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Overcoming the use of conventional internal combustion engines for future transportation is mandatory to achieve sustainability. In this regard, hybrid electric vehicles comprising hydrogen fuel cells with Li-ion battery storage are gaining more attention. The paper proposes a robust approach for economic power-sharing between these sources. A predictive speed control strategy is realized to improve the vehicle's overall operation compared to conventional proportional–integral–derivative controller and tested for various standardized driving cycles. Additionally, a comprehensive techno-economic analysis using total cost of ownership is performed. Results suggest that hybrid vehicles with fuel cells and batteries are more economical with costs($) of 5. 81 e 04 , 2. 17 e 04 , and 2. 04 e 05 , for transient, modal, and manual driving cycles, respectively compared with internal combustion engines and batteries having 6. 23 e 04 , 3. 21 e 04 , and 2. 54 e 05. Furthermore, for evaluating the performance of the proposed predictive controller in terms of error minimization, various performance indices are obtained, compared, and tested using a hardware-in-loop platform. • Predictive control-based optimal power management approach. • Economic benefits through hydrogen fuel cells (HFCs) as an auxiliary energy source. • The controller's performance investigation through performance indices (PIs). • Validation using simulation and Hardware-in-loop results. • Techno-economic analysis for full lifetime use of the HEV. [ABSTRACT FROM AUTHOR]

This paper proposes an energy management strategy (EMS) based on double‐deep Q‐Networks (DDQN) with demand torque prediction (DTP) to optimize the fuel consumption of hybrid electric vehicles (HEVs) by online utilizing vehicle‐to‐vehicle (V2V) information. The main framework of the EMS is designed as DDQN, combining Q‐learning with the deep neural network to realize real‐time training and online optimization of the torque distribution between the internal combustion engine and the electric motor, and given that the model training easily falls into local minima and overestimation, two different Q‐networks are designed to decouple action selection and target evaluation. Meanwhile, BP‐network predicts future demand torque using the ego vehicle speed, front vehicle speed, and distance between the two vehicles to reduce the burden of model training due to the introduction of traffic information. The predicted demand torque is introduced into the DDQN‐based EMS as states, together with the distance traveled in the driving cycle and the current information on HEV. The effectiveness and adaptability to actual driving cycles of the proposed strategy are verified by comparison with DDQN‐based EMS without DTP and rule‐based EMS. The results show that the fuel consumption using DDQN‐based EMS without DTP is reduced by 4.1% compared with rule‐based EMS. The introduction of demand torque prediction resulted in a further 3.9% reduction in fuel consumption. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This study presents a reinforcement-learning-based approach for energy management in hybrid electric vehicles (HEVs). Traditional energy management methods often fall short in simultaneously optimizing fuel economy, passenger comfort, and engine efficiency under diverse driving conditions. To address this, we employed a Q-learning-based algorithm to optimize the activation and torque variation of the internal combustion engine (ICE). In addition, the algorithm underwent a rigorous parameter optimization process, ensuring its robustness and efficiency in varying driving scenarios. Following this, we proposed a comparative analysis of the algorithm's performance against a traditional offline control strategy, namely dynamic programming. The results in the testing phase performed over ARTEMIS driving cycles demonstrate that our approach not only maintains effective charge-sustaining operations but achieves an average 5% increase in fuel economy compared to the benchmark algorithm. Moreover, our method effectively manages ICE activations, maintaining them at less than two per minute. [ABSTRACT FROM AUTHOR]

Plug-in hybrid electric vehicles (PHEVs) and plug-in electric vehicles (PEVs) have gained enormous attention for their ability to reduce fuel consumption in transportation and are, thus, helpful in the reduction of the greenhouse effect and pollution. However, they bring up some technical problems that should be resolved. Due to the ever-increasing demand for these PHEVs, the simultaneous connection of large PEVs and PHEVs to the electric grid can cause overloading, which results in disturbance to overall power system stability and quality and can cause a blackout. Such situations can be avoided by adequately manipulating power available from the grid and vehicle power demand. State of charge (SoC) is the leading performance parameter that should be optimized using computational techniques to charge vehicles efficiently. In this research, an efficient metaheuristic algorithm, accelerated particle swarm optimization (APSO), and its five variants were applied to allocate power to vehicles connected to the grid intelligently. For this, the maximization of average SoC is considered a fitness function, and each PHEV can be connected to the grid once a day so that the maximum number of cars can be charged daily. To statistically compare the performance of these six algorithms, one-way ANOVA was used. Simulation and statistical results obtained by maximizing this highly non-linear objective function show that accelerated particle swarm optimization with Variant 5 achieved some improvements in terms of computational time and best fitness value. The APSO-5 solution has a considerable percentage increase compared with the solution of other variants of APSO for the four PHEV datasets considered. Moreover, after 30 trials, APSO 5 gives the highest possible fitness value among all the algorithms. [ABSTRACT FROM AUTHOR]

