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With the introduction of automated driving, new requirements for the design and control of a vehicle powernet and powertrain arise, since the increasing level of driving automation shifts the responsibility for passengers’ safety from a driver towards automation system. Therefore, the functionality of the subsystems required for the automated transition to a standstill in case of a failure must be guaranteed. The work presented in this paper proposes a novel and generic system architecture for the energy management of a fail-operational powernet and powertrain in fault-free and failure case operation. The underlying control strategy is based on a concept enabling safety based range extension with the main goal to complete a driving mission at the safest possible location for the passengers. The proposed Energy Management System (EMS) approximates a driving trajectory to the destination based on route preview, which is then used for the dynamic optimization of the torque split in a powertrain with multiple motors. In this way, the total energy required for propulsion, which is also used as an input for the energy distribution, can be accurately predicted. The optimal distribution of the energy for the supply of safety-critical subsystems and powernet auxiliaries is then estimated by solving a mixed-integer optimization problem. If the desired driving mission cannot be completed, a three-level-degradation concept adapting the driving mission is applied. The individual modules of the EMS are presented in this paper and a system architecture enabling model predictive and adaptive energy distribution in a vehicle powertrain and powernet with automated fault reactions is proposed and exemplified with simulation results. [ABSTRACT FROM AUTHOR]

This paper presents a decomposed co-design optimization framework for an electric vehicle powertrain equipped with a Continuously Variable Transmission (CVT). We jointly design the sizes of the powertrain components and the energy management strategy, considering the design and the dynamics of the transmission. Specifically, we divide the resulting nonlinear optimization problem into separate hierarchical optimization subproblems leveraging the concept of Analytical Target Cascading (ATC). Furthermore, we compare the proposed decomposition-based strategy with a centralized approach, in which we solve the same problem, and show that the method presented in this paper can be a potential alternative to a centralized optimization formulation for solving the large-scale, complex integrated plant and control system design. Finally, we compare the resulting performance of using a CVT on the vehicle total-cost-of-ownership with that equipped with a Fixed-Gear Transmission (FGT), which is what is commonly implemented in current electric vehicles. Our results show that by jointly optimizing the design of the transmission along with the control strategy, the CVT yields a lower energy consumption and total-cost-of-ownership compared to that of the FGT. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have attracted enormous attention in recent years due to their potentials in mitigating energy crisis and air pollutions. However, the long battery charging time and lack of sufficient charging infrastructure highly restrict the popularization of EVs. In this context, it is promising to establish battery charging and swapping systems (BCSSs) based on the concept of battery swapping services. To optimally achieve the combined operation of BCSSs, this paper proposes a hybrid swapped battery charging and logistics dispatch model in continuous time domain. Identifying the special structure of the mathematical models of the two problems, this paper innovatively formulated the swapped battery charging strategy as the rectangle packing problem and the battery logistics model as the vehicle routing problem. The two models are closely linked by the delivery time of transporting the well-charged batteries from battery charging stations to battery swapping stations. A hybrid optimal operation model of BCSSs is further formulated as a mixed-integer linear programming model by incorporating the interaction between the battery charging and battery logistics. Finally, case studies are conducted on several BCSSs and numerical results validate the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

While the vehicular network enables geographically distributed cooperative computation, its mature implementation has long been constrained due to the lack of an effective management platform. In this paper, employing the security and privacy attributes of blockchain, we propose a novel Blockchain-enabled Large-scale Parked Vehicular Computing (BLPVC) architecture to utilize the potential solar energy and vehicular computational resources in the outdoor parking lot. However, the uneven green power supply and random arrival time of electric vehicles compose the highly complex environment. Accordingly, in this paper, to handle the efficient utilization of the distributed resources by blockchain technology, we propose an integrated optimization framework which leverages the green energy utilization and service latency limit among the processes of block generation, task computing, and communication, whereas such a design leads to the mixed-timescale stochastic optimization problem. To this end, corresponding to the dynamic solar energy arrival, we propose a shaped deep deterministic policy gradient (DDPG) algorithm to accelerate the learning rate of computational frequency control in the short-term stage; while in the long-term stage, for the mixed-integer programming (MIP) of task offloading and blockchain parameters adjustment, a series of transformation is employed to preserve convexity. Finally, experiments are carried out on Python demonstrating that the proposed scheme achieves a balanced performance between service latency and distributed resources, while the battery depreciation cost is heavily reduced. [ABSTRACT FROM AUTHOR]

In this paper, a model fusion method (MFM) is proposed for online state of charge (SOC) and state of power (SOP) co-estimation of lithium-ion batteries (LIBs) in electric vehicles (EVs). Firstly, a particle swarm optimization-genetic algorithm (PSO-GA) method is cooperated with a 2-RCCPE fractional-order model (FOM) to construct battery open-circuit voltage (OCV)-SOC curve, which only relies on a part of dynamic load profile without the prior knowledge of an initial SOC. Secondly, a dual extended Kalman filter (DEKF) algorithm based on a 1-RC model is employed to identify the model parameters and estimate battery SOC with the extracted OCV-SOC curve. Furthermore, battery polarization dynamics in a SOP prediction window is analyzed from two aspects: (1) self-recovery; and (2) current excitation. They are separately simulated using 2-RCCPE FOM and 1-RC model, and then integrated through a model fusion for online SOP estimation, which enables an analytical expression of battery peak charge/discharge current in a prediction window without weakening the nonlinear characteristic of FOM. Experimental results demonstrate the improved performance of the proposed MFM for online discharge SOP estimation, where the mean absolute error and root mean square error are only 0.288 W and 0.35 W, respectively, under the urban dynamometer driving schedule profile. [ABSTRACT FROM AUTHOR]

