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As the development of hydrogen fuel cell cars moves from concept to mass production, customers need safe, practical, and easy refueling experiences. The performance and lifetime of fuel cell stacks, as well as other operational factors like valve functioning, are greatly influenced by the purity of hydrogen. The advances achieved by earlier researchers in promoting hydrogen as a possible significant fuel source for the future are fully examined in this publication. Hydrogen is an energy carrier that has the potential to replace fossil fuels. It may be used as a fuel source for cars that run on fuel cells as well as internal combustion engines. To minimize aberrant combustion, extra consideration has to be given to engine design when considering hydrogen as a fuel for internal combustion engines. This strategy may result in higher power output, lower nitrogen oxide (NOx) emissions, and enhanced engine efficiency. The research explores the designs of fuel cell cars that use hydrogen by converting it into energy as well as the designs of internal combustion vehicles fueled by hydrogen via combustion. In addition, hybrid variants of these cars are shown for a thorough comprehension of the topic. The article also looks at the structure and future possible benefits of hydrogen fuel cell electric cars in the context of the automotive sector; these topics are covered in a separate section. [ABSTRACT FROM AUTHOR]

This paper proposes a new Coyote Swarm Multi-Agent Intelligent Optimization (CSMAIO) Algorithm for a real-time battery management strategy through the wireless rapid-charging performance of hybrid electric vehicles (HEVs). Any electric vehicle can be charged by using any one of the modes among wired and wireless charging modes. The wireless charging system, which transfers electricity from a transmitter to a receiver without making contact, is the most important among them. It is obvious that this power varies with speed and is primarily used to recharge the battery. The proposed CSMAIO performance is analyzed with wired, wireless and proposed wireless rapid-charging methods. Comparative analysis has been done for SOC and torque at different speeds in case of wired, wireless and proposed wireless rapid-charging method. The effectiveness of wireless rapid-charging approach using the proposed CSMAIO at various speeds and torque levels has been evaluated by employing MATLAB Simulink and demonstrated that the slowest speed produces the high SOC and best results. High speed reduces the SOC. The SOC has been analyzed at different speeds, and it has been demonstrated that for speed of 10 km/h in case of wired charging method the SOC is 30%, in case of wireless charging method the SOC is 25% and in case of wireless rapid-charging method the SOC is 40% that is highest among all the three methods. In case of wired charging method torque is 450 Nm, and in case of wireless charging method the torque is 470 Nm. The proposed wireless rapid-charging approach is producing high torque of 500 Nm during a particular speed compared to other charging methods such as wired and wireless methodology. Thus, the efficient battery state-of-charge (SOC) performance is preserved, and lower fuel usage is also achieved with high torque by using the proposed CSMAIO approach. [ABSTRACT FROM AUTHOR]

Accurate knowledge of capacity is crucial for battery management systems as it enables them to predict future failures, specifically through the state of health metric. The primary focus of this study is to estimate battery capacity using the enhanced self-organization maps (EASOM) algorithm. The EASOM is employed here to minimize uncertainties in state of charge estimation and measurements. It identifies the most suitable candidate using an objective function and incorporates a fading memory forgetting factor to improve battery capacity estimation. To validate the precision of the proposed algorithm, six tests were conducted for hybrid electric vehicle and electric vehicle applications. EASOM demonstrated excellent performance, with a maximum error of 1.25%. Additionally, all predictive performance metrics (the mean absolute percentage error, the root-mean-square error, and the mean squared error) were within 1% in all tests. [ABSTRACT FROM AUTHOR]

Summary This paper outlines the development of an interleaved bidirectional DC–DC converter featuring single‐phase high‐frequency isolation and employing two distinct pulse width modulations (PWMs), one for each power flow direction. The analysis of converter stages, expressions for DC voltage gains, and voltage/current stresses in continuous conduction mode are presented. The proposed converter offers several advantages, including a new modulation approach, a current ripple frequency, and voltage in two ports that are four times higher than the switching frequency. Additionally, it entails a reduction in the volume and weight of passive elements and ensures balanced current distribution between the arms. Moreover, the single‐phase transformer offers galvanic isolation between the DC bus and the battery or supercapacitor. An experimental prototype has been implemented in the laboratory to validate the mathematical and theoretical analysis for both energy flow directions under the test conditions, with a converter‐rated power of 2.5 kW, a low voltage side of 180 V, and a high voltage side of 380 V. [ABSTRACT FROM AUTHOR]

All the major economies across the globe have been working towards achieving a commercial viability of fuel cell-powered (FCV) and hybrid electric vehicles (HEV) amid limited fossil fuel reserves as well as environmental factors. Working in the similar direction, the presented work is an experimental investigation on ionic hydrogen storage in an activated carbon (AC) electrode integrated in a modified reversible polymer electrolyte fuel cell (PEFC) for transport applications that is carried out with an aim to check the cost benefits over the HEVs. A lab scale PEFC is developed and equipped with a self-standing porous aC electrode for hydrogen adsorption/desorption. The developed PEFC is test run and recorded parameters are compared with a typical HEV for cost benefit analysis using HOMER Pro microgrid software. The obtained results confirm the technical feasibility of the concept and showcase the lower cost of FCV compared to a HEV for a fixed span of running life time. The ingress and egress of hydrogen within the developed PEFC of 6.25 cm2 active area successfully stored 559.65 mA h/g during charging and give out 510.51 mA h/g while discharging. Various such cells could be stacked either in series or parallel to meet the load demand of the HEV drive motors. It is a maiden attempt to present a real-time cost comparison between a FCV and a HEV that would contribute towards make a selection of future vehicles in a sustainable society. [ABSTRACT FROM AUTHOR]

