to prevent cells from over-charging. Most existing cell-balancing charging methods typically adopt an output feedback control, i.e., the terminal voltages of cells are directly utilized in the controller design. One limitation of these methods is the voltage drop effect when the charging is terminated, which degrades the system capacity and results in cell imbalance. To address this challenge, in this article, we propose an observer-driven charging method for supercapacitors. The switched resistor circuit is applied and is further modeled using the switched systems theory, where the RC model of cells is considered. The communication interactions among cells is modeled using the graph theory. A switching Luenberger observer is designed to estimate the voltage of the equivalent capacitor of each cell, and a consensus-based switching control law is designed to charge and balance supercapacitors. The closed-loop system model is derived using the block diagram. A laboratory testbed has been built to verify the effectiveness of the proposed charging method. Experimental results show that the proposed method can effectively alleviate the voltage drop effect when compared with existing charging methods.

In geo-distributed cloud storage systems, data replication has been widely used to serve the ever more users around the world for high data reliability and availability. How to optimize the data replica placement has become one of the fundamental problems to reduce the inter-node traffic and the system overhead of accessing associated data items. In the big data era, traditional solutions may face the challenges of long running time and large overheads to handle the increasing scale of data items with time-varying user requests. Therefore, novel offline community discovery and online community adjustment schemes are proposed to solve the replica placement problem in a scalable and adaptive way. The offline scheme can find a replica placement solution based on the average read/write rates for a certain period of time. The scalability can be achieved as 1) the computation complexity is linear to the amount of data items and 2) the data-node communities can evolve in parallel for a distributed replica placement. Furthermore, the online scheme is adaptive to handle the bursty data requests, without the need to completely override the existing replica placement. Driven by real-world data traces, extensive performance evaluations demonstrate the effectiveness of our design to handle large-scale datasets.

The accurate estimation of adhesion force between wheels and rails is an important task as it helps to the wheel-slip prevention (WSP) system preventing the wheels from locking and reducing the stopping distance. Influenced by a changing external environment, the adhesion force estimation process is complex. Thus, an extended state observer (ESO) is proposed to accurately estimate the adhesion force of railway vehicles with the modeling deviation and measurement noise. With the estimated modeling error information, an auxiliary compensation part is designed to eliminate the steady-state estimation error causing by the modeling deviation. Further, a Fal function filter is added to the ESO to deal with the effect of measurement noise. The convergence of the proposed estimation method is analyzed theoretically. The effectiveness of the designed algorithm is corroborated by simulation comparisons to other standard approaches.

Precision stopping of trains requires special attention to brake control because a pneumatic brake system of a train is highly nonlinear, hybrid and uncertain. Existing solutions to automatic train stop control ignore the pneumatic brake system or simply treat it as a system delay, which is quite far apart from the real train stopping dynamics. Moreover, the service life of pneumatic brake systems decreases fast due to the frequent changes in output of existing controllers. Thus, an adaptive nonlinear sliding mode control method is developed in this paper, which has strong applicability to nonlinear and hybrid system control synthesis due to its natural variable structure characteristic. A nonlinear integral sliding surface with adaptive updating parameters is proposed to improve the control precision and the robustness. Extensive simulations are performed to validate the effectiveness of the proposed method. The results show that the proposed algorithm outperforms a PID control algorithm in terms of stopping error and expected lifetime of pneumatic brake systems.

Microgrids incorporated with distributed generation (DG) units and energy storage (ES) devices are expected to play more and more important roles in the future power systems. Yet, achieving efficient distributed economic dispatch in microgrids is a challenging issue due to the randomness and nonlinear characteristics of DG units and loads. This paper proposes a cooperative reinforcement learning algorithm for distributed economic dispatch in microgrids. Utilizing the learning algorithm can avoid the difficulty of stochastic modeling and high computational complexity. In the cooperative reinforcement learning algorithm, the function approximation is leveraged to deal with the large and continuous state spaces. And a diffusion strategy is incorporated to coordinate the actions of DG units and ES devices. Based on the proposed algorithm, each node in microgrids only needs to communicate with its local neighbors, without relying on any centralized controllers. Algorithm convergence is analyzed, and simulations based on real-world meteorological and load data are conducted to validate the performance of the proposed algorithm.