Drones have been attracting the attention of diverse industries thanks to their superior maneuverability. Logistics companies especially keep trying to utilize drones for fast delivery following the growing market size of e-commerce. Accordingly, methods for safely operating multi-drone have been researched, and many researchers have proposed various optimal or near-optimal routing methods. However, such methods have some problems that cause routing failures or huge routing computation time in a drone-dense space due to many collisions. In this paper, we propose a centralized framework that deals with enormous collisions and obtains collision-free paths rapidly. We first build a drone energy consumption model with a data-driven method using flight experiment data of a commercial drone to estimate the drone battery state-of-charge (SoC). Then, we develop a novel routing method that generates collision-free paths by considering both the congestion of the space and the SoC of each drone. The proposed method is inspired by the VLSI circuit routing method that connects all signal nets among thousands of logic components. Through numerous delivery routing simulations, we confirm that the proposed method achieves a maximum of 6 times higher routing success rate with a 10x faster runtime compared with the state-of-the-art optimal method. In addition, we validate that the proposed method is applicable to delivery routing problems with various drone battery capacities. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the possibility and condition of exploiting wireless-powered signals as a cover for conducting covert communications in unmanned aerial vehicle (UAV) networks. Specifically, we aim to design the hovering altitude of a UAV and its prior transmission probability to strike a balance between energy replenishment and covert communications. We derive the necessary condition for guaranteeing communication covertness subject to an energy replenishment constraint, under which the UAV’s optimal hovering and the prior transmission probability are determined. Our simulation shows that varying the prior transmission probability can significantly improve the achievable communication covertness under the energy replenishment constraint, relative to a baseline scheme with a fixed prior transmission probability. [ABSTRACT FROM AUTHOR]

Wireless charging vehicle to grid (V2G) system is not-so-futuristic. It can maintain the power supply balance and stabilize the grid through a wireless link between vehicles using batteries based on energy supply-demand statistics. Most of the existing privacy preservation methods are based on data tampering approaches (e.g., encryption, adding noise, aggregation), which are not practicable over V2G due to complexity in billing and inaccurate state estimation. In this paper, we develop a novel adaptive demand-side energy management framework by employing federated learning-based privacy preservation for the wireless charging V2G systems. Our framework learns the temporal evolution of energy consumption of dynamic charging electric vehicles in a distributed fashion and exploits the reinforcement learning model for cost-saving and reward maximization. The convergence and the privacy preservation properties of the framework are demonstrated with extensive evaluations. [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]

The use of multiple low-power fuel cells (FCs), instead of a high-power one, in the powertrain of a FC-hybrid electric vehicle (FC-HEV) has recently received considerable attention. This is mainly due to the fact that this configuration can lead to higher efficiency, durability, and reliability. However, the added degrees of freedom require an advanced multi-agent energy management strategy (EMS) for an effective power distribution among power sources. This paper puts forward an EMS based on game theory (GT) for a multi-stack FC-HEV with three FCs and a battery pack. GT is a well-approved method for characterizing the interactions in multi-agent systems. Unlike the other strategies, the proposed EMS is equipped with an online identification system to constantly update the time-varying characteristics of the power sources. The performance of the suggested strategy is investigated through two case studies. Firstly, a comparative study with two other EMSs, dynamic programming (offline), and a competent rule-based strategy (online), is conducted to realize the capability of GT. Secondly, to justify the necessity of online system identification, the degradation effect of each power source on the EMS performance is examined. The carried-out studies show that the total cost (hydrogen consumption and degradation) of the proposed strategy is almost 6% better than the rule-based EMS while keeping a reasonable difference with dynamic programming. Moreover, health unawareness of power sources can increase the hydrogen consumption up to 7% in the studied system. [ABSTRACT FROM AUTHOR]

The growing popularity and penetration of electric vehicles (EVs) bring in challenges and opportunities to the power grid. They represent a huge aggregate electricity consumption from the city power grid, while it is possible to make use of their batteries for supporting vehicle-to-grid (V2G) regulation service. Besides, there is a need to coordinate a collection of EVs in an intelligent manner through the smart transportation system in a smart city. In this paper, we propose a novel smart cross-system framework to support V2G regulation service in a smart city. Specifically, by considering electric buses (EBs) as a particular group of EVs with large battery packs installed and predictable operation schedules, we jointly solve the problems of EB allocation and scheduling with V2G regulation service. First of all, by using the deep learning approach, the short-term city traffic conditions can be predicted via the gated recurrent units (GRUs) neural network. With the predicted traffic conditions, we then formulate the EB allocation and V2G scheduling as a mixed-integer quadratic programming (MIQP) problem. In order to solve this problem effectively, we utilize the Lagrangian dual decomposition method to decouple the main problem into subproblems for EBs and devise a distributed algorithm to solve each subproblem. The simulation results show that our proposed approach is effective in EB allocation and V2G scheduling for the regulation service. Through efficient EB routing strategies under accurate predicted traffic conditions, the power fluctuations of the city power grid can be well flatten by providing the V2G regulation service. [ABSTRACT FROM AUTHOR]

The use of ultracapacitors in fuel cell electric vehicles holds the potential to promote their advancement by significantly extending the fuel cell lifetime. However, the design of suitable control strategies is critical to take full advantage of the additional degree of freedom and limit the principal causes of fuel cell degradation, such as transient operation and start-up/shut-down cycles. This paper studies the energy management of fuel cell-ultracapacitor-battery hybrid vehicles, investigating different criteria for fuel cell operation to find the best trade-off between hydrogen consumption and the number of start-up/shut-down cycles. In particular, the investigated control criteria are fixed-setpoint and charge-balancing operation. A third strategy is considered as a reference for potential performance improvements of predictive energy management. The study reveals that, in fuel cell-ultracapacitor-battery vehicles, the only significant source of fuel cell degradation are start-up/shut-down cycles. Here, harmful transients are limited because ultracapacitors enable stationary fuel cell operation without compromising battery life. Moreover, the investigation shows that the ultracapacitor and battery systems can sustain short driving cycles alone, avoiding fuel cell starts to preserve its life. The simulation results indicate that the fixed-setpoint strategy yields a better trade-off between hydrogen consumption and start-up/shut-down cycles than the charge-balancing one. Additionally, the study demonstrates that a predictive energy management strategy can significantly reduce degradation without hindering system efficiency. [ABSTRACT FROM AUTHOR]

This paper presents a novel function of energy-optimal adaptive cruise control (EACC) for electric vehicles based on model predictive control (MPC), which plans the host car's speed trajectory in real time for higher energy efficiency by taking look-ahead traffic information and road conditions into consideration. After linear MPC (LMPC) is formulated in time domain, a nonlinear MPC (NMPC) formulation in space domain is proposed in this paper to overcome the drawbacks of LMPC in time domain. To cope with computational complexity of NMPC in space domain, the nonlinear equality constraints are relaxed to inequality constraints to yield a convex optimization problem. Moreover, it is proven that the relaxed optimization problem can be recast as a second-order cone programming (SOCP) problem, for which the efficient numerical optimizers exist. The performance of NMPC is evaluated in simulation, which is compared with the time domain LMPC and a theoretically optimal receding horizon dynamic programming (RH-DP) solution. Results indicate that the proposed space domain NMPC outperforms LMPC in time domain and the optimal solution of NMPC is very close to the result of RH-DP. [ABSTRACT FROM AUTHOR]