In most situations, fuel cells (FCs) are insufficient to supply power demands in hybrid electric vehicles (HEVs), thus battery storage systems (BSSs) are used to make the system more efficient like as rapid starting, high power density, and enhanced dynamic set response. How the power sources are regulated and distributed determines the accomplishment efficiency of the driving forces in such a configuration. The most critical aspect should be how to guarantee torque stability of the traction motor which is in our case a permanent magnet synchronous motor (PMSM) while attaining quick reaction. This study discusses a hybrid battery‐FCs energy storage and management system for a hybrid electric vehicle (HEV), as well as an integrated PMSM's passivity‐based control (PBC) technique to enable power integration and increase the hybrid electric vehicle (HEV)'s operating speed. The present paper is separated into two sections. First, a power management control (PMC) technology is used to manage the FCs‐battery system to guarantee that the HEV gets continuous power from the hybrid energy resources, where a fuzzy logic controller is used as an artificial intelligence system for the FCs converter to maximize the power produced. The second is dedicated to the new proposed PBC control of the EV's PMSM to rectify non‐linearities, external disturbances, and parametric fluctuations. The standard proportional‐integral (PI) control and sliding mode control (SMC) are built for comparison with the proposed PBC dedicated to the PMSM in order to properly assess the efficacy of the proposed control strategy. The simulation results obtained using MATALB/Simulink show that the proposed PBC technique and hybrid battery‐ proton‐exchange membrane PEMFCs energy management enables high power integration and increase the EV's operational speed. [ABSTRACT FROM AUTHOR]

The Hybrid Electric Vehicle's (HEV) fuel efficiency is directly related to the vehicle's Power Management Strategy (PMS). An Artificial Neural Network (ANN) is described here as a PMS. As more and more of our sources of electricity come from renewable sources, Artificial Intelligence (AI) is becoming more important for coordinating the use of these resources. With solar Photovoltaic (PV) panels and Proton Exchange Membrane Fuel Cells (PEMFCs) as the primary energy generators, and a battery bank and ultracapacitor / supercapacitor as the secondary energy storage devices, the hybrid power supply is an environmentally friendly system. The MATLAB Simulink programme family is used to create the suggested model. Assessment has been performed for various PMS based on hydrogen consumption, overall efficiency, State of Charge (SOC) of ultracapacitors and batteries, stress on hybrid sources and stability of the Direct Current (DC) system. Simulation results show that the proposed scheme meets the power demand of typical driving cycles like, the Highway Fuel Economy Test cycle (HWFET), New York City Cycle (NYCC) and Urban Dynamometer Driving Schedule (UDDS) for testing the vehicle's performance. When compared to the Proportional Integral (PI), State Machine Control (SMC), Frequency Decoupling Fuzzy Logic Control (FDFLC), Equivalent Consumption Minimization Strategy (ECMS) and External Energy Minimization Strategy (EEMS), the use of ANN in this proposed work reduces hydrogen consumption by 2.4% to 6.7% and battery SOC is maintained between 62 and 68%. [ABSTRACT FROM AUTHOR]

Summary: The sensorless speed control of permanent magnet synchronous motor (PMSM) is gaining popularity in hybrid electric vehicle (HEV) applications leading to its enhanced safety, reliability, and cost savings. Speed control using vector control for PMSM‐fed HEV requires the speed encoder. When the speed sensor information fails, the inverter must ensure power delivery to the PMSM continuously by estimating the speed; this mode of operation is referred as limp‐home mode in HEV. In this paper, a speed sensorless scheme has been proposed for PMSM‐based HEV during limp‐home mode operation. This paper presents a model reference adaptive system (MRAS) speed estimator based on an adaptive neural network controller (NNC) for speed estimation of PMSM. In the HEV application, in case of speed/position encoder failure, the speed of the PMSM can be estimated by stator flux using stator current.The proposed method employs stator currents in the reference model to eliminate the DC drift problem. Furthermore, the NNC is employed in the adaptation mechanism to improve the Federal Test Procedure (FTP75) driving cycle performance. The performance of the proposed control scheme has been validated with dSPACE 1104 R & D rapid development controller using vector control for the PMSM during variable speed and torque, including the zero‐speed applications [ABSTRACT FROM AUTHOR]

In this study, a parallel hybrid electric vehicle produced within the scope of our project titled "Development of Fuel Efficiency Enhancing and Innovative Technologies for Internal Combustion Engine Vehicles" has been modeled. Firstly, a new rule-based control method is proposed to minimize fuel consumption and carbon emission values in driving cycles in the experimental model of the parallel hybrid electric vehicle produced within the scope of this project. The proposed control method ensures that the internal combustion engine (ICE) operates at the optimum point. In addition, the electric motor (EM) is activated more frequently at low speeds, and the electric motor can also work as a generator. Then, a new dataset was also created on a traffic-free racetrack with the proposed control method for fuel consumption estimation of a parallel hybrid electric vehicle using ECE-15 (Urban Driving Cycle), EUDC (Extra Urban Driving Cycle), and NEDC (New European Driving Cycle) driving cycles. The data set is dependent on 11 different input variables, which complicates the system. Afterward, the fuel estimation process is made with seven different machine learning methods (ML), and these methods are compared using the obtained data set. To avoid overfitting machine learning, two different test data sets were created. The Random Forest algorithm is the most suitable technique in terms of training and testing the fuel consumption model using correlation coefficient ( R 2 ), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) simulation appropriateness for both test datasets. Moreover, the random forest algorithm achieved an impressive accuracy of 97% and 90% for both test datasets, outperforming the other algorithms. Furthermore, the proposed method consumes 4.72 L of fuel per 100 km, while the gasoline-powered vehicle consumes 7 L of fuel per 100 km. The results show that the proposed method emits 4.69 kg less C O 2 emissions. The effectiveness of the Random Forest Algorithm has been verified by both simulation results and real-world data. [ABSTRACT FROM AUTHOR]