Currently, the robot is playing an increasingly significant role in managing a warehouse. This paper proposes an optimal method to help a warehouse robot make task decisions, which aims at minimizing the whole cost of completing multiple tasks. Firstly, Abstract Transition System (ATS) is used to model the warehouse environment, and Linear Temporal Logic (LTL) formula is used to formulate the tasks of warehouse robot. Then based on the ATS and the Büchi automaton translated from the LTL formula, a Min-cost Task Decision Algorithm is proposed to obtain the task decision for the warehouse robot. The decision points out the optimal order and path for the robot to do its tasks. The effectiveness of the proposed method is validated through case studies with two kinds of tasks.

With the rapid development of inspection techniques, more emphases should be placed on the improvement of the reliability, safety and intelligence of the robot system. In this paper, a framework for the patrol robot that automatically finishes complex task and motion planning in the indoor substation is proposed. To realize real-time response to the environmental changes, the proposed framework keeps an ongoing interaction with the environment as a Reactive System (RS). The RS employs the Transition System (TS) and Nondeterministic Biichi Automaton (NBA) to create a discrete controller that bounds the acts of the patrol robot in the safe and reasonable specifications. What's more, the environment signals are treated as the trigger condition of task switching. If a new environment information is detected, our approach can automatically give a feasible plan. Then, the sensor-based mechanism of continuous controllers is guided by the discrete controller, which results in a hybrid system satisfying the high-level specification. The experiment within the LTLMoP toolkit verifies the proposed framework.

Recent advancement in the field of electrical technology and cyber-physical energy system (CPES) has brought the key towards challenging issues regarding transparency of information management and efficient allocation of energy. This paper is dedicated to a CPES that deals with an electric light rail that involves large number of distributed and globally interconnected supercapacitor energy storage modules, with the aim of efficient fusion of information, control protocol and energy. The autonomous modules estimate local state of energy and share the information via the communication network. A consensus control strategy is proposed to reach the balanced state of energy between these distributed supercapacitor modules with the advantages in terms of increasing efficiency and reducing time consumption of energy transfer. The proposed method enforces the CPES constraints specific to the particular supercapacitor modules in the electric light rails. Experimental results are provided to verify the effectiveness of the proposed state of energy balancing method.

This paper presents a novel vision servoing approach using depth maps to perform robotic motion task with field of view (FOV) constraint. The vision servoing scheme relies on the depth information available from an Red Green Blue-Depth (RGB-D) camera. With respect to the previous approaches, the proposed vision servoing approach has the advantage as follow: First, it does not require the estimation of 3D pose, which only relies on the error of depth maps, without feature matching. Moreover, this method addresses the problem of field of view constraint in the motion process by the frame pose estimation. This visual servoing system is computation efficiency, because it does not need the descriptor and estimation of the pose parameters. We drive the robot towards the target position by the depth map error minimizing. Both simulation and experimental results are provided to demonstrate the effectiveness of the proposed depth map-based vision servoing method.

With the proliferation of renewable energy sources and the elevation of environmental concerns, it is expected that microgrids will become one of the major means for residential energy supply. However, the distributed nature of microgrid operation brings new technical challenges to energy management. Endowing wireless communication capability to the distributed generation (DG) units and energy storage (ES) devices in a microgrid is beneficial for their cooperation without a centralized controller. Yet, how to establish distributed energy management without \emph{a priori} statistical information for all the DG units and loads still requires extensive research. In this paper, a reinforcement learning algorithm with cooperative neural fitting iteration is proposed for distributed energy management in microgrids via wireless networks. The reinforcement learning algorithm leverages a distributed actor- critic structure to adopt the continuous states and action spaces of a microgrid. A diffusion strategy is incorporated in the reinforcement learning algorithm to coordinate the actions of DG units and ES devices by exchanging their evaluations and decisions via a wireless network. Simulation results based on realistic renewable power generation and load data are presented to evaluate the performance of the proposed algorithm.