Backscatter communication is an energy-efficient communication technique for the Internet of things (IoT) devices. It enables data transmission by reflecting the incident radio signals. In this paper, we propose a communication mode selection scheme for IoT devices that can communicate using either active transmission or backscattering. In the active transmission mode, the IoT devices can transmit data over narrowband subcarriers using power-domain non-orthogonal multiple access (NOMA). In the backscattering mode, which operates over shorter distance than active transmission, nearby user equipment (UE) devices are used as relays. The UEs receive the backscattered signals from the IoT devices and forward them to the base station. We formulate a connection density maximization problem to select the communication mode used by each IoT device. We determine the IoT device pairing for active transmission mode with NOMA and UE-IoT device association for backscattering mode. The formulated problem is a binary integer programming problem. Although it can be solved optimally, the optimal algorithm incurs exponential computational complexity. Hence, we propose a low-complexity suboptimal algorithm to solve this problem. Results show that our proposed algorithm can enhance the connection density of narrowband IoT systems by up to 64% when compared with using single communication mode. [ABSTRACT FROM AUTHOR]

In heavy-duty vehicle applications, the D+ alternator with the alternator-voltage-regulator (AVR) is widely used to supply electric loads due to lower cost and wide application possibilities. The phase voltage of D+ alternator is usually connected as auxiliary output, such as techometer; however, its wiring may short to ground through the vehicle frame due to the damage of the isolation. This would result in severe thermal events and safety issues. In this paper, a novel phase fault diagnose and protection method is proposed to prevent the thermal event and safety issue for phase auxiliary output fault in D+ alternator system of heavy duty vehicles. The method detects the voltage dip from D+ terminal without extra wiring or external components. Besides, prior art issues such as complex design, high noise sensitivity, and higher cost can be solved. The analysis and LTSpice simulation of D+ voltages in normal conditions and fault conditions set the foundation and parameter design for the proposed method. The experimental platform with a three-phase D+ alternator verified that the proposed method can diagnose and protect the ground fault while the alternator is generating power with rotating speed ranging from 2000 to 6000 rpm, and the AVR can stop the alternator output to prevent the thermal event. As soon as the fault is removed, the AVR can automatically reboot the system and return to a normal operation mode. [ABSTRACT FROM AUTHOR]

Mobile-edge computing (MEC) has been recognized as a promising solution to provide efficient communication and computation capabilities for mobile users (MUs). However, the problem relating to parallel offloading and load balancing with multiple cooperative MEC servers and massive delay-sensitive execution workloads remains to be investigated. In this paper, we study a joint parallel offloading and load balancing policy for such an MEC system. We formulate a long-term system average-cost (i.e., weighted sum of energy consumption and execution delays) stochastic programming problem under MU battery level stability and delay constraints. Our aims include optimizing the data sizes for uplink offloading transmissions for optimized communication and computation resource allocation among multi-users, and maximizing the computation capability utilization of cooperative MEC servers by load balancing. To solve this problem, we first design a Lyapunov-based centralized cost management algorithm (LYP-CCMA) to obtain the optimal system average-cost under the battery level stability constraints. Further, we propose two algorithms based on alternating direction method of multipliers (ADMM) to implement distributed resources allocation. Simulation results verify our analysis and demonstrate the superior performance of our proposed schemes over several baseline schemes. [ABSTRACT FROM AUTHOR]

In this paper, a battery charging and swapping optimization model is established for electric vehicles (EVs) and battery charging/swapping stations (BCSS). The EVs are categorized into private electric vehicles (PrEVs) and electric taxis (ETs), where the charging behaviors of PrEVs are modeled based on the Monte Carlo (MC) method, and the battery swapping (BS) strategies of ETs are optimized by bi-level dynamic game. Moreover, the voltage deviation of the power grid is considered in the load regulation process of ETs. A “path-location” model is established combining with the Floyd algorithm in the simulation and the IEEE 14-Bus system is used to derive the node voltage. Numerical results show that the proposed strategy can simultaneously increase the revenue of BCSS and ETs and reduce the voltage deviation. [ABSTRACT FROM AUTHOR]

In this paper, a detailed design methodology of a Resonant Dual Active Bridge (R-DAB) converter is proposed for battery charging applications. The converter design methodology is tailored to the charging profile of a Li-ion battery and is based on unique operating points (UOPs) that derive from a dual control method analysis. Specifically, both Pulse Frequency Modulation (PFM) and Phase-Shift Modulation (PSM) are adopted, to minimize the switching losses and the reactive power at the resonant tank. A detailed flowchart is presented, considering multiple design parameters and the UOPs, so that the resonant tank parameters are not overestimated. Simulation results of a conventional charger are presented, that validate the control method and design procedure. Finally, a 1kW|30A laboratory prototype charger is designed and evaluated, using a closed loop control system. The analysis of the experimental results proves the significance of the dual control method and verifies the proposed design methodology. [ABSTRACT FROM AUTHOR]

An efficient power battery thermal management strategy under low-temperature conditions can effectively alleviate mileage anxiety associated with electric vehicles and improve environmental adaptability. This paper aims to propose a novel coupled thermal management strategy for range extended electric vehicles (REEVs) power systems containing a range extender and a power battery. First, a novel coupled thermal management structure for REEVs power system is designed. Sequentially, a fuzzy controller is developed based on analysis of control rules for different working conditions on the basis of the structure. Furthermore, the stability of the proposed strategy is strictly proven by Lyapunov’s stability theory and phase plane analysis. Finally, a coupled thermal management model is built in AMEsim, and co-simulation between MATLAB/Simulink and AMEsim is developed. The results show that the proposed coupled thermal management strategy efficiently reduces the warm-up time and the steady-state temperature error of the engine and power battery and improves the energy utilization efficiency. [ABSTRACT FROM AUTHOR]

