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This paper demonstrates a new process that has been specifically designed for the support of the U.S. Department of Transportation's (DOT's) Corporate Average Fuel Economy (CAFE) standards. In developing the standards, DOT's National Highway Traffic Safety Administration made use of the CAFE Compliance and Effects Modeling System (the 'Volpe model' or the 'CAFE model'), which was developed by DOT's Volpe National Transportation Systems Center for the 2005-2007 CAFE rulemaking and has been continuously updated since. The model is the primary tool used by the agency to evaluate potential CAFE stringency levels by applying technologies incrementally to each manufacturer's fleet until the requirements under consideration are met. The Volpe model relies on numerous technology-related and economic inputs, such as market forecasts, technology costs, and effectiveness estimates; these inputs are categorized by vehicle classification, technology synergies, phase-in rates, cost learning curve adjustments, and technology 'decision trees'. Part of the model's function is to estimate CAFE improvements that a given manufacturer could achieve by applying additional technology to specific vehicles in its product line. A significant number of inputs to the Volpe decision-tree model are related to the effectiveness (fuel consumption reduction) of each fuel-saving technology. Argonne National Laboratory has developed a fullvehicle simulation tool named Autonomie, which has become one of the industry's standard tools for analyzing vehicle energy consumption and technology effectiveness. Full-vehicle simulation tools use physics-based mathematical equations, engineering characteristics (e.g., engine maps, transmission shift points, and hybrid vehicle control strategies), and explicit drive cycles to predict the effectiveness of individual and combined fuel-saving technologies. The Large-Scale Simulation Process accelerates and facilitates the assessment of individual technological impacts on vehicle fuel economy. This paper will show how Argonne efficiently simulates hundreds of thousands of vehicles to model anticipated future vehicle technologies. [ABSTRACT FROM AUTHOR]

An advanced control scheme for managing a hybrid energy generation system (HEGS) is presented in this paper. A hierarchical management and control architecture based on multi-agent systems (MAS) is discussed. MAS will account for the complex behavior of a hybrid energy supply system. The management and control strategies are implemented through a system of agents based on three tiers. The upper level agents develop overall energy management strategies for a hybrid energy supply system. The middle-level agents integrate coordinated switching controllers. The lower level agents are responsible for dealing with local control strategies. Coordinated switching controllers within the middle-level agents are designed as event-triggered hybrid controllers based on differential hybrid Petri-net (DHPN) models. The operation modes of distributed energy resources (DERs) can smoothly transfer in a coordinated manner due to the coordinated action of the switching controllers according to variation in operating conditions. Finally, simulation results from different scenarios verifying the feasibility of the proposed scheme are offered. [ABSTRACT FROM PUBLISHER]

In this paper, we consider a method to create an engine emission simulation model for cycle and customer driving of a vehicle. The emission model results from an empiric approach, also taking into account the effects of engine dynamics on emissions. We analysed transient engine emissions in driving cycles and during representative customer driving profiles and created emission meta models. The analysis showed a significantly higher correlation in emissions when simulating realistic customer driving profiles using the created verified meta models (< 1 % model error) compared to static approaches, which are commonly used for vehicle simulation. Therefore, a transient modelling approach is conducted, which shows a great increase in accuracy in customer driving operation. [ABSTRACT FROM AUTHOR]

A modified thermostatic control strategy is applied to the powertrain control of a parallel mild hybrid electric vehicle (HEV) to improve fuel economy. This strategy can improve the fuel economy of a parallel mild HEV by operating internal combustion engine (ICE) in a high-efficiency region. Thus, in this study, experiments of a parallel mild HEV were conducted to analyze the characteristics of the hybrid electric powertrain and a numerical model is developed for the vehicle. Based on the results, the thermostatic control strategy was modified and applied to the vehicle model. Also, battery protection logic by using electrochemical battery model is applied because the active usage of battery by thermostatic control strategy can damage the battery. The simulation results of the vehicle under urban driving conditions show that the thermostatic control strategy can improve the vehicle's fuel economy by 3.7 % compared with that of the conventional strategy. The results also suggest that the trade-off between the fuel economy improvement by efficient ICE operation and the battery life reduction by active battery usage should be carefully investigated when a thermostatic control strategy is applied to a parallel mild HEV. [ABSTRACT FROM AUTHOR]

Many of today's power-split hybrid electric vehicles (HEVs) utilize planetary gears (PGs) to connect the powertrain elements together. Recent power-split HEVs tend to use two PGs and some of them have multiple modes to achieve better fuel economy and driving performance. Looking to the future, hybrid powertrain technologies must be enhanced to design hybrid light trucks. For light trucks, the need for multi-mode and more PGs is stronger, to achieve the required performance. To systematically explore all the possible designs of multi-mode HEVs with three PGs, an efficient searching and optimization methodology is proposed. All possible clutch topology and modes for one existing configuration that uses three PGs were exhaustively searched. The launching performance is first used to screen out designs that fail to satisfy the required launching performance. A near-optimal and computationally efficient energy management strategy was then employed to identify designs that achieve good fuel economy. The proposed design process successfully identify 8 designs that achieve better launching performance and better fuel economy, while using fewer number of clutches than the benchmark and a patented design. [ABSTRACT FROM AUTHOR]

In this paper, concurrent design of energy management (EM) and traction control algorithms for a vehicle equipped with a parallel hybrid powertrain is studied. This paper focuses on designing the two control algorithms together as one control design problem, which are traditionally considered separately. First, optimal control actions and operating points are obtained by applying dynamic programming (DP). Then, this information is used for developing a rule-based supervisory controller. Our objective is to minimize the fuel consumption and the wheel slip simultaneously. Two control problems are also solved separately and compared with the concurrent solution. Results show that promising benefits can be obtained by using the concurrent design approach rather than considering two control problems separately. Under the same conditions, the vehicle with the concurrent supervisory controller is 16% more efficient in fuel consumption and experiences 12% less wheel slip, assuming slippery road friction conditions. [ABSTRACT FROM PUBLISHER]