The efficiency of hybrid electric vehicles may be substantially increased if the energy of exhaust gases, which do not complete the expansion inside the cylinder of the internal combustion engine, is efficiently recovered using a properly designed turbo-generator and employed for vehicle propulsion. Previous studies, carried out by the same authors of this work, showed a potential hybrid vehicle fuel efficiency increment up to 15% employing a 20 kW turbine on a 100 HP-rated power thermal unit. The innovative thermal unit proposed here is composed of a supercharged engine endowed with a properly designed turbo-generator, which comprises two fundamental elements: an exhaust gas turbine expressly designed and optimized for the application, and a suitable electric generator necessary to convert the recovered energy into electric energy, which can be stored in the on-board energy storage system of the vehicle. In this two-part work, the realistic efficiency of the innovative thermal unit for hybrid vehicles is evaluated and compared to a traditional turbocharged engine. In Part 1, the authors presented a model for the prediction of the efficiency of a dedicated radial turbine, based on a simple but effective mean-line approach; the same paper also reports a design algorithm, which, thanks to some assumptions and approximations, allows fast determination of the right turbine geometry for a given design operating condition. It is worth pointing out that, being optimized for quasi-steady power production, the exhaust gas turbine here considered is quite different from the ones commonly employed for turbocharging applications; for this reason, and in consideration of the required power size, such a turbine is not available on the market, nor has its development been previously carried out in the scientific literature. In this paper, Part 2, a radial turbine geometry is defined for the thermal unit previously calculated, employing the design algorithm described in Part 1; the realistic energetic advantages that could be achieved by the implementation of the turbo-generator on a hybrid propulsion system are evaluated through the performance prediction model under different operating conditions of the thermal unit. As an overall result, it was estimated that, compared to a reference traditional turbocharged engine, the turbo-compound system could gain vehicle efficiency improvement between 3.1% and 17.9%, according to the output power delivered, with an average efficiency increment of 10.9% evaluated on the whole operating range. [ABSTRACT FROM AUTHOR]

This paper proposes a forward conversion topology using a multi-winding transformer known as global modular balancing (GMB) for multiple supercapacitor cells connected in a series of high-voltage applications. The proposed topology can easily be modularized and is not limited to the number of supercapacitors existing in the modules. Moreover, the proposed GMB needs merely one MOSFET switch for each supercapacitor, and a one Pulse width modulation circuit that can control all the switches in the modules. Furthermore, the proposed topology is employed to achieve voltage balancing in a supercapacitor string. The proposed topology balancing performance is validated through MATLAB/Simulink Software. [ABSTRACT FROM AUTHOR]

This study presents a reinforcement-learning-based approach for energy management in hybrid electric vehicles (HEVs). Traditional energy management methods often fall short in simultaneously optimizing fuel economy, passenger comfort, and engine efficiency under diverse driving conditions. To address this, we employed a Q-learning-based algorithm to optimize the activation and torque variation of the internal combustion engine (ICE). In addition, the algorithm underwent a rigorous parameter optimization process, ensuring its robustness and efficiency in varying driving scenarios. Following this, we proposed a comparative analysis of the algorithm’s performance against a traditional offline control strategy, namely dynamic programming. The results in the testing phase performed over ARTEMIS driving cycles demonstrate that our approach not only maintains effective charge-sustaining operations but achieves an average 5% increase in fuel economy compared to the benchmark algorithm. Moreover, our method effectively manages ICE activations, maintaining them at less than two per minute.

Plug-in hybrid electric vehicles (PHEVs) and plug-in electric vehicles (PEVs) have gained enormous attention for their ability to reduce fuel consumption in transportation and are, thus, helpful in the reduction of the greenhouse effect and pollution. However, they bring up some technical problems that should be resolved. Due to the ever-increasing demand for these PHEVs, the simultaneous connection of large PEVs and PHEVs to the electric grid can cause overloading, which results in disturbance to overall power system stability and quality and can cause a blackout. Such situations can be avoided by adequately manipulating power available from the grid and vehicle power demand. State of charge (SoC) is the leading performance parameter that should be optimized using computational techniques to charge vehicles efficiently. In this research, an efficient metaheuristic algorithm, accelerated particle swarm optimization (APSO), and its five variants were applied to allocate power to vehicles connected to the grid intelligently. For this, the maximization of average SoC is considered a fitness function, and each PHEV can be connected to the grid once a day so that the maximum number of cars can be charged daily. To statistically compare the performance of these six algorithms, one-way ANOVA was used. Simulation and statistical results obtained by maximizing this highly non-linear objective function show that accelerated particle swarm optimization with Variant 5 achieved some improvements in terms of computational time and best fitness value. The APSO-5 solution has a considerable percentage increase compared with the solution of other variants of APSO for the four PHEV datasets considered. Moreover, after 30 trials, APSO 5 gives the highest possible fitness value among all the algorithms.