The hybrid energy storage system (HESS), which is composed of battery and ultracapacitor, is established to enhance the performance of an electric vehicle (EV). Moreover, several studies highlight the gains of split the traction power demand among electric motors (EMs) with different characteristics and drivetrain configurations, allowing these to operate at higher efficiency. In this paper, we unite the advantages of the multi EM drive train with the HESS power split and propose a novel two HESS system, in which each HESS is in charge of one of the drivetrain configurations (front and rear system). The EV is driven by two in-wheel EM at the front wheels and a single EM assembled with a differential transmission that moves the rear wheels. In this paper, a multiobjective optimization, based on a genetic algorithm (GA), is formulated to minimize the HESS sizing and maximize the driving range of the vehicle. Also employing the fuzzy control, this strategy is responsible to split the power between the front and rear wheels systems in a more reasonable way to satisfy the demands of better performance. The complete strategy has been developed under the FTP-75 (urban), HWFET (highway) and the US06 (high speed and required acceleration) driving cycles using the MATLAB/Simulink software environment. As compared to a similar EV with a single HESS system, the proposed dual-HESS configuration was able to improve the driving range in 145.15 km also decreasing 23.93% of the HESS mass. [ABSTRACT FROM AUTHOR]

This paper presents an DC/DC topology achieving high efficiency of over 97-% for electric vehicle (EV) fast chargers requiring a very wide output voltage range. The proposed topology consists of an unregulated resonant converter and a non-isolated buck converter. The unregulated resonant converter is responsible for the galvanic isolation for overall system, and the buck converter carries out the charging of the battery inside EV by constant-current or constant-voltage mode control. This structure provides a number of advantages such as easy realization of a very wide voltage range, no circulating-current, zero-voltage-switching operation on all the switches of the unregulated resonant converter running at high frequency and high voltage, and the achievement of high efficiency at every time over the entire battery charging profile. The availability of small passive components is a benefit because it enables the design of high power-density. In this paper, the feasibility of the proposed topology in EV fast charger applications has been verified by a prototype converter developed with the specification of 120-kHz or 50-kHz switching frequencies, maximum power rating of 20-kW, maximum charging current of 30-A, and battery voltage range of 50-650-Vdc. [ABSTRACT FROM AUTHOR]

This paper presents a novel method, advisory dynamic programming (AD-DP), for power management of a fuel cell hybrid vehicle (FCHEV). The presented method embraces a new understanding of vehicle states as driver-dependent and -independent states in time domain to define a suitable state space to be used for dynamic programming. Driver-dependent states are defined in terms of multiple characteristic parameters for vehicle speed and power demand, corporately. Driver-independent states are defined in terms of discrete values for supercapacitor's state of charge $SoC_{sc}$. Transition costs between all states in the state space are calculated offline and tabulated in look-up tables for online implementation. A state predictive model is developed based on the transition statistics of driver-dependent states for a suitable number of driving cycles. Backward calculation of the total transition cost for the predicted horizon in state space is used to define optimal power split strategy for the powertrain. The formulation of optimal control problem, in terms of situation-based solutions related to vehicle states, enables a significant reduction of computational steps and hence addresses the main challenge of real-time applications. The algorithm is adapted in terms of optimization horizon and number of discrete states for DIS to suit the real-time application. Experimental application of AD-DP, using an emulation test-rig, is conducted over different driving cycles. The obtained results reveal an improvement in energy efficiency up to 29% compared to the adaptive rule-based method. The contribution of this paper can be identified as: first, development of a corporate definition for vehicle states, that can be further implemented in optimization-based power management methods. Second, the formulation of an adaptive DP that requires lower computational steps and hence suits real-time applications in hybrid electric vehicles. [ABSTRACT FROM AUTHOR]

Active cell balancing is the process of equalizing the State-of-Charge (SoC) of all cells in a series-connected battery pack via charge transfers. While several balancing architectures and equalization strategies exist in the literature, optimization of the circuit components in a balancing architecture has not been sufficiently studied so far. Intuitively, one would select components with low parasitic resistances to minimize the energy dissipation. However, as will be shown in this paper, this might not be an optimal choice in most situations. In this paper, we propose an optimization framework that performs an efficient design space exploration to identify the energy-efficient circuit configuration of a balancing architecture. Moreover, for the first time, we propose a methodology to obtain the optimal operating frequency of an active cell balancing architecture and a control circuit to maintain the operation at the optimal frequency. For this purpose, we derive a closed-form analytical model of the charge transfer process with which we obtain the performance metrics required for our framework. Case studies show that such optimally dimensioned active cell balancing architectures operated at the optimal frequency reduce the energy dissipation by approximately 49% compared to the intuitive approach of selecting components with least parasitic resistances. [ABSTRACT FROM AUTHOR]

State of charge (SOC) estimation of lithium-ion batteries has been extensively studied and the estimation accuracy was mainly investigated through the development of various battery models and dynamic estimation algorithms. All battery models, however, contain inherent model bias due to the simplifications and assumptions, which cannot be effectively addressed through the development of various algorithms such as Kalman filtering (KF) or particle filtering (PF). Consequently, as observed in some study, battery SOC estimation using a typical extended KF in fact is not very accurate where the error could range from 5% to 10% or even more depending on the battery characteristics. This paper proposes bias characterization of the battery model, so that accuracy of the baseline model could be significantly improved and eventually SOC estimation could be much more accurate than the one only using the baseline model. This paper reports great potential for improving battery SOC estimation with the bias characterization and proposes two methods for actual bias modeling. In particular, the polynomial regression model and the Gaussian process regression model are proposed to examine the effects of the two methods on bias modeling and SOC estimation using a typical battery circuit model. Results are demonstrated in both simulation and lab testing using three battery charging/discharging profiles with the cross-validation technique. [ABSTRACT FROM AUTHOR]

In this paper, we develop centralized and distributed dynamic resource allocation schemes for an intelligent reflecting surface (IRS) aided energy harvesting (EH) system that optimize transmit power of a source node and phase-shift of passive reflecting elements of IRS. The source node randomly harvests renewable energy from the surrounding environment and performs data transmission with the harvested energy while satisfying statistical packet delay constraints in terms of maximum acceptable delay-outage probability. Our developed schemes do not require the statistical distributions of channel and energy profiles to be known and apply deep reinforcement learning (DRL) algorithm. Simulation results demonstrate the effectiveness of the proposed resource control scheme for IRS-aided EH system in different channel and energy conditions. [ABSTRACT FROM AUTHOR]