The recent trend of transitioning from cars with internal combustion engines to green cars has accelerated amidst growing concerns of a climate crisis and the COVID-19 pandemic. South Korea is a particularly good test bed for exploring green mobility alternatives and setting priorities for green car policies, owing to its fast-growing green car market and its considerable share in the worldwide purchases of fuel cell electric vehicles. Accordingly, this study explores the Korean conceptualization of green cars and the prioritization of different aspects of future green car policy using qualitative Q methodology and additional interviews. A total of 31 transport policy experts participated in our survey, resulting in three distinct perspectives: a hybrid vehicle supporter with comparative pragmatism, an electric vehicle supporter from multiple aspects, and a radical ecologist searching for new alternatives. Based on these expert insights, we determine the most appropriate green car type and provide important recommendations for future green car policy. [ABSTRACT FROM AUTHOR]

The hybrid electric vehicle (HEV) is a critical transportation disruptive technology that is expected to be widely adopted in the current and future marketplace. Many nations are promoting the success of HEVs. As the technologies and designs of these vehicles are significantly different from conventional vehicles, it is also important to understand the technical and body-related issues associated with these vehicles. This study used the National Highway Traffic Safety Administration's vehicle owner's complaint database to explore the potential issues associated with HEVs. The acquired dataset was divided into two groups based on their involvement in traffic crashes. The study applied association rule mining and text mining methods to analyze vehicle consumer complaint data. The results of association rule mining showed a significant association between hybrid electric all-wheel-drive vehicles manufactured between 2010 and 2021 that do not have anti-lock brakes and cruise control in the crash-related vehicle complaints dataset. Non-HEV vehicles, manufactured between 1992 and 1999, with cruise control and anti-braking systems as well as 5-10 cylinders, appeared frequently in the crash-related complaint dataset. Mileage-related issues and comparatively older HEVs (2000-2009) are dominant in non-crash-related data. The results from the text mining method show that brakes, mileage, failure, and crash are key features for consumer complaints related to crashes and brakes, battery, power, and recall are the key features for consumer complaints not related to crashes. The sentiment analysis results show slightly higher negative sentiments in complaint reports associated with crashes. The findings of this study can provide some insights into this unexplored research area. [ABSTRACT FROM AUTHOR]

Thermoelectric generators have emerged as a potential method for recovering energy from vehicle exhaust. In this study, a hybrid electric vehicle with a thermoelectric generator is dynamically designed and modeled in Simcenter Amesim software. The comprehensive model of the hybrid vehicle system that has been modeled can navigate in all-electric and parallel hybrid modes according to its control method. The driving cycle measured in Tehran city is used in this study, and its results were compared to the other standard driving cycles. In order to analyze the vehicle's dynamic behavior under varied driving situations, two overall scenarios, including constant and changing velocities, were studied. Seven speed profiles were considered in the constant speed mode, and their effects on the amount of energy produced by the thermoelectric generator were shown. It was discovered that with a constant speed of 40 m s−1, the energy production due to temperature difference in the TEG modulus is 96.9 kJ, whereas this amount was 42.8 kJ in the real driving condition. Also, it was demonstrated that increasing vehicle's velocity raises the flow rate and temperature of exhaust gases, enhancing the effect of the thermoelectric generator. In addition, the performance of thermoelectric generators' energy production in standard cycles compared to the surveyed cycle of Tehran city has been investigated. It was observed that the amount of energy ratio, which was 6% in the real driving cycle, could reach 13% in the standard driving cycle. [ABSTRACT FROM AUTHOR]

The economic emission dispatch problem is one of the most crucial problems for the future smart power network. The integration of renewable energy resources and the use of electric vehicles (EVs) are various techniques that have been suggested to deal with the increase in greenhouse gas emissions. However, to carefully examine various scenarios of integration of the above elements is important to reduce emission rates while considering economic constraints. In this research, the effectiveness of the use of photovoltaics (PVs) and EVs' discharging/charging behavior is investigated from various perspectives in a hybrid economic emission dispatch. Here, a multi-objective model includes the optimization of two objectives: cost and pollution is suggested. Based on a penalty factor, we convert the multi-objective function into a single objective. For solving this non-convex, nonlinear and on-smooth problem, a modified whale optimization algorithm (WOA) is exerted to gain the optimum results. The idea of two sub-modifications is derived from Lévy flight algorithm and the mutation operator of the genetic algorithm. The verification of the suggested model is performed by the IEEE 30-bus system with 6-units. The simulated results illustrate that the incorporation of PV along with EVs in economic dispatch problem can improve the pollution emission, the economic costs, and convergence rate, effectively. [ABSTRACT FROM AUTHOR]

In this study, a coordinated control problem of power sources and actuators of a series-parallel hybrid electric vehicle on mode switching condition is addressed. There exist numerous nonlinear factors in the hybrid driveline system which induce system disturbance and will deteriorate the control performance on mode switching condition. However, coupled system nonlinearity is not taken into consideration for controller design on mode switching condition, which can lead to severe driveline oscillation and thus vehicle longitudinal jerk. For this reason, a disturbance observer and compensation method is introduced to resolve the control problem on mode switching condition. First, an analytic model of driveline torsional vibration considering disturbance brought by system nonlinearity is established and simplified for controller design. Based on the simplified model, a disturbance observer-sliding mode control strategy is proposed to suppress mode switching vibration. Numerical simulation is carried out to validate control performance of the DOB-SMC strategy. Simulation results demonstrate that DOB-SMC strategy has better ability to cancel the unwanted effects of disturbance brought by system nonlinearity compared with sliding mode control method, which can be regarded as an effective approach to attenuate vehicle longitudinal jerk and thus improve ride comfort on mode switching condition. [ABSTRACT FROM AUTHOR]

In order to improve the downhill regenerative braking control effect of hybrid electric vehicle (HEV), this paper designs a downhill regenerative braking control method for hybrid electric vehicle (HEV). Firstly, the structures of regenerative braking system, auxiliary braking system and eddy currregenerative braking control, hybrid electric vehicle, braking force distribution, partition coefficientent retarder (ECR) are analyzed. Secondly, the braking torque of ECR when HEV downhill braking is calculated. Then, the braking process of HEV is dynamically analyzed, and the braking force distribution constraint of HEV is realized according to the relevant laws and regulations. Finally, the downhill regenerative braking control of HEV is realized based on the braking force distribution coefficient. The results show that when the vehicle speed is up to 120 km·h, the speed control can still be completed within 2 s by using the proposed method, indicating a good regenerative braking effect. [ABSTRACT FROM AUTHOR]