Appropriate structure is the first key issue that a designer of an efficient hybrid vehicle should pay attention to. This paper presents three different structures for the concept of series–parallel structure in hybrid electric vehicles (HEVs) and compares them with each other for the application of Toyota Prius 2004 as a well-known case study. The first structure consisting of one electric motor, one electric generator, and one power split device is the conventional structure as one existing in Toyota Prius 2004 and called THS-II in the literature. The second and third structures are based on two special electric machines named transmotor-based structure (TBS) and dual-mechanical-port (DMP) machine-based structure (DMPBS), respectively. The three structures are compared by considering different aspects such as weight, occupied volume, and overall efficiency. A surface-mounted fractional-slot concentrated winding DMP machine is designed, and the design is verified by applying the finite-element analysis. Simulation results have been presented to support the accomplished comparison of the structures under study. Simulation results show that all structures have approximately equal weight, volume, and efficiency, but DMPBS is a compact, efficient, and simple way to convert the existing fossil-fuel cars into an HEV. One DMP machine prototype has been implemented, and experimental results have been included. [ABSTRACT FROM PUBLISHER]

Over the last few years; issues regarding the use of hybrid energy storage systems (HESSs) in hybrid electric vehicles have been highlighted by the industry and in academic fields. This paper proposes a fuzzy-logic power management strategy based on Markov random prediction for an active parallel battery-UC HESS. The proposed power management strategy; the inputs for which are the vehicle speed; the current electric power demand and the predicted electric power demand; is used to distribute the electrical power between the battery bank and the UC bank. In this way; the battery bank power is limited to a certain range; and the peak and average charge/discharge power of the battery bank and overall loss incurred by the whole HESS are also reduced. Simulations and scaled-down experimental platforms are constructed to verify the proposed power management strategy. The simulations and experimental results demonstrate the advantages; feasibility and effectiveness of the fuzzy-logic power management strategy based on Markov random prediction. [ABSTRACT FROM AUTHOR]

In this paper, a rear-wheel-driven series hybrid electric vehicle which has a mechanically operated friction brake system is studied. A new cooperative control of regenerative braking and friction braking called ‘combined braking’ is proposed for this vehicle configuration. A mechanism to adjust the proportions of regenerative braking and friction braking was proposed in this paper. Further, the braking force distribution between the front wheels and the rear wheels was analysed to ensure stable braking. The brake system characteristics were considered to ensure that the driver’s feel remains the same in the new proposed combined braking strategy. The simulation results under urban driving and across the Modified Indian Driving Cycle and vehicle road testing results show that the proposed combined braking can regenerate more than twice the braking energy of conventional parallel braking. Also, with combined braking, the braking force distribution between the front wheels and the rear wheels is closer to the ideal braking force distribution curve, which is desirable to ensure stable braking. [ABSTRACT FROM AUTHOR]

Since introduction of the Prius to the Japanese automotive market in 1997, Toyota has sold more than 1 million of these hybrid electric vehicles in the USA and 3 million in the worldwide market up to 2013. The market penetration of the vehicle has been successful because it provides outstanding fuel economy. However, the reduction in the vehicle’s efficiency under severe weather conditions, especially when the weather is very cold, has not been carefully investigated. The engine’s fuel efficiency decreases when the engine is cooled during use in the electric drive mode, or the battery’s efficiency is also degraded under cold weather conditions. Whereas the impacts of the thermal conditions on the fuel economies of vehicles are being increasingly emphasized, an analysis of these impacts has not been conducted widely because this type of analysis needs to be based on test results obtained from a well-designed environmental chamber that can simulate different thermal conditions. The Advanced Powertrain Research Facility at Argonne National Laboratory has equipped its environmental thermal chamber appropriately and has tested the 2010 Toyota Prius at different ambient room temperatures (−7 ºC, 22 ºC, and 35 ºC). On the basis of the test results, the findings include the fact that the control behavior changes significantly according to the thermal conditions. For instance, the controller forces the engine to be turned on if the engine coolant temperature is below 53 ºC. In addition to the thermal impacts on the controller, performance degradation of the powertrain components and the fuel efficiency levels of the vehicle are also analyzed using the test results. Although this study does not advance a novel idea, the information provided herein should be very useful to researchers seeking to understand controller behavior in the real world under different thermal conditions. [ABSTRACT FROM AUTHOR]

With the widespread penetration of plug-in hybrid electric vehicles (PHEVs), the overall demand on microgrids (MGs) may increase manifold in the near future. Unregulated power demands from PHEVs may increase the demand–supply gap at MGs. Thus, to keep MGs stabilize and cater the ever-growing energy demands, there is a requirement of an intelligent solution to regulate and manage PHEVs in vehicle-to-grid (V2G) environment. Keeping in view the given issues, this paper proposes a novel scheme that aims to regulate PHEVs' charging and discharging activities based on MGs' day-ahead load curves. These load curves are obtained by utilizing the existing load forecasting techniques such as fuzzy logic (FL) and artificial neural networks (ANNs). Efficient utilization of PHEVs according to these curves may play a vital role in flattening MG's load profile. Thus, the proposed scheme works by reserving resources such as time slots and charging points (CPs) for PHEVs during peak shaving and valley filling. Different algorithms pertaining to resource reservation for PHEVs have also been designed. These algorithms employ the concepts of game theory and the 0/1 knapsack problem for supporting peak shaving and valley filling, respectively. Moreover, PHEVs are also utilized when there are transitions from valley filling to peak shaving areas in the load curves and vice versa. PHEVs involved in this process have both charging and discharging capabilities and are referred to as dual-mode PHEVs. The proposed scheme has been tested with respect to various parameters, and its performance was found satisfactory. [ABSTRACT FROM PUBLISHER]