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

In this manuscript, we address the problem of online optimal control for torque splitting in hybrid electric vehicles that minimises fuel consumption and preserves battery life. We divide the problem into the prediction of the future velocity profile (i.e. driver intention estimation) and the online optimal control of the hybrid powertrain following a Model Predictive Control (MPC) scheme. The velocity prediction is based on a bio-inspired driver model, which is compared on various datasets with two alternative prediction algorithms adopted in the literature. The online optimal control problem addresses both the fuel consumption and the preservation of the battery life using an equivalent cost given the estimated speed profile (i.e. guaranteeing the desired performance). The battery degradation is evaluated by means of a state-of-the-art electrochemical model. Both the predictor and the Energy Management System (EMS) are evaluated in simulation using real driving data divided into 30 driving cycles from 10 drivers characterised by different driving styles. A comparison of the EMS performances is carried out on two different benchmarks based on an offline optimization, in one case on the entire dataset length and in the second on an ideal prediction using two different receding horizon lengths. The proposed online system, composed of the velocity prediction algorithm and the optimal control MPC scheme, shows comparable performances with the previous ideal benchmarks in terms of fuel consumption and battery life preservation. The simulations show that the online approach is able to significantly reduce the capacity loss of the battery, while preserving the fuel saving performances.

The efficiency of hybrid electric vehicles may be substantially increased if the energy of exhaust gases, which do not complete the expansion inside the cylinder of the internal combustion engine, is efficiently recovered using a properly designed turbo-generator and employed for vehicle propulsion. Previous studies, carried out by the same authors of this work, showed a potential hybrid vehicle fuel efficiency increment up to 15% employing a 20 kW turbine on a 100 HP-rated power thermal unit. The innovative thermal unit proposed here is composed of a supercharged engine endowed with a properly designed turbo-generator, which comprises two fundamental elements: an exhaust gas turbine expressly designed and optimized for the application, and a suitable electric generator necessary to convert the recovered energy into electric energy, which can be stored in the on-board energy storage system of the vehicle. In this two-part work, the realistic efficiency of the innovative thermal unit for hybrid vehicles is evaluated and compared to a traditional turbocharged engine. In Part 1, the authors presented a model for the prediction of the efficiency of a dedicated radial turbine, based on a simple but effective mean-line approach; the same paper also reports a design algorithm, which, thanks to some assumptions and approximations, allows fast determination of the right turbine geometry for a given design operating condition. It is worth pointing out that, being optimized for quasi-steady power production, the exhaust gas turbine here considered is quite different from the ones commonly employed for turbocharging applications; for this reason, and in consideration of the required power size, such a turbine is not available on the market, nor has its development been previously carried out in the scientific literature. In this paper, Part 2, a radial turbine geometry is defined for the thermal unit previously calculated, employing the design algorithm described in Part 1; the realistic energetic advantages that could be achieved by the implementation of the turbo-generator on a hybrid propulsion system are evaluated through the performance prediction model under different operating conditions of the thermal unit. As an overall result, it was estimated that, compared to a reference traditional turbocharged engine, the turbo-compound system could gain vehicle efficiency improvement between 3.1% and 17.9%, according to the output power delivered, with an average efficiency increment of 10.9% evaluated on the whole operating range.

The equivalent consumption minimization strategy (ECMS) has been considered as a practical energy management strategy for the hybrid electric vehicles (HEVs) because it can be implemented in real time while providing satisfactory performance. However, it is still challenging to adjust an equivalent factor (EF) to its own optimal value in real time because the EF is fundamentally affected by the current driving condition. Although many adaptive ECMSs (A-ECMSs) have been developed to adjust the EF based on a charge-sustaining condition, they do not adequately respond to a change in the driving conditions. In this study, a novel ECMS for the HEVs is proposed to provide the near-optimal performance by considering actual driving conditions. First, the near-optimal condition for the EF is defined to consider a driving condition. Based on it, an iterative scheme is presented to numerically obtain the near-optimal EF. Then, the convergence analysis of the iterative scheme is conducted with practical considerations to implementing the proposed method into real-world applications. Simulation results show that the proposed strategy has better adaptability to changes in the driving conditions with a smaller loss of optimality than conventional A-ECMS which relies only on the charge-sustaining condition. The proposed strategy is also experimentally validated under real-world driving conditions. [ABSTRACT FROM AUTHOR]

The belt-driven starter/generator (BSG), as a cost-effective solution, has been widely employed in hybrid electric vehicles (HEVs) to improve the stability and reduce the fuel consumption of the vehicles. It can provide more than 10% reduction in CO2. Electrical machine is the heart of the BSG system, which is functioned both as motor and generator. In order to optimize both aspects of motor and generator simultaneously, this paper presents a new multimode optimization method for the switched reluctance machines. First, the general multimode concept and optimization method are presented. The switched reluctance motor and the switched reluctance generator are the two operation modes. The optimization models are established based on motor and generator functions. Sensitivity analysis, surrogate models and genetic algorithms are employed to improve the efficiency of the multimode optimization. Then, a design example of a segmented-rotor switched reluctance machine (SSRM) is investigated. Seven design variables and four driving modes are considered in the multiobjective optimization model. The Kriging model is employed to approximate the finite element model (FEM) in the optimization. Finally, the optimization results are depicted, and an optimal solution is selected. The comparison between the initial and optimal designs shows that the proposed method can improve the foremost performance of the SSRM under all driving modes. [ABSTRACT FROM AUTHOR]