The recent trend in hybrid electric vehicles (HEV) has increased the need for vehicle charging stations (VCS) in the distribution system. In this condition, the additional load in the system leads to an increase in power loss, reduction in voltage and reliability of the system. The drawbacks of introducing this additional load can be rectified by integrating distributed generation (DG) into the distribution system. In this paper, the ideal location for placing DG is identified through the voltage stability index. The power loss minimization objective function is formulated with all the required constraints to estimate the size of DG required for the distribution system. Moreover, loss of load probability is used as a reliability assessment technique, through which the system reliability is analyzed after assessing the impact of integrating VCS and DG. Simulations are carried out to compare the following cases: a system without VCS and DG, a system that has only VCS and a system that has both VCS and DG. The IEEE 12-bus and 33-bus test systems are considered. In the 12-bus system with both VCS and DG, the power loss is reduced by 56% when compared with the system with only VCS, while the net reliability is also improved. The reliability of the system is evaluated for a 24 h load variation. The proposed work provides an efficient tool to improve the reliability of the system with support from DG. [ABSTRACT FROM AUTHOR]

Over the years, many models have been created to estimate pollution inventories and fuel usage. These models can be divided into two types: travel-based and fuel-based. One of the most used travel-based models for estimating emission inventories is driving cycles. It can be used for a variety of different things, such as establishing pollution regulations, traffic control, and calculating journey time. For these goals, researchers have previously attempted to use easily available, well-established driving cycles. In many ways, however, the local environment differs greatly from that of the driving cycle's genesis. As a result, these cycles' applications have failed to provide high-quality results. This research aims to analyse the various approaches utilised for driving cycle construction in various locations of Malaysia under varied operational situations. [ABSTRACT FROM AUTHOR]

The electric mode is the main operational mode of dual-motor hybrid electric vehicles (HEVs), so the reliability of the dual-motor electric drive system (DEDS) is particularly important. To research the electromechanical coupling mechanism of the DEDS of HEVs, firstly, considering the time-varying mesh stiffness of gears and the nonlinear characteristics of inverters, an electromechanical coupling dynamics model of the DEDS was established, including the permanent magnet synchronous motor (PMSM) and the gear transmission system. Then, the electromechanical coupled dynamic characteristics of the DEDS in the single-motor and dual-motor drive modes were analyzed under steady-state and impact load conditions, respectively. The results show that the motor stator current frequency is modulated by the complicated gear meshing frequency, and the operation state of the gear transmission system can thus be monitored in the stator current. Impact load causes the instantaneous torsional vibration of the transmission system dominated by the first-order natural frequency, and the vibration characteristic frequency appears in the form of a side frequency in the stator current signal; moreover, compared with the single-motor drive mode, the speed synchronization error in the dual-motor drive mode will aggravate torsional vibration in the gear system. The impact energy of the gear system caused by external impact load can be suppressed by reducing the speed synchronization error. [ABSTRACT FROM AUTHOR]

Over the last few decades, lithium-ion batteries have grown in importance for the use of many portable devices and vehicular applications. It has been seen that their life expectancy is much more effective if the required conditions are met. In one of the required conditions, accurately estimating the battery's state of charge (SOC) is one of the important factors. The purpose of this research paper is to implement the probabilistic filter algorithms for SOC estimation; however, there are challenges associated with that. Generally, for the battery to be effective the Bayesian estimation algorithms are required, which are recursively updating the probability density function of the system states. To address the challenges associated with SOC estimation, the research paper goes further into the functions of the extended Kalman filter (EKF) and sigma point Kalman filter (SPKF). The function of both of these filters will be able to provide an accurate estimation. Further studies are required for these filters' performance, robustness, and computational complexity. For example, some filters might be accurate, might not be robust, and/or not implementable on a simple microcontroller in a vehicle's battery management system (BMS). A comparison is made between the EKF and SPKF by running simulations in MATLAB. It is found that the SPKF has an obvious advantage over the EKF in state estimation. Within the SPKF, the sub-filter, the central difference Kalman filter (CDKF), can be considered as an alternative to the EKF for state estimation in battery management systems for electric vehicles. However, there are implications to this which include the compromise of computational complexity in which a more sophisticated micro-controller is required. [ABSTRACT FROM AUTHOR]

Hybrid electric vehicles can achieve better fuel economy than conventional vehicles by utilizing multiple power sources. While these power sources have been controlled by rule-based or optimization-based control algorithms, recent studies have shown that machine learning-based control algorithms such as online Deep Reinforcement Learning (DRL) can effectively control the power sources as well. However, the optimization and training processes for the online DRL-based powertrain control strategy can be very time and resource intensive. In this paper, a new offline–online hybrid DRL strategy is presented where offline vehicle data are exploited to build an initial model and an online learning algorithm explores a new control policy to further improve the fuel economy. In this manner, it is expected that the agent can learn an environment consisting of the vehicle dynamics in a given driving condition more quickly compared to the online algorithms, which learn the optimal control policy by interacting with the vehicle model from zero initial knowledge. By incorporating a priori offline knowledge, the simulation results show that the proposed approach not only accelerates the learning process and makes the learning process more stable, but also leads to a better fuel economy compared to online only learning algorithms. [ABSTRACT FROM AUTHOR]

Since the coupling relationship of excitation sources is complicated, meantime the motor torque changes quickly under the acceleration condition, the problem of torsional vibration is prominent. This paper studies an active damping control (ADC) strategy for a parallel hybrid electric vehicle (HEV) under acceleration condition. Primarily, a full-order dynamic model is built, and the corresponding motion equations are derived. Moreover, a controller design–oriented model is established based on model reduction algorithm. Furthermore, a method that considers time delay characteristics of actuator based on model predictive control (MPC) theory is proposed to solve the torsional vibration problem. The controller handles delay characteristics of an actuator by state-space reorganization method, and the optimal control sequence is obtained by solving the objective function. Finally, the controller is tested using Simulink simulation and hardware-in-loop simulation platform, which mainly includes the verification of model reduction and vibration damping effect. The results show that simplified third-order model has a good consistency with the original full-order model in time and frequency domain. Meanwhile, the designed controller has a considerable damping effect and ensures the comfort performance of the vehicle. This study provides an important reference for vibration control of the hybrid powertrain. [ABSTRACT FROM AUTHOR]