The energy efficiency of parallel hybrid electric vehicles (HEVs) can degrade significantly when the battery state-of-charge (SoC) reaches its boundaries. The road grade has a great influence on the HEV battery charging and discharging processes, and therefore the HEV energy management can be benefited from the road grade preview. In real-world driving, the road grade ahead can be considered as a random variable because the future route is not always available to the HEV controller. This brief proposes a stochastic model predictive control-based energy management strategy using the vehicle location, traveling direction, and terrain information of the area for HEVs running in hilly regions with light traffic. The strategy does not require a determined route being known in advance. The road grade is modeled as a Markov chain and stochastic HEV fuel consumption and battery SoC models are developed. The HEV energy management problem is formulated as a finite-horizon Markov decision process and solved using stochastic dynamic programming. The proposed method is evaluated in simulation and compared with an equivalent consumption minimization strategy and the dynamic programming results. It is shown that the developed method can help maintaining the battery SoC within its boundaries and achieve good energy consumption performance. [ABSTRACT FROM AUTHOR]

This paper presents a power management strategy to minimize the fuel consumption of a heavy-duty series hybrid electric vehicle. The vehicle model, which consists of a CNG engine, a generator, two driving motors and a battery, is developed and validated with experimental data. Dynamic programming (DP) is applied to design a control algorithm optimizing fuel consumption of the series hybrid electric vehicle. By imitating the behavior of the DP control signal, a rule-based control algorithm is proposed. The controller actuates the CNG engine, generator, and battery to minimize fuel consumption and maintain the battery state of charge within a proper region. Simulation studies and experiments are conducted for typical driving modes to investigate the performance of the proposed power management strategy. Results indicate that fuel economy can be enhanced by approximately 30% using the proposed power management strategy to the series hybrid electric vehicle over the conventional vehicle. [ABSTRACT FROM AUTHOR]

Hybrid electric vehicles offer the potential for fuel consumption improvements when compared with conventional vehicle powertrains. The fuel consumption benefits which can be realised when utilising the hybrid electric vehicle architecture are dependent on how much braking energy is regenerated, and how well the regenerated energy is utilised. A number of power management strategies have been proposed in literature. Owing to the prospect of real-time implementation, many of these proposals have centred on the use of heuristics. Despite the research advances made, the key challenge with heuristic strategies remains achieving reasonable fuel savings without over-depleting the battery’s state of charge at the end of the trip. In view of this challenge, this paper offers two main contributions to existing energy management literature. The first is a novel, simple but effective heuristic control strategy which employs a tuneable parameter (the percentage of the maximum motor tractive power) to decide the control sequence, such that impressive fuel savings are achieved without over-depleting the final state of charge of the battery (the battery energy). The second is the quantitative exploration of braking patterns and its impact on kinetic energy regeneration. The potential of the proposed heuristic control strategy was explored over a range of driving cycles which reflect different driving scenarios. The results from this analysis show that fuel savings of as much as 19.07% can be achieved over the Japan 10–15 driving cycle. In comparison with a suboptimal controller whose control signals were derived from dynamic programming optimal control, our proposed strategy was found to be outperforming, in that it achieved impressive real-time fuel savings without much penalty to the final state of charge of the battery. Gentle braking patterns were also found to significantly improve brake energy regeneration by the electric motor. [ABSTRACT FROM AUTHOR]

Driving cycles greatly influence the fuel economy and exhaust of the vehicle, especially in hybrid electric vehicles. The purpose of this study is to develop a method to identify the type of driving cycle with better accuracy and less sampling time than other driving cycle recognition algorithms. A driving cycle recognition algorithm based on Learning Vector Quantization neural network is first created to analyze four selected representative standard driving cycles. Micro-trip extraction and box-and-whisker plots are then applied to ensure the diversity and magnitude of training samples. Finally, a sample training simulation is conducted to determine the minimum neuron number of learning vector quantization network, using the simulation platform of Matlab/Simulink. Afterwards, we simplify the structure of the recognition model to reduce data convergence time. Simulation results show the feasibility and efficiency of the proposed algorithm, which decreases the time window length from 120 s to 60 s with acceptable accuracy. Furthermore, the driving cycle recognition algorithm is used in a series-parallel hybrid vehicle model to improve the fuel economy by about 6.29%. [ABSTRACT FROM AUTHOR]

The slope angle is one of the most influential factors which limit the control performance and the energy efficiency of not only internal-combustion engine vehicles but also electrified vehicles such as hybrid vehicles and plug-in hybrid electric vehicles. However, measuring the slope angle is not easy because slope sensors are not sufficiently accurate and they are too expensive to implement in vehicles. In this study, the climbing angle as well as the slope angle are considered in the presence of yawing motion on a sloping road, and both angles are estimated. In order to describe the combined vehicle motions when turning on a sloping road, a new three-dimensional dynamics model is proposed including the heading angle with respect to the road slope orientation. The extended Kalman filter is designed on the basis of the three-dimensional dynamics model on a sloping road, and the key parameters for the sloping road are updated using the geometry relationship. The proposed estimation method is evaluated using simulations and field tests with a hybrid electric vehicle. [ABSTRACT FROM AUTHOR]