Recently, mobile cloud offloading is a promising technique to deal with the increasingly complex applications on mobile devices, meeting the ever- increasing energy requirements. However, cloud offloading with multiple mobile devices may cause considerable mutual interference, which may result in intolerable time delay and more energy consumption. In this paper, a novel offloading decision method is investigated to minimize the total energy consumption of mobile devices over cellular networks. Generally, mobile devices can execute a sequence of tasks in parallel with different characteristics, i.e., communication- intensive and computation-intensive. And recent advances show that only computation-intensive tasks are applicable to be offloaded for energy saving. The offloading decision issue is formulated as a NP- hard combinatorial optimization problem with the time deadline and communication quality constraints. Combining the problem linearization method and decision variables mapping from integer to the real domain, a rapid and efficient iterative approximation method is proposed, helping the cloud controller to select the best tasks for offloading aiming at minimizing the total energy consumption. Numerical simulation demonstrates that considerable energy can be saved with the proposed task offloading method in mobile cloud scenarios.

In cloud market, cloud providers offer diverse service options, including on-demand instances and reserved instances. Generally reserved instance price is cheaper than on-demand instance, but excessive reservation may result in high capacity underutilization. To improve resource utilization rate and reduce providers' service cost, a steady broker federation is necessary to propose. Broker federation coordinate cloud providers service demands in geo-distributed data centers through deciding when and how many instances to reserve. Firstly, the service cost optimization problem is formulated as a nonlinear integer programming model. Then a hybrid algorithm combining ant colony with particle swarm optimization is presented to reduce computational complexity and providers service cost. Extensive simulations driven by large-scale Parallel Workloads Archive demonstrate the effectiveness and efficiency of the hybrid algorithm.

With the explosion of application of big data, it becomes inefficient and infeasible to process big data stream by using conventional data service infrastructure and management system. Cloud computing platform having multiple geo-distributed data centers is expected to be the most efficient platform to process the big data stream. In this paper, a multi-round combinational double auction based mechanism is proposed to allocate the resources of geo-distributed data centers to multiple users with large data stream processing tasks. This mechanism combines the advantages of combinatorial auction and double auction. In the proposed mechanism, the different types of VMs can be integrated into a bundle to be bid. The auction is double and conducted from both users and data centers. Different from existed double auctions, the QoS level is taken into consideration. In addition, the multiple rounds mode is adopted, so the failed users and data centers have the chance to adjust bids and asks to participate next auction round, increasing the ratio of successful transactions. Simulation results validate the effectiveness of the proposed mechanism.

Abstract Introducing orthogonal frequency division multiplexing (OFDM) into cognitive radio (CR) can potentially increase the spectrum efficiency, but it also leads to further challenges for the resource allocation of CR networks. In OFDM‐based cognitive relay networks, two of the most significant research issues are subcarrier selection and power allocation. In this paper, a non‐cooperative game model is proposed to maximize the system throughput by jointly optimizing subcarrier selection and power allocation. First, taking the direct and relay links into consideration, an equivalent channel gain is presented to simplify the cooperative relay model into a non‐relay model. Then, a variational inequality method is utilized to prove the existence and uniqueness of the Nash equilibrium solution of the proposed non‐cooperative game. Moreover, to compute the solution of the game, a suboptimal algorithm based on the Lagrange function and distributed iterative water‐filling algorithm is proposed. The proposed algorithm can jointly optimize the process of subcarrier selection and power allocation. Finally, simulation results are shown to demonstrate the effectiveness of the proposed joint subcarrier selection and power allocation scheme. Copyright © 2015 John Wiley & Sons, Ltd.