Accurate state of energy (SOE) estimation of the battery pack is very important for the electric vehicle's driving range estimation, which is still a very challenging problem under actual vehicle application conditions at present. This paper proposes a method for estimating the SOE of the battery pack based on prediction, taking into account the future voltage and temperature changes of the battery pack. Firstly, a novel definition of the battery pack SOE is proposed, which considers the inconsistency of the battery pack. Secondly, the thermo-electric lumped parameter equivalent circuit model of a single cell in the battery pack is applied. The electrical parameters are identified using the forgetting factor recursive least-squares algorithm, and the extended Kalman filter is used to estimate the SOC of each cell. Finally, the future current, voltage and temperature response of the cell in the battery pack are accurately predicted and iterative calculations are performed to get SOE under the condition that the future output power of the battery pack is predicted by the Markov chain model method. The effectiveness of the proposed method is verified under dynamic conditions at different temperatures. The results show that the above method has high precision and good robustness, and can meet the needs of actual vehicle applications. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries (LIBs) charging at low temperatures will easily accelerate the aging of LIBs and reduce the useful life. This paper applies advanced multi-factors coupling aging model and bi-objective particle swarm optimization (PSO) algorithm to derive suitable charging patterns for LIBs at low temperatures. Based on the results of orthogonal experiments, which consider the effects of charging rate, charging temperature and charging cut-off voltage on battery aging, the low-temperature capacity degradation model was established. Furthermore, two important yet competing charging objectives, including battery health and charging time, are taken into account simultaneously. These two conflicting charging objectives are traded off by solving a bi-objective optimization problem based on battery electrothermal and aging behavior. Combined with PSO algorithm, the optimal low-temperature charging strategy is obtained. As a result, the three-stage constant current and constant voltage (CC-CV) charging strategy is optimized to balance various combinations of charging objectives. Different tradeoffs are compared and analyzed based on the Pareto frontiers. The verification results show that the optimized three-stage CC-CV charging strategy can effectively offer feasible health-conscious charging with desirable tradeoffs among charging speed and battery health under low-temperature charging conditions. [ABSTRACT FROM AUTHOR]

Energy storage devices such as batteries and supercapacitors are arousing a growing interest in many areas of industrial applications since they allow improving the dynamic response and efficiency. In this sense, the availability of electric models of batteries and supercapacitors is necessary for the correct sizing, control, and energy management of power systems. In order to identify the parameters of the equivalent electric model, the identification current profile is often a pulse of charge or discharge. Subsequently, these models are validated with a current verification profile. However, the verification profile compared to the identification profile is more dynamic in amplitude and frequency, producing validation errors due to the identification signal's limited bandwidth. This paper proposes a novel Pulse – Pseudo Random Binary Sequence (Pulse – PRBS) identification current profile, with similar spectral characteristics to the verification profile, in order to improve the modeling accuracy. The parameters of a Lithium-ion battery and a supercapacitor are obtained with this new identification current profile and the Optimization Toolbox of Matlab, using the electrical models in Simulink or Simscape. Finally, the models are validated with the Urban Driving Cycle ECE-15 and the Hybrid Pulse Power Characterization (HPPC) for electric vehicle applications. [ABSTRACT FROM AUTHOR]

Recent improvements in vehicle-to-everything (V2X) communication and onboard computing power have enabled the development of control algorithms that jointly optimize the vehicle velocity and powertrain control in Connected and Automated Vehicles (CAVs), commonly referred to as the Eco-Driving problem. This paper presents a novel and computationally efficient algorithm to optimize the velocity planning and energy management in a CAV with a hybrid electric powertrain. The Eco-Driving problem is formulated as a dynamic, constrained optimization problem in the spatial domain, where information about the upcoming speed limits and road topography is assumed known. This problem is solved by embedding an Equivalent Consumption Minimization Strategy (ECMS) into a Dynamic Programming (DP) optimization to obtain a sub-optimal solution that provides results close to the global optimum at a fraction of the computational cost. Further, a multi-layer hierarchical control architecture is proposed as a path to a causal, real-time implementation. The DP-ECMS algorithm is converted into a Model Predictive Control (MPC) framework by using principles of Approximate Dynamic Programming (ADP). This causal implementation is finally benchmarked to a global optimal solution obtained with DP for different scenarios. [ABSTRACT FROM AUTHOR]

In multi-access edge computing systems with energy harvesting (MEC-EH), the mobile devices are empowered with unstable energy harvested from renewable energy sources. To prolong the life of mobile devices, as many computation-intensive tasks as possible should be offloaded to the MEC server. However, when the system states of mobile device and MEC server are unstable, e.g. poor communication channel conditions, a great number of tasks will be executed locally, leading to a long execution time. Even worse, some tasks may be dropped due to low energy levels. To address this problem, in this paper, we propose a multi-relay assisted computation offloading framework for MEC-EH systems. In this framework, a computation task can be executed by offloading to the MEC server with the help of multiple relay nodes, such as the neighboring nodes. We introduce execution cost as a performance metric to incorporate both the task execution time and task failure. We then develop a low-complexity online algorithm, namely MRACO algorithm, to minimize the average execution cost. MRACO algorithm can select the optimal execution strategy for each task from (1) executing the task locally, (2) offloading it to the MEC server directly, (3) offloading it to the MEC server with the help of the most suitable neighboring nodes, and (4) simply dropping it. Moreover, we also develop an algorithm for selecting the suitable neighboring devices to act as relays and determining the optimal task splitting ratio between them. Finally, performance evaluation shows that the proposed MRACO algorithm greatly outperforms the benchmarks in terms of both average execution time and task drop rate. [ABSTRACT FROM AUTHOR]

This paper focuses on the performance analysis of point-to-point communication, where the source node is powered with a fixed-power battery as well as a harvest-use battery. A time-switching scheme is implemented such that in each time frame the source either harvests energy or sends information to the destination node. Since switching takes place within a communication time frame, energy harvesting may not necessarily be beneficial in terms of improving the maximum achievable spectral efficiency or the energy efficiency of system. Here, we investigate and provide conditions under which harvesting can improve the system performance. We consider a scenario where the transmission power from the fixed battery is constant. Closed-form expressions for the conditions under which energy harvesting is proven beneficial are provided. The effects of important parameters, namely, time switching parameter, circuit powers, and harvested energy, on the system performance and on the condition for harvesting beneficialness, are investigated and discussed. [ABSTRACT FROM AUTHOR]