Due to increasing fuel prices, the world is moving towards the use of hybrid electric vehicles (HEVs) because they are environmentally friendly, require less maintenance, and are a green technology. The energy management system (EMS) plays an important role in HEVs for the efficient storage of energy and control of the power flow mechanism. This paper deals with the design, modeling, and result-oriented approach for the development of EMS for HEVs using a fuzzy logic controller (FLC). Batteries and supercapacitors (SCs) are used as primary and secondary energy storage systems (ESSs), respectively. EMS consists of the ultra-power transfer algorithm (UPTA) and FLC techniques, which are used to control the power flow. The UPTA technique is used to charge the battery with the help of a single-ended primary inductor converter (SEPIC) during regenerative braking mode. The proposed research examines and compares the performance of FLC with a proportional integral (PI) controller by using MATLAB (Simulink) software. Three scenarios are built to confirm the efficiency of the proposed design. The simulation results show that the proposed design with FLC has a better response as its rise time (2.6 m) and settling time (1.47 µs) are superior to the PI controller. [ABSTRACT FROM AUTHOR]

The magnetic-field modulated brushless double-rotor machine (MFM-BDRM), getting rid of brush and slip ring, is a preferable selection for the power splitting device in hybrid electric vehicles. In practical application, however, the MFM-BDRM is facing the challenge of low power factor owing to its special operating principle—the magnetic field modulation principle. Although some researchers dedicate themselves to improving the power factor of MFM-BDRM, the cause of low power factor in the MFM-BDRM is still not clear. This article will investigate the power factor problem of MFM-BDRM, systematically. First, the inductance feature of MFM-BDRM based on the magnetic field modulation principle is investigated. Second, the fundamental cause of the low power factor of MFM-BDRM is analyzed. Furthermore, three feasible attempts are made and investigated to improve the power factor of MFM-BDRM. Finally, a prototype of MFM-BDRM is manufactured, and parts of the analysis are verified by experiments. [ABSTRACT FROM AUTHOR]

Multimode hybrid electric vehicles (HEVs) have been widely applied due to its high efficiency and excellent overall performance. However, the introduction of multimode HEVs makes it difficult to choose reasonable design factors (architectures, component parameters, and control strategies) and determine correct mode shift rules, which undoubtedly enhances design complexity. To improve the design efficiency and reduce screen difficulty of optimal multimode configurations, a systematic design method is proposed to generate, screen, and optimize multimode HEVs with a single planetary gear. Based on equivalent tree graph method, the novel architectures are first generated. Then, considering mode shift rules and component parameters, requirements of speed and torque in corresponding main mode and minimum shift rule graph of working modes are taken as constraints to complete configuration screening. Finally, the equivalent consumption minimization strategy considering the proposed mode shift rules is put forward to evaluate and optimize energy management. The numerical results show that fuel economy of optimized configurations is improved by at least 22.1%, which proves the effectiveness of proposed systematic design method. Furthermore, it reveals that the multimode configuration can achieve better fuel economy when adopting the appropriate mode shift rule and designing the right arrangement and number of clutches and brakes. [ABSTRACT FROM AUTHOR]

This article proposes a novel back-to-back competitive learning mechanism (BCLM) for a fuzzy logic (FL) supervisory control system of hybrid electric vehicles (HEVs). This mechanism allows continuous competition between two fuzzy logic controllers during real-world driving. The leading controller will have the regulatory function of the supervisory control system. First, the configuration of the HEV model and its FL-based control system are analyzed. Second, the algorithm of chaos-enhanced accelerated particle swarm optimization (CAPSO) is developed for back-to-back learning of the membership function. Third, based on fuel-prioritized cost functions, the regulation of competitive assessment is designed to select a controller with a better fuel economy. Finally, the competitive performance of using the CAPSO algorithm is contrasted with other swarm-based methods and the BCLM-driven control system is validated by a hardware-in-the-loop test. The results demonstrate that the BCLM control system significantly reduces fuel consumption, at least 9% from charge sustaining and charge depleting based, and at least 7% from conventional FL-based systems. [ABSTRACT FROM AUTHOR]

The 48-V diesel mild-hybrid electric vehicle equipped with an electrically heated catalyst is a cost-effective solution to reduce tailpipe emissions, while its supervisory control design is more challenging, due to cross-coupled energy and thermal subsystems. This article presents a distributed control design for the energy and thermal management system, aiming to optimize fuel economy and reduce catalyst warm-up time, by cooperation between electrical heating and engine load upshift. A control-oriented model of HEV and catalyst is developed, which captures the dynamics of the battery state of charge and catalyst temperature. The energy and thermal management problem is cast as a hybrid optimal control problem, to minimize entire trip fuel consumption under different switching (warm-up) times. An off-line solution method is designed to exploit the tradeoff between fuel and warm-up time based upon hybrid minimum principle and a nested shooting algorithm. For online implementation, a distributed supervisory control is proposed, which covers both cold-start and normal operations, and is compatible with the equivalent consumption minimization strategy. Both off-line and online methods are validated in simulation, using two driving cycles and five warm-up speeds. [ABSTRACT FROM AUTHOR]