Following years, electric vehicles (EVs) are promising technology for shifting scattered exhaust emissions in mega‐cities to integrated power plants in rural areas, especially in urban areas. Transport sector electrification and increased popularity of EVs make scientists and researchers, search for charging stations. The ideal position, charge scheduling, and developed charging infrastructure are the primary concern for the large‐scale deployment of EVs. This paper describes the possible demand for EVs charging station infrastructure and challenges in the Indian situation. Along these lines, this study also provides the research community with the latest developments and research findings of charging infrastructure for EVs in India. [ABSTRACT FROM AUTHOR]

Introduction/purpose: The paper presents a systematic approach to the development of a series hybrid electric tracked vehicle (HETV) including powertrain sizing and adequate energy management strategy (EMS) selection. Methods: Powertrain elements were sized considering key performance requirements. Three energy management strategies were proposed: Thermostat Control Strategy (TCS), Power Follower Control Strategy (PFCS), and Optimal Power Source Strategy (OPSS). The evaluation of the powertrain configuration and the three proposed EMSs was performed in the Simulink environment using a driving cycle containing significant acceleration, braking and steering. Results: The results showed that the OPSS proved to be the best due to increased fuel economy and a low battery state of charge (SOC) variation. Compared to the previous research of the same vehicle with a parallel hybrid configuration, significantly better results were achieved. The investigation of the results indicates that the proposed powertrain and control strategy offer 53.79% better fuel economy which indicates that the powertrain sizing was properly performed. Conclusions: The results of this work are of great importance for understanding the effect of proper powertrain sizing on fuel economy. Compared to the reference vehicle, the proposed configuration achieves significant improvement, most of which is attributed to adequate sizing. The OPSS proved to be the best strategy, thus confirming the theoretical hypothesis. The series hybrid configuration with the OPSS as the EMS proved to be a major candidate for use in HETVs. [ABSTRACT FROM AUTHOR]

Any engineering system involves transitions that reduce the performance of the system and lower its comfort. In the field of automotive engineering, the combination of multiple motors and multiple power sources is a trend that is being used to enhance hybrid electric vehicle (HEV) propulsion and autonomy. However, HEV riding comfort is significantly reduced because of high peaks that occur during the transition from a single power source to a multisource powering mode or from a single motor to a multiple motor traction mode. In this study, a novel model-based soft transition algorithm (STA) is used for the suppression of large transient ripples that occur during HEV drivetrain commutations and power source switches. In contrast to classical abrupt switching, the STA detects transitions, measures their rates, generates corresponding transition periods, and uses adequate transition functions to join the actual and the targeted operating points of a given HEV system variable. As a case study, the STA was applied to minimize the transition ripples that occur in a fuel cell-supercapacitor HEV. The transitions that occurred within the HEV were handled using two proposed transition functions which were: a linear-based transition function and a stair-based transition function. The simulation results show that, in addition to its ability to improve driving comfort by minimizing transient torque ripples and DC bus voltage fluctuations, the STA helps to increase the lifetime of the motor and power sources by reducing the currents drawn during the transitions. It is worth noting that the considered HEV runs on four-wheel drive when the load torque applied on it exceeds a specified torque threshold; otherwise, it operates in rear-wheel drive. [ABSTRACT FROM AUTHOR]

In this article, a novel dual-rotor multiple-winding machine (DRMWM) is proposed for the continuous variable transmission (CVT) in hybrid electric vehicles (HEVs). First, a mathematical model of the proposed machine is constructed by using the harmonic modeling method. Then, on the basis of the magnetic field analysis, the operating principle of the DRMWM is deduced. By introducing multiple windings with two different working harmonics, namely the permanent magnet (PM) harmonics, and the magnetic gear harmonics, the proposed DRMWM can cope with various operating conditions of HEVs. To further obtain optimal performance, a finite-element analysis and multiobjective genetic algorithm II combined sequential optimization is carried out for the proposed DRMWM. The proposed optimization method is proven to be more computationally efficient compared to the global optimization method. Finally, a prototype is manufactured and tested in various operating modes. The results validate the simulation and analytical method predictions. Also, it proves that the proposed DRMWM is suitable to work as the CVT in HEVs. [ABSTRACT FROM AUTHOR]

Summary: In this article, a novel hybrid method is proposed to optimally manage the energy for a hybrid electric vehicle system. The proposed technique is the joint execution of both the Kernel Wingsuit Flying Search Algorithm and Sea Lion Optimization Algorithm, hence it is called WF2SLOA. The main objective of the WF2SLOA method is integrated in the energy management system to split the torque between the engine and electric machine. During the WF2SLOA‐based energy management development, this article performs a parametric investigation on numerous main factors, such as state types and number of states, states and action discretization, exploration and exploitation, and learning experience selection. The proposed method is implemented in MATLAB/Simulink, and the performance is assessed with the existing methods. Consequently, the outcomes illustrate that the selection of the learning experience can diminish the fuel consumption of the vehicle. Furthermore, the states and action discretization study indicates the fuel consume of the vehicle diminishes as action discretization enhances while raising the states discretization is harmful to the fuel consume. The maximizing count of states also raises the economy of fuel. Thus, the simulation outcomes show that the performance of the proposed method is more efficient than the existing methods. The mean, median, and SD of the WF2SLOA method attains 1.5420, 1.5043, and 0.0509. [ABSTRACT FROM AUTHOR]

