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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 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]

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]

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]

This paper presents an offline state-of-health (SoH) estimation based on charge transfer resistance for high-power lithium-ion (Li-ion) batteries, such as lithium iron phosphate (LFP) batteries. As shown in the experimental results, the charge transfer resistance has a great aging change with battery degradation and good abilities against state-of-charge (SoC) drift and external resistance variation in the impedance parameter set of a single-time-constant equivalent circuit model (ECM), including ohmic resistance, charge transfer resistance, double-layer capacitance, and time constant, for SoH estimation. A fast and efficient three-point (TP) impedance extraction method is also proposed in this paper for accurately extracting the charge transfer resistance in offline SoH estimation. The results of long-term cycling test demonstrate that the TP impedance extraction method can successfully indicate the SoH of LFP batteries with low estimation error of 6.1%. [ABSTRACT FROM PUBLISHER]

The fuel efficiencies of Hybrid Electric Vehicles (HEVs) depend heavily on the control concepts, especially on energy management strategies, because the system efficiency of the vehicle is determined by the exploitation of the electric energy. Control concepts based on optimal control theories like Dynamic Programming (DP) or Pontryagin's Minimum Principle (PMP) have been widely studied over the last two decades, and it has been proven that these control concepts provide near-optimal solutions if future driving conditions are given prior to actual driving. However, it is difficult to apply to real vehicles unless the future driving conditions are known. In this paper, a well-known control strategy based on PMP has been developed, which needs only current driving conditions. The significance of the study is that the control concept introduced herein was evaluated in the IEEE VTS Challenge 2018 competition. It produced the best results in terms of the total energy consumption, where 52 controllers had been competing to minimize the energy consumption in unknown driving cycles. Although the control system was designed from traditional PMP-based control, it becomes a practicable and effective solution by using an adaptive concept for balancing State Of Charge (SOC) of a battery. This study is an extended version of the study presented during IEEE VPPC 2018 and provides the development process of the control concept used in the controller during the IEEE VTS Challenge 2018. [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]

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]

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]

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 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]

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 considers a parallel hybrid electric truck where the tractive force delivered to the wheels can be provided by a conventional internal combustion engine (ICE) and/or a motor generator (MG). This hybrid truck is equipped with a clutch system that is capable of decoupling the ICE and the MG from the drivetrain to improve the fuel reduction performance, compared with a hybrid truck without a separate clutch for the MG. In the considered hybrid truck, the energy management strategy utilizes the battery to reduce the fuel consumption and the associated emissions. As the battery life is limited, the vehicle owner will suffer from extra costs for battery replacement. Henceforth, it is necessary to guarantee sufficient battery life. This paper develops a quasi-static battery cycle-life model and formulates a model-based integrated energy management (IEM) strategy. This IEM strategy optimizes the power split (between the ICE and the MG) and the operation of the clutch system to minimize the hybrid truck fuel consumption while guaranteeing the requested battery life. The problem of fuel minimization with battery life guarantee is analytically solved using optimal control theory. Mathematical and physical arguments are used to explain the compromise between fuel reduction and battery life preservation. Simulation results demonstrate that the IEM strategy will guarantee the requested battery life for different driving cycles while allowing appropriate hybrid powertrain operations for fuel minimization. Moreover, the results also show the fuel reduction improvement from decoupling the MG from the drivetrain using the clutch system. [ABSTRACT FROM PUBLISHER]

This paper examines the problem of optimizing hybrid electric vehicle (HEV) power management for fuel economy. This paper begins by presenting a pseudospectral algorithm to solve this optimization problem. Compared with traditional dynamic programming (DP)-based optimal power management approaches, this algorithm has two key advantages: It is numerically more efficient, and it furnishes both the optimal state and costate trajectories. Building on the second advantage, this paper proposes a two-level strategy for optimal power management in vehicles commuting along fixed routes. The upper level of the proposed strategy is a costate adaptation algorithm employing pseudospectral optimization, whereas the lower level is an instantaneous optimization controller employing Pontryagin's minimum principle (PMP). This paper shows its pseudospectral optimization algorithm and two-level strategy using numerical simulation for a series hybrid school bus. Parameters of the bus powertrain model are obtained from experimental component tests performed at the National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology. Simulation results show that the pseudospectral method reaches a solution close to DP with higher computational efficiency. Furthermore, the proposed two-level strategy is capable of adapting vehicle power management based on road-grade predictions, i.e., an attractive feature compared with more traditional online hybrid power management approaches such as the use of proportional integral derivative (PID) control for adaptive equivalent fuel consumption minimization [PID equivalent consumption minimization strategy (PID-ECMS)]. [ABSTRACT FROM PUBLISHER]