Power-split hybrid technologies have been transformed from single mode to multimode to realize even greater savings in fuel and more powerful output. However, the optimization procedure of the mode shift map for multimode hybrid electric vehicles (HEVs) has not been formalized. In this paper, the types of mode shift are first classified as direct/indirect and conditional/unconditional. The optimal shift path with minimum energy variation for the indirect mode shift is formulated as a shortest path problem and solved by Dijkstra’s algorithm. By taking the energy loss during mode shift into consideration, the method for designing a mode shift map is presented from the perspective of the normalized system efficiency. The derived mode shift map is combined with a novel energy management strategy, referred to as normalized efficiency maximum strategy, to formulate an integrated real-time control strategy for the multimode HEV. Finally, by comparing two other commonly used strategies, dynamic programing and equivalent consumption minimization strategy, the proposed control strategy shows great potential in improving fuel economy while maintaining smooth engine operation, thus leading to better ride comfort of the vehicle. [ABSTRACT FROM AUTHOR]

This paper proposes a cascaded multiport switched reluctance motor (SRM) drive for hybrid electric vehicle applications, which not only allows flexible energy conversion among the generator/ac grid, the battery bank, and the motor, but also achieves battery management (BM) function for state-of-charge balance control and bus voltage regulation. By integrating the battery packs into the asymmetrical half-bridge converter, the cascaded BM modules are designed to configure multilevel bus voltage and current capacity for SRM drive, which can accelerate the excitation and demagnetization processes during the commutation region, extend the speed range, reduce the voltage stress on the switches, and improve the torque capability and system efficiency. According to the different operation requirements, the multiple driving modes, regenerative braking modes, and charging modes are equipped in the proposed converter. Moreover, with the proposed BM strategy, each battery pack can be separately connected or disconnected from the power supply, which will greatly enhance the fault-tolerance ability and easily avoid the overcharge and overdischarge issues during the motor operation. The feasibility and effectiveness of the proposed cascaded multiport SRM drive are verified by the experiments on a three-phase 12/8 SRM. [ABSTRACT FROM AUTHOR]

This paper deals with the study and implementation of a losses minimization control strategy for an integrated multidrive (IMD) topology used in the drivetrain of parallel hybrid electric vehicles. In this IMD, the stator sections of a multiwinding induction machine are interfaced to the storage units through standard three-phase inverters. The goal of this study is to optimize the management of multidirectional power flows in an IMD configuration, reducing both the power losses of the induction machine and the energy storage units while keeping the computational complexity suitably low. To this aim, the proposed approach consists in a control strategy that continuously searches for the best compromise between the torque response and the power capability of each storage unit while minimizing the overall power losses. Using such an approach, a significant improvement in the overall efficiency is obtained. Simulations and experimental tests confirm the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The large-scale spread of hybrid electric vehicles (HEVs) in the automotive industry is dominated today by continuous variable transmission parallel powertrain as far as medium size car are concerned. As a matter of fact, the tendency in this sector is to design parallel architectures in that they have provided until now a higher overall powertrain efficiency. However, technological improvements in many electric components and their increased efficiency can reshape this vision and give new outlines on HEV structure design. This paper aims to show the potentials of the series architecture, thanks to the latest electrical improvements especially in storage systems and power electronics. In particular, simulation models and fuel consumption data over different road missions are presented for the series architecture proposed in this study. In addition, a comparison between these results and data available in the scientific literature for state-of-the-art HEVs is reported. [ABSTRACT FROM AUTHOR]

Hybrid electric vehicles are getting popular due to the depletion of fossil fuel and natural gas reserves. It is important to utilize renewable energy sources to avoid the horrific effects of global warming and greenhouse gas emissions. This study presents an optimized nonlinear controller for a hybrid energy storage system (HESS) of fuel cell, battery, supercapacitor, and hybrid photoelectrochemical and photovoltaic cells (HPEV) based hybrid electric vehicles. All these energy sources are connected to a DC-DC power converter followed by a DC-AC inverter and a motor. Lyapunov based nonlinear controller is proposed to achieve tight DC bus regulation, good tracking of sources current, and global asymptotic stability of the closed loop system. The gain parameters of the proposed nonlinear controller are optimized using the grey wolf optimization (GWO) algorithm for performance improvement. Maximum power point tracking of HPEV is performed using an artificial neural network. Experimental data from the extra-urban driving cycle is used to demonstrate the performance of the proposed optimized HESS using Matlab/Simulink. To validate the performance of the proposed system the simulation results are compared with hardware in the loop experimental results. It can be observed from the simulation results that the proposed GWO optimized nonlinear controller decreases errors, and enhances the performance of the dynamical system. • Mathematical modeling of the proposed HPEV, FC, battery, and SC based HESS for HEVs. • MPPT of HPEV using ANN under varying temperature and irradiance. • Nonlinear controller is designed for DC bus voltage regulation of the proposed HESS. • Gain optimization of nonlinear controller using a grey wolf optimization algorithm. • Hardware in the loop experiment is performed to validate the proposed topology. [ABSTRACT FROM AUTHOR]