Lap time is the most relevant performance indicator in electric racing. In order to minimize it, it is important to carefully design the powertrain of the race car in terms of sizing and technology, and meticulously administer the energy available on-board. To facilitate efficient design, this paper presents a convex optimization framework to rapidly compute the minimum-lap-time control strategies for a battery electric race car. Specifically, we first identify a convex model of the electric powertrain, including the battery, the electric motor, and two transmission technologies: a fixed-gear transmission (FGT) and a continuously variable transmission (CVT). Second, assuming an expert driver, we formulate the minimum-lap-time control problem in a convex fashion for a given driving path, and compute its globally optimal solution with second-order conic programming algorithms. Third, we showcase our framework on the Le Mans track by comparing the performance achievable with an FGT and a CVT, and validate it with nonlinear simulations. Our results show that for the given setup a CVT can balance its lower efficiency and higher weight with a higher-efficiency and more aggressive motor operation, significantly outperforming the lap time achievable with an FGT. Finally, we leverage the computational efficiency of our framework to carry out parameter studies on the components, revealing that optimizing the size of the battery and the motor for the specific scenario can considerably improve the achievable lap time, and that the best transmission strongly depends on the sizing decisions. [ABSTRACT FROM AUTHOR]

All-electric ships (AES) are regarded as a promising solution for decreasing greenhouse gas emissions since they are able to utilize clean technologies like fuel cells instead of fossil fuel. A zero-emission hybrid energy system comprising the Proton Exchange Membrane (PEM) fuel cell, battery, cold-ironing, and Recuperative organic Rankine cycle (RORC) is scrutinized in this paper. To use the waste heat produced by PEM fuel cells, a hybrid system which is composed of PEM fuel cells and RORC is suggested. In order to analyze this zero-emission all-electric ship, the real data of a ferry boat, containing the load profiles and routes is used to appraise the possibility of the suggested hybrid system. The structure of the boat and energy sources, along with mathematical models, are represented and examined. Eventually, the hourly energy management of the boat for one specific day is analyzed, and also, to optimize the power dispatch, the Deep Deterministic Policy Gradient (DDPG) is applied in actor-critic architecture. To corroborate the proposed models, the MATLAB software is employed. According to obtained results, the proposed models could ensure effective and efficient ways in marine vessels due to not only their high performances but also permissible energy cost to be a zero-emission ship. [ABSTRACT FROM AUTHOR]

Accelerated battery degradation, resulting in a reduced capacity, is the main concern when discussing vehicle-to-grid (V2G) services. This paper gives a unique empirical insight into the long-term effects on batteries performing an energy demanding V2G service. To this end, an extensive method for measuring the battery capacity of series produced electric vehicles (EVs) via the DC charge port was developed. The reproducibility of the method is demonstrated with repeated tests on new and aged vehicles and the results are compared with the EVs internal estimates, read via the on-board diagnostics port. The method is also validated with a state-of-the-art battery degradation model. The capacity test method is used to evaluate the battery degradation of a fleet of commercially owned EVs which, as a part of the world's first commercial operation, have been used for V2G services for five years. The EVs have, together with ± 10 kW V2G chargers, been delivering primary frequency regulation for 15 hours per day with a daily energy throughput of 50.6 kWh. The usable battery capacity, initially found to be 23 kWh, after two years is reduced on average to 20.7 kWh and after five years is reduced to 18.9 kWh. [ABSTRACT FROM AUTHOR]

In this paper, we consider an unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) network, where the UAV is responsible for sustaining the network with all devices on the ground. The sustainable charge process operates periodically, i.e., in each working period, the UAV flies from and back to a landing position. The initial remaining energy in the batteries and battery storage limits are considered in this study. We assume only the initial remaining network lifetime (RNL) known and no further lifetime updates available, and provide a sustainable design aiming at minimizing the average UAV consumed power while sustaining the network including all devices. We formulate the problem via determining the UAV trajectory and lengths of working period and charging phase, and introduce an iterative algorithm for an efficient solution. The repeats of designed working period are proved to guarantee the sustentation of the whole network. Moreover, when UAV behaves the same in all working periods, the obtained solution is strongly influenced by the initial RNL. To get rid of this influence, we further propose a second approach, namely a transition-based sustainable design, in which a transition state is introduced for improving the RNL, and the UAV updates its behaviour in each working period. The convergence and limit of this transition approach are addressed. After transitions, the network shifts to a steady state, and operates periodically as in the first design. Finally, through simulations, we validate the sustaining performance of both designs and the performance improvement via the transition approach. [ABSTRACT FROM AUTHOR]

The benefits of intermediate couplers (IC) in inductive power transfer (IPT) systems are well documented in the literature. However, the lack of guideline rules makes the selection process of the passive components and operation mode difficult. This paper presents an iterative design methodology based on the equivalent mathematical model of the system to determine the optimal magnetic coupler (MC) dimensions and resonant tanks parameters that operates the system in a quasi constant current mode. It was found that the transconductance function of a series-series (SS) with an intermediate coupler exhibits a quasi-load independent characteristic when the reflected reactance from the receiver side is null. The design methodology then combines the self and mutual coupling profiles of the MC, obtained from a finite element analysis (FEA) tool, with the system specifications and constraints to find all possible solutions. A set of selection metrics are proposed to identify the optimal solution. This work also presents an identification method of the load and mutual inductances based only in the information available from the transmitter side. The proposed method uses the steady-state equations and does not require any form of communication to the receiver side. An experimental prototype was built and assembled in a converted combustion vehicle with efficiency of 89 $\%$. [ABSTRACT FROM AUTHOR]

With the increase in the capacity and power rating of batteries in today's HEVs and EVs, the battery thermal management (BTM) system bears growing importance in vehicle safety and efficiency. A practical BTM system is commonly coupled with the passenger cabin heating-ventilation-air-conditioning (HVAC) system, which makes a major energy consumer and a challenging control object. Thanks to the connected and automated vehicle (CAV) technology, predictions of the vehicle speed profile and power trajectory can be obtained, providing the possibility for predictive control of the BTM and HVAC coupled system in order to maintain battery safety, passenger comfort, and to reduce energy consumption. However, the tradeoff among higher energy saving potential and wider control range from a long and sparse horizon, and higher accuracy from a short and dense horizon is inevitable in the conventional predictive control. In this paper, a two-layer predictive control strategy for warm/hot weather is proposed to address the aforementioned tradeoff. The upper layer controller firstly plans the optimized battery temperature trajectory according to the look-ahead speed preview and battery power profiles provided by the CAV network and the integrated BTM and HVAC system efficiency surface from off-line data. Then the lower layer model predictive controller tracks the planned trajectory and cabin temperature reference while enforcing the energy consumption optimization. Simulation results demonstrate that the proposed strategy exhibits accurate battery and passenger cabin temperature reference tracking while achieving up to 10.47% HVAC energy saving comparing to a baseline control strategy with UDDS profile and typical driving conditions. Implementation of the proposed strategy on a real-time vehicle emulator based on rapid control prototyping (RCP) and hardware-in-the-loop (HIL) platforms demonstrates the real-world implementation capability of the proposed two-layer framework. [ABSTRACT FROM AUTHOR]