This paper presents a supervisory control strategy for plug-in hybrid electric vehicles based on energy demand prediction and route preview. The aim is to minimize the fuel consumption in real-time operation. This strategy is realized through three successive steps. First, a neural network model is established to predict the energy demand of the vehicle. It reduces the complete traffic data to several statistical parameters, which contributes to ease the prediction process. Second, a mathematical model is proposed to translate the predicted energy demand into a state of charge (SOC) reference of the battery, which significantly simplifies the SOC-programming method. Finally, the adaptive equivalent consumption minimization strategy (ECMS) is used to track the SOC reference and determine the powertrain state. The proposed strategy can optimally distribute the energy between the engine and the motor on a global range and achieve an optimal torque split on a local range. Simulations are carried out on a power-split plug-in hybrid electric bus, and the proposed strategy shows substantial improvements in fuel economy and other indexes compared with the rule-based strategy and the ECMS. [ABSTRACT FROM PUBLISHER]

This paper presents the current research trends and future issues for industrial electronics related to transportation electrification. Specific emphasis is placed on electric and plug-in hybrid electric vehicles (EVs/PHEVs) and their critical drivetrain components. The paper deals with industry related EV energy storage system issues, EV charging issues, as well as power electronics and traction motor drives issues. The importance of battery cell voltage equalization for series-connected lithium-ion (Li-ion) batteries for extended life time is presented. Furthermore, a comprehensive overview of EV/PHEV battery charger classification, standards, and requirements is presented. Several conventional EV/PHEV front-end ac/dc charger converter topologies as well as isolated DC/DC topologies are reviewed. Finally, this paper reviews various EV propulsion system architectures and efficient bidirectional DC/DC converter topologies. Novel DC/AC inverter modulation techniques for EVs are also presented. The architectures are based on the battery voltage, capacity, and driving range. [ABSTRACT FROM PUBLISHER]

A transformerless hybrid series active filter is proposed to enhance the power quality in single-phase systems with critical loads. This paper assists the energy management and power quality issues related to electric transportation and focuses on improving electric vehicle load connection to the grid. The control strategy is designed to prevent current harmonic distortions of nonlinear loads to flow into the utility and corrects the power factor of this later. While protecting sensitive loads from voltage disturbances, sags, and swells initiated by the power system, ridded of the series transformer, the configuration is advantageous for an industrial implementation. This polyvalent hybrid topology allowing the harmonic isolation and compensation of voltage distortions could absorb or inject the auxiliary power to the grid. Aside from practical analysis, this paper also investigates on the influence of gains and delays in the real-time controller stability. The simulations and experimental results presented in this paper were carried out on a 2-kVA laboratory prototype demonstrating the effectiveness of the proposed topology. [ABSTRACT FROM PUBLISHER]

Dual-rotor permanent-magnet synchronous motors (PMSMs) offer the merits of higher efficiency, higher torque density, and inherent electric differential (ED). This paper proposes a novel four-axis vector-controlled dual-rotor PMSM drive for driving plug-in electric vehicles (PEVs). A four-axis $d$– $q$ model of the dual-rotor PMSM is developed with the proposed three-phase to four-phase transformation. This transformation separately resolves the stator variables to the $d$– $q$-axes of the inner and outer rotor. Instead of applying back electromotive forces and rotor power angles, the $d$-axis fluxes are used for computing the displacement between the two $d$– $q$-axes, which is essential for the transformation. The proposed four-axis vector control of dual-rotor PMSM allows for the independent control of stator quantities corresponding to the inner and outer rotors. This facilitates ED and better control under parametric differences, such as rotor inertia and permanent magnet flux for the inner and outer rotors. The comparison of the dual-rotor PMSM-based PEV operation with the proposed control and two earlier reported schemes reveals that the proposed scheme offers effective ED without the loss of vehicle control. Furthermore, the proposed configuration is validated with different drive cycles, including the loss modeling, sensitivity analysis, and efficiency mapping. [ABSTRACT FROM PUBLISHER]

We present a method for automated engine calibration, by optimizing engine management settings and power-split control of a hybrid electric vehicle (HEV). The problem, which concerns minimization of fuel consumption under a NOx constraint, is formulated as an optimal control problem. By applying Pontryagin’s maximum principle, this paper shows that the problem is separable in space. In the case where the limits of battery state of charge are not activated, we show that the optimization problem is also separable in time. The optimal solution is obtained by iteratively solving the power-split control problem using dynamic programming or the equivalent consumption minimization strategy. In addition, we present a computationally efficient suboptimal solution, which aims at reducing the number of power-split optimizations required. An example is provided concerning optimization of engine management settings and power-split control of a parallel HEV. [ABSTRACT FROM PUBLISHER]

The paper presents selected simulation and experimental results of a hybrid ECPMS-machine (Electric Controlled Permanent Magnet Synchronous Machine). This permanent magnets (PMs) excited machine offers an extended magnetic field control capability which makes it suitable for battery electric vehicle (BEV) drives. Rotor, stator and the additional direct current control coil of the machine are analyzed in detail. The control system and strategy, the diagram of power supply system and an equivalent circuit model of the ECPMS-machine are presented. Influence of the additional excitation on the performance parameters of the machine, such as: torque, efficiency, speed limits and back-EMF have also been discussed. [ABSTRACT FROM AUTHOR]

This paper presents a new stochastic multi-layer agent-based model to study the behavior of electricity market participants. The wholesale market players including renewable power producers are modeled in the first layer of the proposed multi-agent environment. The players optimize bidding/offering strategies to participate in the electricity markets. In the second layer, responsive customers including plug-in electric vehicle (PEV) owners and consumers who participate in demand response (DR) programs are modeled as independent agents. The objective of the responsive customers is to increase their benefit while retaining welfare. The interaction between market players in day-ahead and real-time markets is modeled using an incomplete information game theory algorithm. Due to the uncertainties of resources and customers' behavior, the model is developed using a stochastic framework. A case study containing wind power producers (WPPs), PEV aggregators and retailers providing DR is considered to demonstrate the usefulness and proficiency of the proposed multi-layer agent-based model. [ABSTRACT FROM PUBLISHER]