Electric vehicles based on high-energy lithium-ion batteries often exhibit a substantial loss in performance at subzero temperatures: Due to slower electrochemical kinetics, the internal resistances of the batteries rise and diminish available power and capacity. Hybrid energy storage systems (HESSs) can be used to overcome these weaknesses. In this paper, the performance of two HESSs, combining a high-energy lithium-ion battery with either a high-power lithium-ion battery or a lithium-ion capacitor, has been investigated experimentally for a driving scenario at various temperatures. Both configurations enable driving at -20 °C, which was not possible without hybridization. The HESS using the high-power lithium-ion battery provides a substantially higher driving range due to its higher energy density. An analysis of different operating strategies has helped to maximize the driving range: Discharging the high-energy battery with a constant current and keeping the high-power cell at a higher state of charge (SoC) extend the driving duration, as the requested driving power can still be provided at a lower SoC of the high-energy battery. In addition to the HESSs, two energy storage systems without hybridization, consisting of different generations of high-energy lithium-ion cells, have been examined to disclose improvements in battery technology. These improvements narrow the benefits of HESSs, as the high-energy batteries have become less reliant on the support of an additional high-power device. Although HESSs lose importance for current lithium-ion battery systems, they can be a valuable option for next-generation lithium batteries, which are expected to provide higher energy densities but exhibit reduced rate capability. [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]

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 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]

When it comes to content access using intervehicle communications (IVC), data will mostly flow through roadside units (RSUs) that deployed in our cities. Unfortunately, the RSU coverage is expected to be rather scattered. Instead of relying on RSUs only, this paper investigates the possibility of exploiting parked vehicles to extend the RSU service coverage. Our approach leverages optimization models aiming at maximizing the freshness of content that downloaders retrieve, the efficiency in the utilization of radio resources, and the fairness in exploiting the energy resources of parked vehicles. The latter is constrained to not excessively drain parked vehicle batteries. Our approach provides an estimate of the system performance, even in those cases where users may only be willing to lease a limited amount of their battery capacity to extend RSU coverage. Our optimization-based results are validated by comparing them against ns-3 simulations. Performance evaluation highlights that the use of parked vehicles enhances the efficiency of the content downloading process by 25%–35% and can offload more than half the data traffic from RSUs with respect to the case where only moving cars are used as relays. Such gains in performance come at a small cost in terms of battery utilization for the parked vehicles, and they are magnified when a backbone of parked vehicles can be formed. [ABSTRACT FROM AUTHOR]

Batteries are the most commonly used electrical energy storage devices. The cost and performance of an electric vehicle (EV) are strongly affected by the proper selection of technology, number, and arrangement of the battery cells used. This paper formulates the problem of optimal sizing of the battery unit (BU) for a plug-in EV (PEV), given the specifications of the vehicle, battery cell data, and drive cycle. This paper employs a metaheuristic optimization algorithm to find the optimal size of the BU with the objective of minimizing the overall cost. Feasibility of the optimization results in terms of respecting the constraints is verified via simulation. The developed optimization platform is highly flexible and allows use of different vehicle designs, component data, and drive cycles. [ABSTRACT FROM PUBLISHER]

With the proliferation of sensors, such as accelerometers, in mobile devices, activity and motion tracking has become a viable technology to understand and create an engaging user experience. This paper proposes a fast adaptation and learning scheme of activity tracking policies when user statistics are unknown a priori, varying with time, and inconsistent for different users. In our stochastic optimization, user activities are required to be synchronized with a backend under a cellular data limit to avoid overcharges from cellular operators. The mobile device is charged intermittently using wireless or wired charging for receiving the required energy for transmission and sensing operations. First, we propose an activity tracking policy by formulating a stochastic optimization as a constrained Markov decision process (CMDP). Second, we prove that the optimal policy of the CMDP has a threshold structure using a Lagrangian relaxation approach and the submodularity concept. We accordingly present a fast Q-learning algorithm by considering the policy structure to improve the convergence speed over that of conventional Q-learning. Finally, simulation examples are presented to support the theoretical findings of this paper. [ABSTRACT FROM PUBLISHER]