Particle number (PN) emissions from hybrid electric vehicles (HEV) during engine ignition and re-ignition events are an important but scarcely reported area. The objectives of the present work are to study the effects of drive cycle properties on the engine behaviour of a hybrid electric vehicle (HEV) and to investigate how this impacts the tailpipe PN emissions and their size distributions (PNSD). Worldwide harmonised light vehicles test cycle (WLTC) testing was conducted, as well as chassis dynamometer emission measurements over a realistic real driving emissions (RDE) speed pattern, using a Euro 5 Toyota Prius HEV with a Cambustion DMS500 sampling PN concentrations at the tailpipe. It is shown that the number of vehicle stops during a test cycle has a direct impact on the re-ignition activity for the HEV. 64 ± 3% of the total PN from WLTC testing was produced during engine re-ignition events while only 6 ± 1% was from stabilised engine operation. Similar proportions were observed for the RDE-style test cycle. The majority of engine reignition and destabilised activity, and hence PN emission, was during the low-speed sections of the drive cycles used. The average PNSD across cycle phases was different between cycles, due to the influence of dynamic properties on engine behaviour and hence the PN emission profile. The PNSD at the engine re-ignition and destabilised events had a merged wide peak with a maximum at 60 nm diameter and a shoulder at 12 nm diameter. The HEV had increased emissions of particles smaller than 23 nm under cold start, but similar overall PN emission values, compared to a warm start. The results of this work highlight the importance of controlling HEV PN emissions to limit human exposure to PN in urban environments where the majority of PN emissions occur. The sensitivity of HEV PN emission factors and PNSD to engine behaviour and, in turn, test cycle dynamic properties, is important to note when considering legislative test cycles, particularly with reference to the freedoms afforded by the RDE test cycle. The results also indicate that substantial improvements to air quality could be made by reducing the particle measurement protocol PN cut-off size to 10 nm. [ABSTRACT FROM AUTHOR]

In order to improve the fuel economy of hybrid electric vehicle (HEV) with single row planetary gear and reduce the fuel consumption of the HEV, an energy management strategy based on gated recurrent unit neural network (GRU-NN) speed predictive model and adaptive differential evolution (A-DE) algorithm was proposed. The future speed of HEV was predicted under the framework of model predictive control (MPC). The energy management strategy converted the global optimization solution problem in the entire working condition into a local optimization solution in the prediction time domain. Aiming at the lowest fuel consumption of the engine and the balance of battery state of charge (SOC) during driving, the optimal control sequence in the prediction domain was solved by A-DE. The simulation results show that the energy management strategy based on the GRU-NN and A-DE reduces fuel consumption by 4.55% compared with that of equivalent consumption minimization strategy (ECMS), and the fuel economy reaches 93.04% compared with that of dynamic programming (DP) under the driving cycle collected by vehicle. [ABSTRACT FROM AUTHOR]

Hybrid energy in electric vehicles has been globally accepted as the fossil fuels will be exhausted by the middle or end of 21st century. The hybrid vehicles have emerged as a major source of clean and renewable energy to support the never-ending demand of vehicles. The main idea of the paper is the use of hybrid energy in working of e-rickshaw, which has become the most favorite area of research in automobile industry, recently. The proposed model is powered by lead-acid battery and this battery is charged by either using electric plug or solar panel. In the present study, hardware model has been analyzed e-rickshaw in terms of different parameters like mileage, charging and discharging time of battery, charging current, discharging current from the performance as well as security point of view. The hardware model has been developed with Photovoltaic (PV) module, capacitor bank, and Arduino circuit. Here, a modified e-rickshaw has been developed which has improved vehicle performance as compared to existing conventional e-rickshaw. The mileage has improved from 80 to 120.61 km and starting current has been reduced from 57 to 41 A on full load conditions. This has enhanced the battery life and Arduino circuit was implemented to enhance security features. The proposed model of e-rickshaw has improved vehicle performance as compared to existing conventional e-rickshaw in terms of mileage, starting current, battery life, and security feature of the vehicle. [ABSTRACT FROM AUTHOR]

Hybrid technology has been regarded as an effective way to realize vehicle energy conservation and emission reduction. The P2 hybrid electric vehicles were taken as the research object. Considering that the state of charge (SOC) of battery is high, medium and low, the different energy management strategies were designed to maintain the balance of SOC while achieving the energy saving and emission reduction of hybrid electric vehicles. On the basis of rule, the multi-objective genetic algorithm in the Isight software was adopted by the strategy to optimize the threshold, so as to improve the reliability and effectiveness of the threshold in the energy management strategy. According to the simulation results, it indicates that the energy management strategy has a significant effect on the reduction of vehicle fuel consumption, which is also able to maintain the balance of battery SOC. [ABSTRACT FROM AUTHOR]

Reliability Redundancy Allocation Problem (RRAP) plays a very major role in reliability improvement and designing of any structure which depends on arrangement of components, reliability allocation for the components, redundancy allocation for the components. In this work, a hybrid electric vehicle (HEV) is considered for RRAP. The non-linear mixed integer programming problem is optimized using the metaheuristic named HPSGWO. The HPSGWO fuses the Particle Swarm Optimizations (PSO) exploitation ability with the grey wolf optimizers (GWO) exploration ability. The mathematical model calculates the maximum achievable reliability for HEV, by the implementation of the HPSGWO while the volume and weight are set as per the available resources. [ABSTRACT FROM AUTHOR]

To overcome such Hybrid Electric Vehicle (HEV) engine problems as high fuel consumption and poor economical efficiency when working in low-efficiency areas, this paper intends to establish the ISG light hybrid vehicle structure-based mathematical models of engines, electric motors, batteries and the whole vehicle power output first; to analyse the relationship between the engine load rate and the optimal engine fuel consumption characteristics, and then to take into consideration the performance characteristics of the motor battery output efficiency, so as to come up with the switching pattern of the ISG parallel hybrid vehicle operating mode. According to the switching rules, the primary influence parameters of the engine load rate as well as the State of Charge (SOC) were extracted and a fuzzy neural network control strategy for torque management was developed. Under the Matlab/Advisor joint simulation platform, a simulation model was established and the effectiveness of the fuzzy control strategy was verified under the typical state of cyclic operation NEDC. The simulation results indicate that the adoption of fuzzy neural network control strategy for torque management can lead to considerable advancements in engine operating efficiency–the fuel economical efficiency can be improved by about 12%. [ABSTRACT FROM AUTHOR]