Due to the complexity in the control system of hybrid vehicles, the electronic control units should go through extensive testing before getting installed in a prototype vehicle. In the first stages of the development process, Hardware-in-the-Loop (HiL) simulation can not be performed because the hardware components of the vehicle are not yet available. In this case, Model-in-the-loop (MiL) simulation is used which couples the designed control software with an environment simulation with no need for a special hardware. In this paper, the MiL simulation is introduced for verification of the vehicle control software in a series hybrid electric bus. To do so, considering the dynamic behavior of various components of the vehicle, a simulator of the hybrid bus is designed by going through with the input/outputs of the vehicle control software. Using the designed test bench, the user can act as a real driver and experience different driving regimes and analyze the control software commands. In the designed simulator, all subsystems are simulated separately in LabVIEW environment and real-time simulation is achieved with an acceptable error. Therefore, it can be used in a HiL test bench for testing each of the vehicle components. The designed simulation model has been validated using real test results. Using that, results show that all control functions in the vehicle control software can be tested and verified with no cost and in the shortest possible time. [ABSTRACT FROM AUTHOR]

This paper considers a diesel series hybrid electric vehicle (SHEV) and proposes the utilization of an engine–generator power rate constraint map to reduce soot emissions without a significant compromise in fuel economy. Specifically, model predictive control (MPC) is used to split the vehicle power demand between the engine–generator unit and the battery. To achieve a reduction in soot, the engine–generator power rate is constrained. Unlike existing strategies, the power rate limit is not a fixed value but varies, depending on the power level, resulting in a map. This constraint map is designed by formulating the soot emission reduction problem as an optimization problem, which is solved through a three-step offline discrete optimization process. The optimization relies on a quasi-static soot emissions map that captures the trends, even during transients, but underestimates the magnitudes. Therefore, to evaluate the performance of the MPC-based power management with the power rate constraint map, experiments are conducted through an engine-in-the-loop simulation framework. Experimental results show that compared with a constant power rate constraint, soot emissions can be reduced by 44.5% while compromising fuel economy by only 0.3% through the proposed approach. As a tradeoff, the ampere-hour (Ah) processed in the battery, which is a variable that has been shown to correlate with battery capacity loss, increases by 5.5%. [ABSTRACT FROM PUBLISHER]

A variant of the hybrid electric vehicle (HEV) is a split-axle, through-the-road parallel hybrid, which enables existing vehicles to be retrofitted into an HEV with minimal modification - achieved by placing electric motors in the hub of the otherwise non-driven wheels. The term 'through-the-road' (TTR) refers to the power coupling scheme between the internal combustion engine (ICE) and in-wheel motors (IWM), which is not through some mechanical device but through the vehicle itself, its wheels and the road on which it moves. The objective of the present work is to identify specific operating modes, power flow, torque-speed relationship, profiles and system architecture of the TTR-IWM hybrid - in particular a retrofit-conversion hybrid - in comparison to the ordinary parallel HEV. This paper also discusses three possible control architectures and the corresponding control parameters. The TTR-IWM hybrid's reduced operating modes and inability to charge its battery bank during vehicle stand-still present challenges which require a specific control strategy. Simulation results of acceleration show that the TTR-IWM hybrid has better fuel economy and acceleration performance, compared to the pre-transmission, parallel hybrid vehicle with on-board motor. [ABSTRACT FROM AUTHOR]

China's auto industry is faced with the historical opportunities for technological transformation and strategic industry transformation, while the development of electric vehicle industry is an important factor to energy development. Based on strategic planning, industry goal promotion, institution transformation, model replacement, core technology development, infrastructure construction, policy mechanism, business model optimization, and other factors, this paper compares the advantages and disadvantages of technologies in hybrid vehicles, plug-in hybrid vehicles, fuel-cell vehicles and pure electric vehicles, considers their procurement costs, and divides the development of electric vehicle industry into demonstration stage, rapid development stage, popularity and mature stage from the perspective of ecological energy. According to characteristics of different stages, develop corresponding technologies, economic policies, and financial policies, legal and administrative policies. Through researches on the development model of electric vehicle industry, promote the development of new energy vehicle industry, and improve the entry conditions, entry standards, entry procedures and certification program for electric vehicle products. [ABSTRACT FROM AUTHOR]

Electrical drive systems are key components in modern appliances, industry equipment, and systems, e.g., hybrid electric vehicles. To obtain the best performance of these drive systems, the motors and their control systems should be designed and optimized at the system level rather than the component level. This paper presents an effort to develop system-level design and optimization methods for electrical drive systems. Two system-level design optimization methods are presented in this paper: 1) single-level method (only at system level); and 2) multilevel method. Meanwhile, the approximate models, the design of experiments technique, and the sequential subspace optimization method are presented to improve the optimization efficiency. Finally, a drive system consisting of a permanent-magnet transverse flux machine with a soft magnetic composite core is investigated, and detailed results are presented and discussed. This is a high-dimensional optimization problem with 14 parameters mixed with both discrete and continuous variables. The finite-element analysis model and method are verified by the experimental results on the motor prototype. From the discussion, it can be found that the proposed multilevel method can increase the performance of the whole drive system, such as bigger output power and lower material cost, and decrease the computation cost significantly compared with those of single-level design optimization method. [ABSTRACT FROM PUBLISHER]

Control algorithms for hybrid vehicles have undergone extensive research and development leading to near-optimal techniques being employed and demonstrated in prototype vehicles over the previous decade. The use of different implementations of optimal controllers is inevitably linked through the assumed knowledge of the system being controlled. With the growing interest in alternative fuels, such as ethanol, liquified petroleum gas (LPG), and compressed natural gas (CNG) due to enhanced emissions and fuel security considerations, a natural extension is to hybridize these engines to improve fuel economy and CO2 emissions. This step is complicated by the potential variation in fuel composition seen with many gasoline and diesel alternatives, leading to uncertainty in the models used by the hybrid powertrain controller. This work investigates the robustness of one hybrid powertrain optimal control approach, the equivalent consumption minimization strategy (ECMS). Two case studies are performed involving experimentally obtained engine maps from two significantly different prototypes flex-fuel vehicles to quantify the potential impact of map error caused by incorrect fuel assumptions. [ABSTRACT FROM AUTHOR]