In cloud computing, Infrastructure-as-a-Service (IaaS) cloud providers can offer two types of purchasing plans for cloud users, including on-demand plan and reservation plan. Generally reservation price is cheaper than on-demand price, while reservation plan may cause highly underutilized capacity problem. How to joint optimize the service cost and the resource utilization for clouds is a critical issue. To address this issue, a novel steady broker federation is developed to coordinate service demands in this paper. And the optimal reservation problem can be formulated as a nonlinear integer programming model. Then a fine-grained heuristic algorithm is proposed to reduce its computational complexity and obtain quasi-optimal solutions. Numerical simulations driven by large-scale Parallel Workloads Archive demonstrate that the proposed approach can save considerable costs for cloud users and improves the resource utilization for IaaS cloud providers.

The traffic signal control is the basic method to solve the urban congestions problem coming with the accelerating urbanization. For large city, it is challenge to improve the traffic signal control flexibility to adapt the real-time traffic change while to decrease the computation complexity. This paper proposes a cooperative Q-learning with function approximation(CQFA) algorithm for vehicle to infrastructure (V2I) networks. By gathering the local intersection traffic information from V2I networks and employing cooperative behaviors with neighboring intersections, the algorithm can achieve the optimal policy without any central supervising agents. To address the curse of dimensionality effectively, the Q-learning function is approximated by using a fast gradient-descent function approximation method to pick out the optimal Q-learning action. The Q-learning with Function Approximation algorithm combining the cooperative mechanism balances the urban traffic flow and uses approximating strategy to decrease the computation dimensionality. It can improve the traffic throughout, reduce the average waiting time and avoid congestions. Simulation results verify the effectiveness of the proposed algorithm.

It is a complex task to allocate the secondary users' power in OFDM-based cognitive radio networks which have many sub-carriers, especially when the channel uncertainty and coupled interference exist in cognitive networks. To address the issue, a worst-case robust distributed power allocation scheme is proposed for OFDM-based cognitive radio networks in this paper. Firstly, a robust power allocation model is constructed using worst-case approach with the uncertain channel state information. A non-cooperation game framework is introduced to optimize secondary users' rate from their own profit individually with the primary user interference constraint The variational inequality is employed to analyze the existence and uniqueness of the Nash equilibrium. The proposed scheme could optimize the power allocation under uncertain channel information and maximize secondary users' rate. And the primary user interference temperature could also be satisfied even the channel in the worst case. The simulation is presented to valid its applicability and robust in a variety of scenes.

Multimedia data transmission requires strict QoS in Wireless Multimedia Sensor Networks. However, the limited frequency spectrum and serious interference between users is a challenge in communication system. In this paper, with the introduction of cognitive radio technique in wireless multimedia sensor networks, a Q-learning based multi-relay cooperative mechanism is proposed to guarantee QoS requiements during data transmission. Firstly, according to the results of spectrum sensing of cognitive nodes, a service aware criterion is designed to judge whether a node needs cooperative relays. Secondly, the reward value of Q function is considered as the ratio of the residual energy and communication energy consumption. Then, a satisfaction function based on transmission distance and SNR is proposed. Considering the reward value and the satisfaction, cooperative relays are obtained. Simulation results show that our proposed mechanism can significantly decrease the transmission delay and balance the network load.

Effective handoff scheme is an important issue in heterogeneous wireless networks. In this paper, we propose a context-aware-based vertical handoff decision scheme for heterogeneous wireless networks. A linear fuzzy membership function is employed to normalize dynamic environmental information, such as terminal's position, speed and link quality. In this scheme, energy consumption is introduced to judge whether to handoff or not, and the candidate handoff networks are determined by the network speed threshold. We also present a comprehensive utility evaluation function to evaluate networks quality, and the weights of decision factors are calculated by analytic hierarchy process(AHP). Then, the target handoff network is selected adaptively according to the comprehensive utility of candidate networks. Specifically, we consider a knowledge base to match the environmental state with target handoff network. Simulation results show that the proposed handoff decision scheme could effectively avoid unnecessary handoff.