Battery Thermal Management System (BTSM) is essential for maintaining optimal operation conditions for hybrid electric vehicles (HEVs) and electric vehicles (EVs). This study aimed to propose an innovative impinging jet cooling BTMS for HEVs using mist cooling. The dilute mist completely evaporated to avoid the risk of external circuit failure or corrosion that could result from surface wetting. Impinging experiments were performed under the conditions where inflow air temperature, T a,in, was 21.2 °C ≤ T a,in ≤ 31.0 °C and initial humidity, φ , was 50.9 %RH ≤ φ ≤ 96.0 %RH. It was found that the target plate was cooled down by up to 0.8 K without surface wetting by adding 5.5 mg/s water mist to the air. Numerical analyses were performed under conditions ranging from 21.2 °C ≤ T a,in ≤ 31.0 °C and 0.0 %RH ≤ φ ≤ 100.0 %RH. The results and discussion highlight the importance of the critical initial humidity, φ critical : the complete evaporative threshold. Deviation between the experimental and numerical results at a fixed inflow air temperature, Δ φ , was −1.9 %RH ≤ Δ φ ≤ 3.0 %RH. Δ φ was within the range of measurement uncertainty U (φ) = 4.0 %RH. Thus, the experimental and numerical results were consistent within the experimental measurement uncertainty. As a result, φ critical tends to be high in the case of high inflow temperature. The mist cooling is a viable way of BTMS for HEVs without surface wetting due to its large cooling capacity that results in a 7.4 K cooling effect in a hot environment. [Display omitted] • Mist jet impinging battery thermal management system for HEVs was investigated. • Guaranteed safe operation through complete evaporation of the water mist. • Numerical simulations and experiments confirmed efficient mist evaporation. • Achieved a mist cooling effect of up to 7.4 K. • Simple structure enables high cooling efficiency with air and water supply. [ABSTRACT FROM AUTHOR]

The growing market for hybrid electric vehicles (HEV) has raised concerns about the long-term impacts of non-ionizing radiation (NIR) exposure. This study is the first to address the impact of NIR on consumer choice between HEV and internal combustion engine (ICE) vehicles. We explore the hypothesis that NIR is associated with a lower probability of HEV choice in the presence of NIR information and the relative effect of NIR-health concerns versus environmental attitudes and driving norms. The data are collected from a stated choice experiment and estimated via a hybrid choice model. The results show that i NIR is associated with a lower choice probability of HEV, ii NIR-dread is associated with a higher probability of choosing ICE vehicles, while skepticism about NIR is associated with a higher probability of choosing HEV, iii prompting positively or negatively framed information about NIR discourages HEV choice compared to providing no information. [ABSTRACT FROM AUTHOR]

To address the comprehensive optimization problem of driving performance and fuel economy in the driving process of hybrid electric vehicles (HEV) in the car-following scene in the connected environment, an energy management strategy (EMS) based on front vehicle speed prediction and ego vehicle speed planning is designed by combining intelligent transportation system (ITS) technology. The front vehicle speed predictor is first established based on the long short-term memory neural network (LSTM). Then, based on the predicted speed of the front car, the predictive cruise control (PCC) strategy is designed for realizing the speed control in the car-following scene by combining it with the adaptive cruise control (ACC). Finally, based on the planned vehicle speed, deep reinforcement learning (DRL)-based EMS is used to optimize the power distribution among different power components of HEVs. The analysis of simulation results under the SUMO-Python joint simulation platform verifies the proposed strategy. • A novel speed prediction method based on LSTM is proposed. • The established method improves the prediction accuracy effectively. • An advanced PCC strategy proposed to improve the safety and comfort of the vehicle. • A hierarchical control strategy combining car-following control and energy management is proposed and verified. [ABSTRACT FROM AUTHOR]

Fuel cell hybrid electric vehicles (FCHEV) are helping to advance the cause of environmental protection as a sustainable form of transportation. An effective energy management strategy (EMS) is crucial to reduce the usage cost of FCHEV and enhance SOC maintenance ability. This study establishes separate degradation models for fuel cells and lithium batteries, incorporating the decay factor of energy sources' lifetime into the EMS. To address the sparse reward problem during training, a novel energy management strategy algorithm based on deep reinforcement learning is proposed, which combines the twin delayed deep deterministic policy gradient (TD3) algorithm framework with learning rate annealing (AL) and hindsight prioritized experience replay (HPER) optimization methods, resulting in strong training performance. Experimental results demonstrate significant advantages of the EMS based on the HPER_AL_TD3 algorithm over traditional TD3-based approaches. The proposed EMS exhibits superior adaptability to various driving cycles, ensuring stable SOC levels and reducing the overall usage cost. This research aims to enhance the learning capability of EMS based on deep reinforcement learning and contribute to the promotion of FCHEV. • Several reinforcement learning features including a clipped double Q-network within the actor-critic framework, delayed policy updating and target policy smoothing regularization are utilized to formulate the EMS based on TD3. This represents one of the preliminary works for optimal control of energy management. • A rule-based supercapacitor controller is embedded into the EMS control loop as a dynamic output action regulator. It takes into account the charging and discharging characteristics of the main energy source, consisting of FCs and lithium battery packs, to eliminate frequent high-frequency power demand. • A hybrid experience replay method is proposed that combines the concept of both prioritized experience sampling and hindsight memory replay which not only considers the priority of the experience but also transforms failed experiences into successful ones. • The concept of learning rate annealing is employed, reducing the learning rate as the number of training iterations increases to expedite convergence towards optimal values. [ABSTRACT FROM AUTHOR]