Hybrid vehicle fuel economy and drive quality are coupled through the "energy management" controller that regulates power flow among the various energy sources and sinks. This paper studies energy management controllers designed using shortest path stochastic dynamic programming (SP-SDP), a stochastic optimal control design method which can respect constraints on drivetrain activity while minimizing fuel consumption for an assumed distribution of driver power demand. The performance of SP-SDP controllers is evaluated through simulation on large numbers of real-world drive cycles and compared to a baseline industrial controller provided by a major auto manufacturer. On real-world driving data, the SP-SDP-based controllers yield 10% better fuel economy than the baseline industrial controller, for the same engine and gear activity. The SPSDP controllers are further evaluated for robustness to the drive cycle statistics used in their design. Simplified drivability metrics introduced in previous work are validated on large real-world data sets. [ABSTRACT FROM AUTHOR]

The recently developed power distribution strategies in hybrid electric vehicles (HEVs) are often hard to apply, or the mathematical backgrounds are burdened by very complicated calculations. In this paper, a new control strategy, referred to as a hybrid thermostat strategy, is proposed for a series-type HEV (SHEV). The proposed strategy has its foundation in the thermostat strategy and incorporates merits from the power follower strategy such that it overcomes those strategy weaknesses and accomplishes increased efficiency with a practical concept. The development of the proposed strategy is based on AMESim and Simulink cosimulation with Simplex method optimization. [ABSTRACT FROM PUBLISHER]

The reliability prediction of hybrid electric vehicles (HEVs) is of paramount importance for planning, design, control, and operation management of vehicles, since it can provide an objective criterion for comparative evaluation of various configurations and topologies and can be used as an effective tool to improve the design and control of the overall system. This paper presents a mission-profile-dependent simulation model based on MATLAB for quantitatively assessing the reliability of the electric drivetrain of HEVs. This model takes into consideration the variable driving scenarios, dormant mode, electrical stresses, and thermal stresses. Therefore, more reliable and accurate prediction of system reliability has been achieved. The methodology is explained in detail, and the results of reliability assessment based on a series HEV are presented. Based on reliability analysis, two control strategies are proposed to increase the mean time to failure of HEV powertrains: 1) variable dc-link voltage control and 2) hybrid discontinuous pulsewidth modulation scheme. These novel control schemes reduce the power losses and thermal stresses of power converters, and consequently, enhance system reliability. Numerical simulation results verify the benefits of two proposed control strategies in terms of power losses and reliability. [ABSTRACT FROM PUBLISHER]

Convex optimization has recently been suggested for solving the optimal energy management problem of hybrid electric vehicles. Compared with dynamic programming, this approach can significantly reduce the computational time, but the price to pay is additional model approximations and heuristics for discrete decision variables such as engine on/off control. In this paper, the globally optimal engine on/off conditions are derived analytically. It is demonstrated that the optimal engine on/off strategy is to switch the engine on if and only if the requested power exceeds a certain nonconstant threshold. By iteratively computing the threshold and the power split using convex optimization, the optimal solution to the energy management problem is found. The effectiveness of the presented approach is demonstrated in two sizing case studies. The first case study deals with high-energy-capacity batteries, whereas the second case study deals with supercapacitors that have much lower energy capacity. In both cases, the proposed algorithm yields optimal results much faster than the dynamic programming algorithm. [ABSTRACT FROM PUBLISHER]

The double-rotor machine is a kind of multiple input and output electromechanical energy transducer with two electrical ports and two mechanical ports, which is an ideal transmission system for hybrid electric vehicles and has a series of advantages such as integration of power and energy, high efficiency and compaction. In this paper, a switched reluctance double-rotor machine (SRDRM) is proposed for hybrid electric vehicles, while no conductor or PM in the middle rotor. This machine not only inherits the merits of switched reluctance machine, such as simple salient rotor structure, high reliability and wide speed range, but also can avoid the outer rotor's cooling problem effectively. By using an equivalent magnetic circuit model, the function of middle rotor yoke is analyzed. Electromagnetic analyses of the SRDRM are performed with analytical calculations and 2-D finite element methods, including the effects of main parameters on performance. Finally, a 4.4 kW prototype machine is designed and manufactured, and the tests are performed, which validate the proposed design method. [ABSTRACT FROM AUTHOR]

In the rapidly growing field of electrified transportation technology, hybrid electric and electric vehicle (HEV/EV) manufacturers have been placing increasing demands on the designers to improve the efficiency and reduce the costs of vehicle components to sustain a niche in the EV market. To understand the significance and challenges in an efficient and economical drivetrain design, this paper presents a part of an ongoing project that aims at developing a comprehensive study on dc/ac drive motor technology in EV applications. In this paper, a new three-level three-phase matrix converter topology, along with two conventional frequently used three-level three-phase dc/ac converter topologies, namely, diode-clamped and H-bridge converters, are implemented. Furthermore, comprehensive analysis and comparative studies are conducted to examine the performance and efficiency of these converter topologies according to EV design criteria. In addition, experimental investigations are carried out on the aforementioned converter configurations to help EV designers choose the most suitable converter topology. [ABSTRACT FROM PUBLISHER]