Recently, as plug-in hybrid electric vehicles (PHEVs) take center stage for the eco-friendly and cost-effective transportation, commercial PHEV charging stations will be widely prevalent in the future. However, previous studies in the fields of the management of PHEV charging stations have not synthetically taken practical charging systems into account. In this paper, we study the profit-optimal management of a PHEV charging station under the realistic environment addressing not only various types of vehicles but waiting time guarantee for PHEV customers as well. This paper is first to jointly take into account pricing for charging services, scheduling of reserved vehicles to PHEV chargers, dropping of reserved vehicles, and management of the energy storage in a unified framework that contains key features of a practical PHEV charging station. Based on this framework, we develop an algorithm to find the parameters required for charging management by invoking the “Lyapunov drift-plus-penalty” technique. Through theoretical analysis, we prove that the proposed algorithm achieves close-to-optimal performance under particular conditions by exploiting opportunism of time-varying arrival of charging vehicles, price of electricity, and renewable energy generation, but it requires no probabilistic future information. Finally, we find several significant messages via trace-driven simulation of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

The twin goals of maximizing fuel economy (FE) and improving consumer acceptance by reducing the cost of the energy storage system (ESS) in a series hybrid electric vehicle (SHEV) powertrain is addressed here by using energy storage as a means for filtering drive-cycle power demands on the engine, rather than an energy source for supplying an all-electric drive mode. The concept is intended to minimize, if not eliminate, the battery in the SHEV without resorting to full-range proportional control of the engine and generator. An initial optimization study reported for a mid-size SHEV showed that a 4.5-kWh lithium-ion battery pack was still required. In this paper, a sports-car-class SHEV was studied, where the challenge is to reduce the size of the ESS even more because the available space allocation is only one fourth that of the mid-size vehicle. In this paper, a controller is developed, which allows a hybridized SHEV to be realized with a light ESS. The controller includes a duty-cycling feature that manages the engine performance in multiple efficient regions and a bandwidth-limited (BWL) proportional controller feature that limits low-bandwidth battery current. The performance of the controller has been validated for a SHEV powertrain model with an 80- \textV\mathrm{dc} 1.125-kWh battery, plus an 80- \textV\mathrm{dc} 46.4-F ultracapacitor module using a customized Autonomie vehicle model. The results show that the combined FE of the new design is increased by 13%, compared with the corresponding FE in the equivalent conventional vehicle. Additional FE improvement is possible by reducing the engine size further to reflect the average power demand imposed by real drive cycles. [ABSTRACT FROM AUTHOR]

This paper proposes a blended rule-based energy management system (EMS) for a plug-in hybrid electric vehicle (PHEV). The proposed EMS is formulated over driving information and vehicle trip energy and not over vehicle speed profiles, as usually seen. The proposed EMS design structure and strategy is described followed by its evaluation. This is the first time a platform for a rule-based acausal EMS has been designed. Performance metrics such as the fuel economy and the number of engine stop–starts are compared with conventional rule-based EMS over real-world destinations with uncertain trip demand. Its performance metrics compared with a conventional EMS for a full parallel PHEV was found to be superior in this paper. [ABSTRACT FROM PUBLISHER]

In an energy harvesting vehicular network, efficient file transfer between two charging stations is challenging due to the lack of communication infrastructure. Naturally, a source station can allocate the packets to a set of vehicles, which serve as relays to opportunistically deliver these packets to the destination station while performing their own tasks. In this paper, we show that, with packet-level erasure coding (e.g., fountain codes), only a few redundant packets are required to achieve a significant reduction in file delay. As more redundant packets are added, however, the file delay improvement diminishes, and the storage cost rises. In both the single and multiple-destination scenarios, the fundamental tradeoffs between coding and file delay are characterized. For the multiple-destination scenario, there exists a unique and optimal coding rate that minimizes the average file delay. We propose a greedy algorithm that can achieve this minimum delay in practice. Finally, the proposed tradeoffs are verified through intensive numerical experiments. [ABSTRACT FROM PUBLISHER]

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]

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]

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]

Some electrified vehicles, for example the Toyota Prius, integrate a dc–dc converter between the battery pack and the traction inverter to increase the powertrain efficiency. However, adding this extra converter adversely affects the cost and power density of the powertrain due to the additional components, such as the switches and the inductor, that are expensive and heavy in high-power converters. The concept of the multi-source inverter was previously introduced as an alternative solution to reduce the power rating of the dc–dc converter while keeping similar powertrain performance. In this paper, the implementation of the multi-source inverter in a hybrid power-split powertrain is discussed. The operating modes during both dc–ac and ac–dc conversions are detailed and a prototype is experimentally tested with a load. New control strategies of the proposed powertrain show that three out of five operating modes bring new opportunities in terms of battery charging and power rating reduction of the dc–dc converter. A vehicle-level simulation model is developed in MATLAB/Simulink to quantify the benefits of the proposed topology. Simulations are performed for four drive cycles to compare the power profile of the dc–dc converter of the suggested powertrain with the Toyota Prius. The average power transferred by the dc–dc converter can be reduced by up to 25 $\%$ in most drive cycles tested. [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]