This paper presents a control design approach for optimizing the comfortability of hybrid electric powertrains in acceleration mode. A parallel hybrid electric vehicle powertrain system with two motors and a single turbo-charged engine is considered. In acceleration mode, it is assumed that the desired acceleration rate cannot be satisfied by using the electrical motor individually. The first challenge is managing the combustion engine to assist power generation and power split such that the system satisfies comfortability, and the second challenge is modeling the comfortability (e.g., analytically describing the human feeling). This paper exploits a black-box module typically used in the automotive industry to quantitatively evaluate comfortability. A genetic algorithm is applied to find the optimum power split and gear schedule that can improve the comfortability evaluated by the module in acceleration mode. The simulation results conducted on a simulator with a practical background demonstrate the significance of the proposed design approach. [ABSTRACT FROM AUTHOR]

Due to increasing fuel prices, the world is moving towards the use of hybrid electric vehicles (HEVs) because they are environmentally friendly, require less maintenance, and are a green technology. The energy management system (EMS) plays an important role in HEVs for the efficient storage of energy and control of the power flow mechanism. This paper deals with the design, modeling, and result-oriented approach for the development of EMS for HEVs using a fuzzy logic controller (FLC). Batteries and supercapacitors (SCs) are used as primary and secondary energy storage systems (ESSs), respectively. EMS consists of the ultra-power transfer algorithm (UPTA) and FLC techniques, which are used to control the power flow. The UPTA technique is used to charge the battery with the help of a single-ended primary inductor converter (SEPIC) during regenerative braking mode. The proposed research examines and compares the performance of FLC with a proportional integral (PI) controller by using MATLAB (Simulink) software. Three scenarios are built to confirm the efficiency of the proposed design. The simulation results show that the proposed design with FLC has a better response as its rise time (2.6 m) and settling time (1.47 µs) are superior to the PI controller.

To obtain the better performance of Hybrid Electric Vehicles/Electric Vehicles (HEVs/EVs), are implemented with Hybrid Energy Storage System (HESS), in that Battery is main source and UltraCapacitor (UC) is the auxiliary source. The battery is connected to DC Bus through Boost Converter and UC is connected through Buck-Boost Converter. Battery and UC voltage levels are maintained less than the DC bus voltage. The main aim of this research is to design an intelligent controller for a smooth transition between the sources in the HESS. A Math Function Based (MFB) Controller is modeled and implemented to an electric motor for HEVs/EVs application. The MFB controller has to work based on the motor's speed and this controller creates the closed loop operation of the overall system with smooth operation between the energy sources. Proportional-Integral-Derivative (PID) controller is used here to maintain the constant voltage profile for various loads at the terminals of the electric motor. Combination of PID and MFB controllers have given closed loop operation of the entire model with smooth switching between the sources. HESS with proposed controller has been applied to four different types of loads and another type with consecutive load changes are simulated in MATLAB/Simulink and obtained satisfactory results. [ABSTRACT FROM AUTHOR]

Hybrid electric vehicles (HEVs) require the power to drive the vehicle via a combination of internal combustion engine (ICE) and electric machine (EM). To improve the drivability, the smooth torque change during the driving mode switching is essential. This task can be achieved by using the coordinated control strategy. This paper presents a coordinated control strategy based on considering the different dynamic response characteristics of the ICE and the EM, which can effectively suppress the torque surge during the driving mode switching processes. The novelty lies in the proposed control is a motor active synchronization control strategy without clutch disengagement based on the mode switching classification. The coordinated control strategy is designed according to the classification of the driving modes. The objective is to minimize torque fluctuation and maintain or improve the driving performance of the vehicle. Results from the computer simulation demonstrate the effectiveness of this approach in reducing the torque surge without sacrificing vehicle performance. [ABSTRACT FROM AUTHOR]

This paper proposes a powertrain controller for a solar photovoltaic battery powered hybrid electric vehicle (HEV). The main objective of the proposed controller is to ensure better battery management, load regulation, and maximum power extraction whenever possible from the photovoltaic panels. The powertrain controller consists of two levels of controllers named lower level controllers and a high-level control algorithm. The lower level controllers are designed to perform individual tasks such as maximum power point tracking, battery charging, and load regulation. The perturb and observe based maximum power point tracking algorithm is used for extracting maximum power from solar photovoltaic panels while the battery charging controller is designed using a PI controller. A high-level control algorithm is then designed to switch between the lower level controllers based on different operating conditions such as high state of charge, low state of charge, maximum battery current, and heavy load by respecting the constraints formulated. The developed algorithm is evaluated using theoretical simulation and experimental studies. The simulation and experimental results are presented to validate the proposed technique. [ABSTRACT FROM AUTHOR]

This paper proposes a new real-time energy management strategy (R-EMS) to improve the fuel economy performance of the power-split hybrid electric vehicles (HEVs). Being different from most conventional optimization-based EMS, the R-EMS does not need priori information of the driving cycle and is used to the online control of HEV. The forward dynamic model of power-split powertrain is built based on the Prius MY10. At each instant, the proposed R-EMS tries to minimize the equivalent consumed power of the HEV, which is the weighted summation of gasoline power and battery output power. The equivalence factor of battery output power has a clear physical meaning that is the efficiency of gasoline energy transferred to battery energy. Another two coefficients are introduced to control the state of charge (SOC) of battery. By considering the engine torque and engine speed as two independent or dependent design variables, respectively, the 2-D R-EMS and 1-D R-EMS are formed. Several typical driving cycles are used to simulate the performance of the R-EMS, and the results show that the proposed R-EMS not only maintains the battery SOC but also saves the fuel consumption compared with the rule-based EMS. [ABSTRACT FROM AUTHOR]