The present paper is dedicated to the investigation of a predictive Equivalent Consumption Minimization Strategy. The objective is to determine the torque split between the internal combustion engine and the electric machine of a hybrid vehicle. The energy management is formulated as a receding optimization problem. To avoid a complex prediction of the vehicle speed and acceleration over time, the slow dynamic of their distribution is exploited. A rational tuning of the algorithm parameters is proposed as well as some improved implementations. The number of individual operations (additions, multiplications, interpolations, etc) required per seconds is discussed. Finally, the energy management algorithm energy consumption are assessed over different driving cycles, including one with a \boldsymbol 15406  ${km}$ length obtained using GPS measurements. A comparison with an adaptive Equivalent Consumption Minimization Strategy is provided. The predictive Equivalent Consumption Minimization Strategy allows controlling the state of charge close to a (possibly time varying) set point while providing low fuel consumption. [ABSTRACT FROM AUTHOR]

This paper presents predictive energy management of hybrid electric vehicles (HEVs) via computationally efficient multi-layer control. First, we formulate an optimization problem by considering driveability and a penalty for using service brakes in the objective function to optimize gear, engine on/off, engine clutch state, and power-split decisions subject to constraints on the battery state of charge (SOC) and charge sustenance. Then, we split it into two control layers, including a supervisory control in a higher layer and a local power-split control in a lower layer. In the supervisory layer, a gear and powertrain mode manager (PM) is designed, and optimal gear, engine on/off and clutch states are obtained by using a combination of dynamic programming (DP) and Pontryagin's minimum principle (PMP). Moreover, a real-time iteration Secant method is proposed to calculate optimal battery costate such that the constraint on charge sustenance is satisfied. In the local controller layer, a linear quadratic tracking method (LQT) is used to optimally split power between the engine and the electric machine and keep battery SOC within its bounds. [ABSTRACT FROM AUTHOR]

This paper presents a hierarchical power allocation method (HPAM) for dual-stack fuel cell (FC) hybrid locomotive powertrain. In a high-power FC application such as rail transportation, the use of multi-stack system is able to provide the primary and stable demanded power. The battery is adopted as the energy storage source (ESS) to fulfill the transient power demand fluctuations. Besides, considering that the performance of the stack will change with the operating conditions, this study also searches an online identification method based on forgetting factor recursive least square (FFRLS) algorithm to update the system parameters of the dual-PEMFC system in real time. In addition, sequential quadratic programming (SQP) scheme is employed in this study to search for the optimal solution of nonlinear constraint problems. Furthermore, it is also to ensure that the proton exchange membrane fuel cell (PEMFC) systems can operate as much as possible in the high efficiency range, and the final battery state of charge (SOC) can be close to the initial SOC. Moreover, this study also adopts the equivalent consumption minimization strategy (ECMS) as benchmark to foreground the superiority of the presented method. A reduced-scale hybrid locomotive powertrain test bench is developed to demonstrate the practicability of the developed HPAM. It can be seen from the experimental results that, the proposed HPAM can optimize PEMFC systems efficiency and performances, and reduce system fuel consumption. [ABSTRACT FROM AUTHOR]

This paper aims to resolve a clear and unrealistic limitation found in recent works that have proposed solutions to the massive demand for the autonomous electric mobility-on-demand (AEMoD) services. Namely, this paper will focus on the matched charge-to-trip only service by enabling sub-class service; i.e., allowing vehicles to serve customer classes with trips needing less charge. The paper proposes a multi-class management system with a new queuing model for one city zone, and then derives the stability conditions of the system. Proportions of vehicles from each class that will immediately serve customers with sub-class service or full/partial charge are modeled, and the maximum expected response time of the system is then minimized by optimizing these vehicle service decisions. The advantages of the new model are then compared with previously proposed schemes and non-optimized models through simulations. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to review the recent literature regarding the effects of low temperatures on Lithium ion (Li-ion) batteries for electric vehicle, plug-in hybrid electric vehicle, and hybrid electric vehicle applications. Special consideration is given to nine major effects that directly or indirectly impact the battery use at low temperatures and it is presented that they are correlated to each other. The main discussions in this paper are capacity loss, power loss, life degradation, safety hazard, unbalanced capacity, charging difficulty, thermal management system complexity, battery model and state estimation method complexity, and incremental cost. The effects correlations and possible solutions are explained to provide a detailed, yet broader understanding of the Li-ion batteries low-temperature operating scenarios. [ABSTRACT FROM AUTHOR]

State-of-the-art drone technologies have severe flight time limitations due to weight constraints, which inevitably lead to a relatively small amount of available energy. Therefore, frequent battery replacement or recharging is necessary in applications such as delivery, exploration, or support to the wireless infrastructure. Mobile charging stations (i.e., mobile stations with charging equipment) for outdoor ad-hoc battery charging is one of the feasible solutions to address this issue. However, the ability of these platforms to charge the drones is limited in terms of the number and charging time. This paper designs an auction-based mechanism to control the charging schedule in multi-drone setting. In this paper, charging time slots are auctioned, and their assignment is determined by a bidding process. The main challenge in developing this framework is the lack of prior knowledge on the distribution of the number of drones participating in the auction. Based on optimal second-price-auction, the proposed formulation, then, relies on deep learning algorithms to learn such distribution online. Numerical results from extensive simulations show that the proposed deep-learning-based approach provides effective battery charging control in multi-drone scenarios. [ABSTRACT FROM AUTHOR]

Electric motorcycles use a battery storage system and a controlled inverter (motor drive) to drive the wheels. Given that the sale of electric motorcycles is expected to grow significantly over the next decade, the aim of this research is to add a supercapacitor storage system to this simple network in order to extend the autonomy of the motorcycle. This paper analyzes and demonstrates the benefits of two different structures for adding a supercapacitor bank to a lithium-ion battery pack: a passive filter and a buffer for the battery. The advantages of each structure are presented and design guidelines are provided. In addition, we propose a synthetic inductor filter typology based on the two analyzed structures in order to increase the autonomy of the motorcycle. Simulations and experimental results using real data validate the analysis. This paper then uses real data extracted from an electric motorcycle to compare the four topologies: batteries only, the passive filter, the buffer, and the synthetic inductor semi-active hybrid storage system. The comparison shows that battery capacity is extended up to 7.8% with a smoother current profile. However, due to the losses from the added converter in cascade, the net capacity of the whole system is extended only up to 3.7%. Therefore, improvements in the dc/dc converter can further increase the entire system capacity. [ABSTRACT FROM AUTHOR]