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]

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 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]

The energy management of plug-in hybrid electric vehicles (HEVs) is commonly divided into two modes: charge-depletion mode and charge-sustaining mode. This paper presents the optimal adaption law for any type of adaptive energy consumption minimization strategy (ECMS) in charge-depletion mode for plug-in HEVs. To present the optimal law, a particular adaptive ECMS is selected, known as catch energy saving opportunity (CESO). CESO has previously been introduced for series and parallel HEVs in charge-sustaining mode. Here, by introducing the optimal adaption law, CESO strategy is expanded to charge-depletion mode for plug-in HEVs. [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 we propose a one-dimensional convolutional neural network (CNN)-based state of charge estimation algorithm for electric vehicles. The CNN is trained using two publicly available battery datasets. The influence of different types of noises on the estimation capabilities of the CNN model has been studied. Moreover, a transfer learning mechanism is proposed in order to make the developed algorithm generalize better and estimate with an acceptable accuracy when a battery with different chemical characteristics than the one used for training the model, is used. It has been observed that using transfer learning, the model can learn sufficiently well with significantly less amount of battery data. The proposed method fares well in terms of estimation accuracy, learning speed and generalization capability. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive equivalent consumption minimization strategy (ECMS) and a linear quadratic tracking (LQT) method for optimal power-split control of a combustion engine and an electric machine in a hybrid electric vehicle (HEV). The objective is to deliver demanded torque and minimize fuel consumption and usage of service brakes, subject to constraints on actuator limits and battery state of charge (SOC). First, we derive a function for calculating maximum deliverable torque that is as close as possible to demanded torque and then reduce the number of control inputs to one while minimizing the usage of service brakes in the case of negative demanded torque. Next, we propose modeling SOC constraints by tangent or logarithm functions that provide an interior point to both the ECMS and the LQT. Then, we show that the resulting objective functions are convex, and we propose sub-optimal analytic solutions for the optimization problem by applying their second order approximation about a given reference. We also consider robustness of the controllers to measurement noise using a simple model of noise. Simulation results of the two controllers are compared, and their effectiveness is discussed. [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]

Electric vehicles will become a popular mode of travel when petroleum prices increase and global warming intensifies. However, the short driving range of electric vehicles remains a challenge. To avoid battery depletion, navigation systems route electric vehicles to surrounding charging stations for battery exchange or recharge. Nonetheless, zero inventories and occupied sockets may result in variable charging time and longer travel time. Conventional navigation approaches have been unable to respond to variable charging time because these approaches do not consider the charging times and energy consumption of electric vehicles. Navigation systems can manage shared information, traffic, and battery life, and can improve the mileage per kilowatthour of electric vehicles. In this paper, an electric-vehicle navigation system (EVNS) based on an autonomic computing architecture and a hierarchical architecture over vehicular ad-hoc networks (VANETs) is proposed. In addition to moving energy, the proposed EVNS considers air conditioner energy to predict the state of charge (SOC) because air conditioning is the most demanding auxiliary load in electric vehicles. Moreover, this paper proposes a time-dependent routing algorithm that considers shared information. The results show that the proposed EVNS method results in 31.15% and 9.52% improvements in mileage compared with the shortest path first (SPF) method and the distributed method in Manhattan, New York City, NY, USA, respectively. Furthermore, the proposed EVNS method results in 32.39% and 20.67% improvements in mileage compared with the SPF method and the distributed method in Taipei, Taiwan, respectively. [ABSTRACT FROM AUTHOR]

Fuel cells have increasingly become an attractive option for providing clean electricity at relatively high efficiency. This paper discusses the key features of a proton exchange membrane fuel cell for a fuel cell-based auxiliary power unit (FC-APU) to supply a portion of electric loads in a vehicle. The FC-APU includes two converters: a unidirectional converter to control the fuel cell current and a bidirectional converter to control the dc-link voltage and battery current. This paper proposes 1) an energy management system (EMS) to manage the power transfer between the load and energy sources with emphasis on low stress on the fuel cell, 2) an iterative method to size the fuel cell and battery for a given load profile, based on the proposed EMS, and 3) a current estimator to reduce the effects of system transients on the dc-link voltage and increase system modularity. Simulation and experimental results validate the proposed EMS, sizing, and controller design. [ABSTRACT FROM PUBLISHER]