This paper investigates a flux-modulated double-rotor machine (FM-DRM) for series–parallel hybrid electric vehicles (HEVs). Based on flux-modulated gears, the FM-DRM has similar speed-torque characteristics. Two configuration schemes are analyzed and compared for the hybrid electric system based on the FM-DRM. The boundary operating conditions of the FM-DRM system are discussed. Moreover, the power flows regarding various operating modes are investigated and corresponding power equations are deduced. An HEV system based on the FM-DRM is simulated for a driving cycle of the United States Environmental Protection Agency to verify the various operating modes. A prototype FM-DRM is manufactured and tested by a driving cycle. The feasibility of using the FM-DRM as a power-splitting device for HEVs is validated by experimental results. [ABSTRACT FROM AUTHOR]

Adapting the energy management strategy (EMS) to a specific route is important for plug-in hybrid electric vehicles (PHEVs), because it is usually preferred to apply a blended strategy to use the electricity and fuel energy during the whole trip to maximize the energy efficiency. Studies show that both the route information and human driving style have great impact on vehicle energy consumption. This brief focuses on developing an EMS for PHEVs running on multiple frequent routes, while the controller may not know the destination of each trip beforehand. The human driving style influence can be learned from his or her historical driving data, and the possible future routes as well as their probabilities can be obtained using global positioning system information and historical driving data. An optimal control problem for the higher level PHEV control is formulated and solved. This EMS can be implemented onboard in real time. Simulation results show that it consumes only 2.5% more energy than the posterior global optimal result in an eight-frequent-route scenario without needing to know the destination of each trip in advance. [ABSTRACT FROM AUTHOR]

In this paper, a fast calculation method using finite-element method (FEM) analysis-based magnetostatic data with a parameterized geometry is proposed to find the optimal ratio of the outer diameter and the axial length for switched reluctance motors (SRMs). The proposed method shortens the amount of calculation time by a factor of more than one-hundred compared to the transient FEM analysis. The proposed method is verified by two SRMs for automotive traction applications. It is found that the optimal ratios of the outer diameter to the axial length and the stator outer diameter to the rotor outer diameter are around 2.5 and 0.65, respectively. It is also found that the motor volume has little influence on the optimal ratio, whereas the current density increases the optimized ratio of the stator and rotor outer diameters. [ABSTRACT FROM AUTHOR]

This paper presents a novel platform for accurate mathematical modeling of the propulsion system of electric cars. It provides, for the first time, a hardware-in-the-loop (HIL) real-time experimental verification case study of a General Motor Chevrolet Volt for power, control systems, and the mechanical systems. The contribution of this paper can be categorized into three approaches: First, each component of the powertrain is accurately modeled taking transient dynamics of all components of the electric vehicle into consideration. Further, a PSIM simulation platform is consequently developed to demonstrate the validity of this mathematical modeling. Finally, the Typhoon HIL is used to provide the experimental verification of the proposed model in real time, which precisely validates the viability of the model. HIL technology is used to prototype and test the proposed control system while simulating the power circuit on the HIL module platform. The permanent magnet synchronous motor and the power electronics hardware components are simulated in real time at which the parameters can be changed during the simulation. However, the control algorithm is generated as a C code and downloaded to the TI controller that exists on a digital signal processing board. The results from the simulation based on PSIM environment and hardware validations using HIL are in agreement, which validates the developed model. The performance has been investigated under different load operating conditions in real time to verify its robustness. The case study can be extended for any electric car as it provides a generic platform for modeling any propulsion system. [ABSTRACT FROM AUTHOR]

This paper presents a new brushless electrically excited claw-pole generator (EECPG) for vehicle power generation. The proposed design uses a toroidal excitation coil to magnetize all the stator poles and then generate effective power by complementary saliency effect of dual rotors. One salient advantage of this new design is its good excitation ability with minimized excitation copper loss as well as improved energy conversion efficiency. Meanwhile, the brush and slip rings are eliminated compared to the conventional rotor-EECPG. The generator structure is introduced, with its operating principle illustrated by 3-D finite-element predicated flux distribution. Rotor configurations using interlaced and non-interlaced types are both elaborated in terms of back electromotive force harmonic distribution and cogging effect. Load characteristic is predicted by the field-circuit coupled analysis, which proves the proposed brushless claw-pole generator could serve as a high-efficiency cost-effective candidate for vehicle power generation. [ABSTRACT FROM AUTHOR]