This paper proposes the series–series compensation topology to realize a low-voltage and high-current inductive power transfer system (IPT) for the automatic guided vehicles (AGVs). Since the height of the AGV chassis is in the tens of mm, the coupling coefficient is close to 1, resulting in a tightly coupled IPT system. This paper has three main contributions. First, it reveals that the high-order harmonic currents in a tightly coupled IPT system could be very significant. Second, it quantifies the impact of the high-order harmonic currents on the efficiency, which shows the efficiency can be reduced. Third, it proposes an effective method to design the parameters in order to reduce the harmonics and maintain high efficiency. Aiming at the charging of AGVs, a prototype is constructed. The magnetic coupler size is 220 mm × 220 mm × 10 mm. When the airgap is 10 mm, it achieves 1.8-kW power transfer with a dc–dc efficiency of 89.9% from a 400-V dc source to a 24-V dc load, and the charging current is 74 A. When the airgap varies from 5 mm to 15 mm, the power variation is within ±350 W, and the efficiency is not affected. The fast Fourier transform analysis of the experimental currents also validates the theoretical analysis and the simulation results. [ABSTRACT FROM AUTHOR]

The electric vehicle battery life is generally affected by its rapid utilization, accumulated heat and overall energy throughput. Inclusion of ultracapacitor along with battery provides enhanced flexibility in operating and utilizing the battery more adequately. In such dual storage systems, the energy management scheme plays an important role in determining the overall system efficiency. In this paper, a new concept to manage the state of charge (SOC) of ultracapacitor is proposed. The aim of the proposed approach is, based on the vehicle velocity at acceleration, to regulate the amount of energy that ultracapacitor should support in order to ensure its availability for an extended period. A two-stage artificial neural network based strategy is developed to achieve the aforementioned ultracapacitor SOC control. The battery energy throughput and its temperature rise during a complete battery discharge are taken as key parameters for the evaluation. It is shown in the paper that with the proposed approach, the energy storage system efficiency and battery life can be improved significantly. Finally, the applicability of the proposed concept is demonstrated experimentally. [ABSTRACT FROM AUTHOR]

This paper deals with the problems of the Plug-in Electric Vehicles charging costs in housing sector. A review of the optimal strategies is proposed and as such six algorithms are presented: three smart unidirectional and three smart bidirectional charging algorithms where the Vehicle-to-Grid (V2G) and the Vehicle-to-Home (V2H) concepts were exploited. In addition an innovative V2G algorithm named Optimal Logical Control (V2G-OLC) is introduced in this paper. This latter is dedicated to the French energy billing system within the peak/base hour's contract. All the algorithms are tested over a set of 1000 data composed of real elements with four different daily energy price profiles. The results show a great efficiency of the V2G-OLC algorithm compared to the traditional optimal charging strategies. Indeed, the results show a reduction of 47.94% of the average charging cost with a unitary selling/buying price ratio in comparison to the simple charging at 230V-32A. [ABSTRACT FROM AUTHOR]

This paper proposes a novel emulation system for emulating the power system in the fuel cell electric vehicle. The major purpose of the proposed emulation system is to test the dc–dc converter between the fuel cell and high-voltage powertrain. The bidirectional buck–boost type dc–dc converter is applied in the emulation system as the basic elements. A modularized approach is presented for easily scaling up and down the power level of the emulation system. Meanwhile, an energy loop structure is implemented in the proposed emulation system for power capability boosting purpose. Because of the proposed energy loop configuration, the emulation system offers long-term running capability with high power rating without the grid connection. A neutral current control mechanism is applied in this paper to further enhance the long-term testing capability as well as extending the lifespan for the battery packages. The simulation studies are proposed in this paper to validate the control mechanisms of the emulation system. A 10 kW hardware prototype has been built and the experimental tests are presented for validating the functionalities of the emulation system. [ABSTRACT FROM AUTHOR]

The contemporary problem of scheduling the recharge operations of electric vehicles (EVs) has gained a lot of research attention. This is particularly true given the governmental and industrial confidence in a bright future for EVs accompanied with the widespread installation of an enormous number of charging stations across the world. As such, this paper addresses the delay-optimal scheduling of charging EVs at several charging stations (CSs) each with multiple charging outlets. At first, a centralized optimization framework is formulated using an integer linear problem (ILP) that accounts for the delayed arrival of EVs to CSs and the randomness in the requested recharge time interval. Simulation results showed the efficacy of the ILP model when compared to naive as well as sophisticated scheduling heuristics. Next, motivated by the scalability issues of the ILP model, this paper then proposes a distributed game-theoretical approach where each EV communicates with its selected CS and iterates on modifying its strategy until all EVs converge to selecting an appropriate CS that minimizes their waiting times for receiving services. The distributed game-theoretical approach recorded promising results especially when compared to the well-known shortest job first scheduling algorithm. Further, unlike the other approaches, which normally are centralized and suited for offline scheduling, the game-based method is suited for online scheduling since it played at anytime a batch of EVs requests charging services. The running time of the game is remarkably small and outperforms all other heuristics and its convergence to Nash equilibrium is guaranteed after only small number of iterations. [ABSTRACT FROM AUTHOR]

In this paper, based on model predictive control algorithm, a torque demand control approach is proposed to optimize driving energy consumption of battery electric vehicle, which consists of demand control approach and model predictive controller. The demand control approach is developed to compute the driving mode and the desired vehicle speed. For the design of control law, a novel driving dynamics model of battery electric vehicle is formulated into a set of differential equations by vehicle speed, the front and rear wheel speed. A model predictive control law is designed to compute the optimized torque of electric motor. The torque demand model predictive control algorithm is downloaded into vehicle control unit, which is equipped on the battery electric vehicle for experimental validation. The New European Driving Cycle is utilized to test the control law on the real road. The experimental results indicate that the proposed model predictive controller has a preferable performance in reducing energy consumption, which can improve 1.81% over the original control strategy in the urban road cycle and 1.67% in the city highway condition. It can be considered that the torque demand model predictive control approach is a good candidate for driving energy optimization of battery electric vehicle. [ABSTRACT FROM AUTHOR]