An energy internet is a system that enables energy sharing in a distribution system such as the Internet. It has been attracting a lot attention from both academia and industry. The main purpose of this paper is to develop a model of an electric vehicle (EV) energy network to transmit, distribute, and store energy by EVs from renewable energy sources to places that need the energy. We describe the EV energy internet in detail and then formulate an optimization problem to place charging stations in an EV energy internet. We develop two solutions: one using a greedy heuristic and another based on diffusion. Simulation results using real-world data show that the greedy heuristic requires less charging stations, whereas the diffusion-based algorithm incurs less energy transmission loss. [ABSTRACT FROM PUBLISHER]

Hybrid electric vehicles (HEVs) are capable of improving fuel economy with reduced emissions over traditional vehicles powered only by the internal combustion (IC) engine. HEV durability is often significantly limited by the useful life of the battery, and battery life can be significantly reduced if the battery is often operated outside its allowed discharging/charging limits. This could occur particularly at extremely low temperature, leading to permanent damage and reduced useful life of the battery. To reduce battery overdischarging duration for extended battery life, this paper proposes a stochastic predictive boundary management (SPBM) strategy to proactively make the engine power available to the powertrain based on the predicted torque requirement and its prediction error variance. The effectiveness of the proposed SPBM is validated in simulations, and its performance is compared with the baseline power follower control strategy (PFCS) and predictive boundary management (PBM) under five typical driving cycles with selected initial battery temperature. The simulation results show that the proposed control strategy dramatically reduces the battery overdischarging energy under all five typical driving cycles over both PFCS and PBM, where the average reduction is 82% under aggressive US06 and ARB02 driving cycles, 67% under NYCC and FTP driving cycles, and 37% under the IM240 highway driving cycle. Note that the adaptive prediction and its error variance can be calculated in real time with fairly low computational load. [ABSTRACT FROM PUBLISHER]

This paper studies the fuel-optimal cruising strategies of parallel hybrid electric vehicles (HEVs) and their underlying mechanisms. To achieve fuel-optimal operations, a discontinuous nonlinear optimal control problem is formulated and solved using the Legendre pseudospectral method and the knotting technique. Three optimal cruising strategies in free/fixed-speed cruising scenarios are proposed: vehicle speed pulse-and-glide strategy, state-of-charge (SoC) pulse-and-glide (PnG) strategy, and constant-speed strategy. The performance and optimal behavior of the engine and the motor are presented, and their fuel-saving mechanisms are explained. Finally, two principles to compromise between fuel economy and ride comfort are proposed and studied. [ABSTRACT FROM PUBLISHER]

This paper presents the development of an electric-vehicle (EV) interior permanent-magnet synchronous motor (IPMSM) drive and its operation control. The power circuit of the established EV IPMSM drive consists of a two-leg interleaved bidirectional front-end dc/dc buck–boost converter and a three-phase inverter. The front-end converter can establish adjustable and boostable dc-link voltage for the motor drive to enhance its high-speed driving performance. It can also allow the battery bank to be charged from the motor during regenerative braking. Good current sharing control between converter modules and dc-link boosted voltage regulation response are preserved via the developed robust control scheme. Moreover, fast disturbance rejection response against the module fault and recovery is achieved. The comparative driving performance enhancements via dc-link voltage boosting and commutation instant tuning are conducted. Then, through proper setting and control, the established EV IPMSM drive possesses good driving performances, including starting, acceleration/deceleration, regenerative braking, and reversible operation characteristics. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel double-grid-frequency-ripple-reducing technique for electric vehicle (EV) battery charger applications. In 1- $\phi$ EV battery chargers, due to double-frequency ripple, it is not possible to control the current using a single-power-stage approach. Hence, a two-stage approach is used. However, a bulky inductor and a capacitor are required to control the current, which adds to the system's weight and cost. The single-stage charging system described in this paper can charge the battery with almost ripple-free current without using a bulky electrolytic capacitor. At the same time, it can maintain high power factor (PF) and low total harmonic distortion on the input ac side. The proposed ripple compensation solution is realized by an innovative combination of boost and zeta converters connected in series with the battery charger, which reduces the filter capacitor value considerably. This facilitates the use of a film capacitor, which has a much longer life compared with an electrolytic capacitor. Similarly, the value of the filter inductor used in series with the battery is reduced significantly. Battery ripple current is not increased in the process; therefore, the battery life remains unaffected. Representative analytical, simulation, and experimental results are presented. [ABSTRACT FROM AUTHOR]