Silicon carbide (SiC)-based switching devices provide significant performance improvements in many aspects, including lower power dissipation, higher operating temperatures, and faster switching, compared with conventional Si devices. However, tradeoffs in efficiency, size, and weight between Si- and SiC-based converters are still unclear in the literature. In this paper, a bidirectional dc–dc converter that is suitable for hybrid or electric vehicle application is studied based on three sets of device combinations, e.g., all-silicon [conventional silicon insulated-gate bipolar transistors (IGBTs) and silicon PN diodes], hybrid (silicon IGBTs with SiC Schottky diodes), and all-SiC (SiC metal–oxide–semiconductor field-effect transistors with SiC Schottky diodes). At the switching frequency of 20 kHz, comparative analyses regarding the power loss reduction of power devices and efficiency improvements are carried out for the converters. Possible size and weight reduction is also investigated by increasing the operating frequencies of hybrid and all-SiC converters while reducing the capacitance and inductance values. [ABSTRACT FROM PUBLISHER]

Because of the drawbacks of ultracapacitors (UCs) and batteries, an active combination of UCs and Li-ion batteries has been proposed as an energy storage system (ESS) for hybrid electric vehicles (HEVs). Given the complexity of the active control system in an active-combination energy storage system (ACES), the performance match for the ACES used in an HEV is much more complex. In this paper, a widely applicable method to analyze the design process of the ESS used in HEVs is presented. The concept of the power–energy (PE) function is proposed to illustrate the power demand from the HEV and the energy and power capability of the ESS. This concept draws a clear contrast between demand and capability, particularly for the ACES. At the same time, the efficiency of the ACES could be estimated on the basis of this method. Furthermore, by using operating data from a hybrid electric bus in Changchun, China, power demand analysis and performance estimation are carried out for the optimal design of the ACES. [ABSTRACT FROM PUBLISHER]

This paper presents a six-phase current reconstruction scheme for dual traction inverters in hybrid electric vehicles (HEVs) with a single dc-link current sensor. During the phase current reconstruction, one of the inverters is proposed to operate using active vectors, whereas the other inverter operates using nonactive vectors. An advanced phase-shift scheme for all pulsewidth-modulated (PWM) signals based on the sequence of duty cycles is implemented. PWM signals are considered in three types: large, medium, and small duty cycles. Then, the minimum phase-shift scheme is applied to each type of PWM signal to ensure the minimum duration of the active vector for stable current reconstruction. With this proposed phase-shift scheme, the maximum allowable modulation index and dc-link voltage utilization are improved. In addition, the relationship between the minimum required time for stable phase current reconstruction and the maximum allowable modulation index is derived theoretically. The effectiveness of the proposed method is verified by both simulation and experimental results. With this scheme, cost and volume of dual inverters in HEVs are reduced, considering the lower number of current sensors. [ABSTRACT FROM PUBLISHER]

This paper investigates a claw-pole double-rotor machine (DRM) for power-split hybrid electric vehicles (HEVs). Based on the mathematical analysis of the machine, the boundary speed–torque characteristic required by the hybrid electric system is studied. To achieve high power density with acceptable torque ripple for automotive applications, the back electromotive force (EMF) and torque performance of the DRM are investigated with respect to the configurations of permanent-magnet rotor, claw-pole dimensions, and air-gap length. Based on the optimized model, characteristics of the claw-pole DRM, such as flux density, inductance, torque, core losses, and efficiency, are investigated by finite-element method. A downsized prototype machine is manufactured and tested. The experimental EMF, inductance, and torque performance agree well with simulation data. A drive cycle containing various working modes of the DRM is carried out, and the feasibility of using the machine as a power-split device for HEVs is validated. [ABSTRACT FROM PUBLISHER]

This paper develops reduced order, linear models of lithium ion batteries that can be used for model-based power train simulation, design, estimation, and controlling in hybrid and electric vehicles (HEV). First, a reduced order model is derived from the fundamental governing electrochemical charge and Li+ conservation equations that are linearized at the operating state of charge and low current density. The equations are solved using analytical and numerical techniques to produce the transcendental impedance or transfer function from input current to output voltage. This model is then reduced to a low order state space model using a system identification technique based on least squares optimization. Given the prescribed current, the model predicts voltage and other variables such as electrolyte and electrode surface concentration distributions. The second model is developed by neglecting electrolyte diffusion and modeling each electrode with a single active material particle. The transcendental particle transfer functions are discretized using a Padé Approximation. The explicit form of the single particle model impedance can be realized by an equivalent circuit with resistances and capacitances related to the cell parameters. Both models are then tuned to match experimental electrochemical impedance spectroscopy (EIS) and pulse current-voltage data. [ABSTRACT FROM AUTHOR]

This paper focuses on building an efficient, online, and intelligent energy management controller to improve the fuel economy of a power-split plug-in hybrid electric vehicle (PHEV). Based on a detailed powertrain analysis, the battery current can be optimized to improve the fuel economy using dynamic programming (DP). Three types of drive cycles, i.e., highway, urban, and urban (congested), are classified, and six typical drive cycles are analyzed and simulated to study all the driving conditions. The online intelligent energy management controller is built, which consists of two neural network (NN) modules that are trained based on the optimized results obtained by DP methods, considering the trip length and duration. Based on whether the trip length and duration are known or unknown, the controller will choose the corresponding NN module to output the effective battery current commands to realize the energy management. Numerical simulation shows that the proposed controller can improve the fuel economy of the vehicle. [ABSTRACT FROM PUBLISHER]

This paper investigates a pricing strategy to maximize the revenue in future plug-in hybrid electric vehicles' market for battery charging services. Service models are developed for a monopoly market with two typical service classes. The unique characteristics of battery charging result in a piecewise linear quality of service (QoS) model. By resorting to the concept of superdifferential, some theoretical results, including the existence and uniqueness of the subscriber equilibrium and the convergence of the corresponding subscriber dynamics, are established. In the course of developing revenue-maximizing pricing strategies for both service classes, a general tradeoff has been identified between monetization and customer acquisition. [ABSTRACT FROM PUBLISHER]