Under a low-temperature environment, electric vehicles face serious environmental adaptability problems, and efficient vehicle thermal management strategies are urgently needed. This paper presents a novel engine–battery coupled thermal management strategy for connected hybrid electric vehicles (HEVs). An improved system structure for an engine–battery coupled thermal management system (engine–battery CTMS) is designed to avoid unnecessary heat loss. The control requirements of the engine–battery CTMS include minimum engine fuel consumption, minimum power battery aging damage and minimum system energy consumption, which constitutes a multi-objective optimal control problem in a finite time domain. Based on model predictive control (MPC) theory, a switched nonlinear MPC (NMPC) control strategy is proposed to solve the optimal control problem of the complex coupled multi-input multi-output system. To verify the effectiveness of the proposed strategy, three comparative experiments of the centralized NMPC-based and rule-based methods combined with the improved system structure and the unimproved system structure are designed. The results of the cosimulation experiment between MATLAB/Simulink and AMEsim under various driving cycles and different ambient temperatures show that the improved structure and switched control strategy confer great advantages in reducing the controller computation burden, engine fuel consumption, and power battery aging damage. • An improved engine–battery coupled thermal management system is designed. • A switched NMPC strategy is designed to effectively reduce the computation burden. • Effectively reduce fuel consumption, battery aging damage, and energy consumption. • Conduct co-simulation experiments under various conditions and temperatures. [ABSTRACT FROM AUTHOR]

Hybrid Electric Vehicles (HEVs) have been proven to be a promising solution to environmental pollution and fuel savings. The benefit of the solution is generally realized as the amount of fuel consumption saved, which by itself represents a challenge to develop the right energy management strategies (EMSs) for HEVs. Moreover, meeting the design requirements are essential for optimal power distribution at the price of conflicting objectives. To this end, a significant number of EMSs have been proposed in the literature, which require a categorization method to better classify the design and control contributions, with an emphasis on fuel economy, providing power demand, and real-time applicability. The presented review targets two main headlines: (a) offline EMSs wherein global optimization-based EMSs and rule-based EMSs are presented; and (b) online EMSs, under which instantaneous optimization-based EMSs, predictive EMSs, and learning-based EMSs are put forward. Numerous methods are introduced, given the main focus on the presented scheme, and the basic principle of each approach is elaborated and compared along with its advantages and disadvantages in all aspects. In this sequel, a comprehensive literature review is provided. Finally, research gaps requiring more attention are identified and future important trends are discussed from different perspectives. The main contributions of this work are twofold. Firstly, state-of-the-art methods are introduced under a unified framework for the first time, with an extensive overview of existing EMSs for HEVs. Secondly, this paper aims to guide researchers and scholars to better choose the right EMS method to fill in the gaps for the development of future-generation HEVs.

A hybrid electric vehicle (HEV) is a product that can greatly alleviate problems related to the energy crisis and environmental pollution. However, replacing such a battery will increase the cost of usage before the end of the life of a HEV. Thus, research on the multi-objective energy management control problem, which aims to not only minimize the gasoline consumption and consumed electricity but also prolong battery life, is necessary and challenging for HEV. This paper presents an adaptive equivalent consumption minimization strategy based on a recurrent neural network (RNN-A-ECMS) to solve the multi-objective optimal control problem for a plug-in HEV (PHEV). The two objectives of energy consumption and battery loss are balanced in the cost function by a weighting factor that changes in real time with the operating mode and current state of the vehicle. The near-global optimality of the energy management control is guaranteed by the equivalent factor (EF) in the designed A-ECMS. As the determined EF is dependent on the optimal co-state of the Pontryagin’s minimum principle (PMP), which results in the online ECMS being regarded as a realization of PMP-based global optimization during the whole driving cycle. The time-varying weight factor and the co-state of the PMP are map tables on the state of charge (SOC) of the battery and power demand, which are established offline by the particle swarm optimization (PSO) algorithm and real historical traffic data. In addition to the mappings of the weight factor and the major component of the EF linked to the optimal co-state of the PMP, the real-time performance of the energy management control is also guaranteed by the tuning component of the EF of A-ECMS resulting from the Proportional plus Integral (PI) control on the deviation between the battery SOC and the optimal trajectory of the SOC obtained by the Recurrent Neural Network (RNN). The RNN is trained offline by the SOC trajectory optimized by dynamic programming (DP) utilizing the historical traffic data. Finally, the effectiveness and the adaptability of the proposed RNN-A-ECMS are demonstrated on the test platform of plug-in hybrid electric vehicles based on GT-SUITE (a professional integrated simulation platform for engine/vehicle systems developed by Gamma Technologies of US company) compared with the existing strategy.

For both globally suboptimal solution and implementable strategy, a real-time energy management strategy, based on equivalent consumption minimization strategy (ECMS), is proposed for commuter hybrid electric vehicles (HEVs) running on fixed routes. The determination of the adaptive equivalence factor is a focus. By the statistical characteristics deriving from historical driving data, the infinite-horizon stochastic dynamic programming (SDP) optimization with a discount factor is first formulated for finding proper equivalence factor according to uncertain driving cycles on a fixed route. And then, a mapping of equivalent factor on the system state is established off-line by stochastic optimal solution deriving from SDP policy iteration algorithm. In the power splits online, the equivalence factor of the implemented adaptive ECMS is obtained from the mapping according to the real time driving condition to achieve the near global optimal control objective that fuel consumption is minimized and the battery state of charge (SOC) is maintained within the boundaries over the whole driving route. Based on the HEV test platform established by specialized GT-Suite, simulation results and comparisons in some real driving cycles are presented to verify the effectiveness of the proposed strategy and to evaluate the advantages over other strategies.