The paper proposes a power-split hybrid electric vehicle control strategy that combines a rule-based controller, including a state-of-charge controller and engine start–stop logic, with a precisely formulated equivalent consumption minimization strategy. A one-dimensional directional search-based instantaneous equivalent consumption minimization strategy optimization and a two-dimensional directional search-based instantaneous equivalent consumption minimization strategy optimization are used to find optimal powertrain points in high-efficiency engine map regions that are previously determined by an offline optimization approach. The simulation results confirm that the rule-based controller performance can be significantly improved when extended with the proposed equivalent consumption minimization strategy formulation, without requiring an adaptive empirical penalty factor to satisfy the state-of-charge sustainability condition. The proposed rule-based + equivalent consumption minimization strategy control strategies were verified against a dynamic-programming-based optimization benchmark for different certification driving cycles. [ABSTRACT FROM AUTHOR]

An in situ carbon-encapsulating solution route for the synthesis of Li4Ti5O12@C composite hollow microspheres has been developed. The finally obtained Li4Ti5O12@C hollow microspheres possess a microsized spherical shape, embedded Li4Ti5O12 nanocrystals, and fully encapsulating conductive carbon shells, which endow this Li4Ti5O12 anode material with high specific capacity, rate capability, and cycle stability. Owing to their unique microstructure characteristics, these hollow Li4Ti5O12@C microsphere composites are promising anode materials for highly efficient Li-ion batteries with an excellent rate capability (discharge capacity of 169, 152, 130, 116, 93, and 76 mA h g-1 at 0.2, 1, 2, 5, 30 and 60 C, respectively) and a significantly enhanced cycling performance. [ABSTRACT FROM AUTHOR]

Hybrid electric vehicles (HEVs) are considered to be one of the energy-efficient technologies for near-term sustainability of the transportation sector. Over the years, research has focused on improving fuel economy (FE) for a given drive cycle, but FE variability over a realistic range of real-world driving patterns has been generally overlooked, and this can lead to FE benefits not being fully realized in real-world usage. No systematic methodology exists to reduce FE variability by design optimization of powertrain components. This study proposes a methodology of powertrain component optimization to reduce the FE variability due to variations in driving patterns. In the proposed methodology, powertrain components are optimum over a range of driving patterns of different traffic conditions and driving styles simultaneously. The proposed methodology demonstrates the potential to reduce FE variability by up to 34% over six driving patterns of different traffic conditions and driving styles. [ABSTRACT FROM PUBLISHER]

Permanent-magnet motors are widely used in-wheel motors of electric vehicles and hybrid vehicles. Based on a movable stator design, this paper presents a new type permanent-magnet motor, whose torque can be adjusted in order to meet different driving requirements. The stator geometry is varied by means of changing movable stator positions. Accordingly, the air-gap length in permanent- magnet motors is changed so that torque can be adjusted. To derive an analytical model, Fourier series expansions are employed to formulate air-gap geometry variation. The analytical model is validated by finite element numerical results. Concerning motor torque variation capability achieved in this study, the ratio of the largest vs. the smallest torque is 2.3. [ABSTRACT FROM AUTHOR]

Hybrid electric vehicles (HEV) in addition to provide the benefits of electric vehicles could satisfy consumers for some performances of conventional internal combustion engine (ICE) vehicles such as acceleration and long range. On this way, suitable energy optimization strategies should be employed to get desired efficiency, less fuel consumption and pollution. One of the favorite and simple configurations of HEVs is a parallel type. A student team at University of Kashan, IRAN have designed and manufactured Shaheb 2 hybrid electric vehicle. It is a separated-axle (or Through-to-Road (TTR)) parallel HEV type based on Pride platform. Employed energy management in Shaheb 2 is on/off strategy and three modes; motor, engine and hybrid have been implemented. This paper investigates the modeling of separated-axle (or TTR) parallel type of HEV in ADVISOR software and then evaluates two control strategies for Shaheb 2; on/off strategy and an intelligent control based on fuzzy logic. On this way, maximizing the engine is considered as an objective function. The simulation results indicate that the fuzzy strategy leads to less fuel consumption and lower pollution for given UDDS driving cycle rather than on/off strategy for Shaheb 2. [ABSTRACT FROM AUTHOR]

Automotive engineers and researchers have proposed different topologies for series–parallel hybrid electric vehicles (SP-HEVs). The Toyota Hybrid System (THS) is the best known SP-HEV-based vehicle, but alternative solutions, such as electric variable transmission (EVT), have been also proposed. An efficient comparison between these different solutions is a key point to estimate the added value of each topology. This paper presents the application of optimal control to two series–parallel hybrid architectures for efficiency assessment purposes. The dynamic programming method is applied to the THS and to a virtual hybrid vehicle with an EVT. The way to take into account the supplementary degree of freedom provided by the decoupling of the wheels and the engine in both topologies is presented. The optimal fuel consumption is then compared on different driving cycles and brings out an overconsumption of the EVT topology. Then, a parametric study shows that inserting an appropriate gear ratio on the internal-combustion-engine (ICE) shaft can improve the EVT efficiency that becomes close to the THS efficiency. [ABSTRACT FROM PUBLISHER]

To improve the shift quality of a specific hybrid electric vehicle with AMT, its two typical working modes are studied with corresponding shift control strategies proposed based on the features of its structure and working modes. A motor / engine's electronic throttle combined speed control strategy is adopted for engine drive mode, while a shift control strategy of motor speed active regulation without clutch disengagement is used for engine / motor hybrid drive mode. The control strategies are verified by both the simulations and bench tests. The results show that the control strategies adopted can effectively reduce shift shock, shorten power interruption time and improve shift quality. [ABSTRACT FROM AUTHOR]