The engine slipping start (ESS) benefits parallel hybrid electric vehicles from stable ignition and emission reduction. However, inappropriate coordination between the traction motor torque and clutch slipping torque during the ESS will lead to poor smoothness of the vehicle and failed start of the engine. Uncertainty in clutch slipping torque and change in driver demand torque bring tough challenges with sluggish convergence and intensive vehicle jerk in practice. To deal with this problem, a novel two-layer model reference adaptive controller (MRAC) which contains two parallel reference models is proposed to improve robustness and convergence rate simultaneously. On one hand, uncertainties of clutch slipping torque are divided into a low-frequency part and a high-frequency part, and adaptive laws based on the output feedback are designed contrapuntally to enhance robustness. On the other hand, two parallel reference models are designed to accelerate the tracking error convergence rate without changing the reference profiles, which is generated according to the driver demand torque in real time. To test the robustness and convergence rate, the proposed two-layer MRAC is compared with the classical MRAC and proportional–integral controller under the driving scenario with uncertain clutch slipping torque and abrupt change in driver demand torque. The sensitivity with different adaptive gains and low-frequency and high-frequency uncertainties in clutch slipping torque are examined. Finally, hardware-in-the-loop experiments are performed to verify the effectiveness of the proposed two-layer MRAC. [ABSTRACT FROM AUTHOR]

The power management strategy (PMS) is intimately linked to the fuel economy in the hybrid electric vehicle (HEV). In this paper, a hybrid power management scheme is proposed; it consists of an adaptive neuro-fuzzy inference method (ANFIS) and the equivalent consumption minimization technique (ECMS). Artificial intelligence (AI) is a key development for managing power among various energy sources. The hybrid power supply is an eco-acceptable system that includes a proton exchange membrane fuel cell (PEMFC) as a primary source and a battery bank and ultracapacitor as electric storage systems. The Haar wavelet transform method is used to calculate the stress σ on each energy source. The proposed model is developed in MATLAB/Simulink software. The simulation results show that the proposed scheme meets the power demand of a typical driving cycle, i.e., Highway Fuel Economy Test Cycle (HWFET) and Worldwide Harmonized Light Vehicles Test Procedures (WLTP—Class 3), for testing the vehicle performance, and assessment has been carried out for various PMS based on the consumption of hydrogen, overall efficiency, state of charge of ultracapacitors and batteries, stress on hybrid sources and stability of the DC bus. By combining ANFIS and ECMS, the consumption of hydrogen is minimized by 8.7% compared to the proportional integral (PI), state machine control (SMC), frequency decoupling fuzzy logic control (FDFLC), equivalent consumption minimization strategy (ECMS) and external energy minimization strategy (EEMS). [ABSTRACT FROM AUTHOR]

A novel fuzzy energy management strategy (EMS) based on improved Q-learning controller and genetic algorithm (GA) is proposed for the real-time power split between fuel cell and supercapacitor of hybrid electric vehicle (HEV). Different from driving pattern recognition–based method, Q-Learning controller takes actions by observing the driving states and compensates to fuzzy controller, that is, no need to know the driving pattern in advance. Aimed to prolong the fuel cell lifetime and decrease its energy consumption, the initial values of Q-table are optimized by GA. Moreover, to enhance the environment adaptation capability, the learning strategy of Q-learning controller is improved. Two adaptive energy management strategies have been compared, and simulation results show that current fluctuation can be reduced by 6.9% and 41.5%, and H2 consumption can be saved by 0.35% and 6.08%, respectively. Meanwhile, state of charge (SOC) of supercapacitor is sustained within the desired safe range. [ABSTRACT FROM AUTHOR]

In the controller development process, it is required to verify whether control functions operate normally, without fault or anomaly. Many control functions with complex system structures require various actual tests and test data analysis for verification. This paper presents an anomaly detection algorithm to verify control functions and to more efficiently analyze test data of a hybrid control unit (HCU) of a hybrid electric vehicle (HEV). The anomaly detection algorithm automatically detects anomaly of control functions from the test data, instead of manual labor by engineers. The examined target HCU control functions in this paper are the engine clutch engagement/disengagement control function and the engine start cooperative control function, which are crucial functions of transmission mounted electric drive (TMED) HEVs. A data-driven approach using a one-class support vector machine (SVM) is used to make it easy to apply various control functions. Actual vehicle test data is examined by the algorithm to verify the control functions at an actual vehicle level. The developed anomaly detection algorithm demonstrates feasibility and effectiveness of the proposed algorithm in detecting not only prior-known anomalies but also prior-unknown anomalies. Because other vehicle control functions have similar characteristics with the target functions of this paper, this algorithm is expected to be successfully applied to other HCU control functions. [ABSTRACT FROM AUTHOR]

This article proposes a method for compiling the load spectra of reducers for hybrid electric vehicles. Selecting typical working conditions for real vehicle data collection, the load data under each typical working condition were divided into five categories according to the state of the power source and the data were preprocessed. The optimal sample loads for compiling load spectra were obtained based on a multi-criteria decision-making method, rainflow counting for optimal sample loads was performed according to different power source output patterns, non-parametric extrapolation was performed to obtain the full-life two-dimensional load spectrum after dimensionality reduction, and a full-life eight-level programmed load spectrum that could be used for bench tests was obtained. Using the programmed load spectrum and the extracted sample load as the load input, a fatigue life prediction simulation of the reducer gear of a hybrid electric vehicle was carried out. The reducer gear fatigue life from the programmed load spectrum was compared to the gear fatigue life under actual load. The fatigue life of the reducer gear when the programmed load spectrum was used as the input was 1.412 × 103. When the actual load was used as the input load, the fatigue life of the reducer gear was 1.933 × 103. The relative error between the two is only 26%, which is in the normal range. The results show that the programmed load spectrum is effective and reliable and that the load spectrum compilation method provides a basis for accurately evaluating the reliability of the hybrid electric vehicle reducer. [ABSTRACT FROM AUTHOR]

The primary purpose of fuel cell hybrid electric vehicles (FCHEVs) is to tackle the challenge of environmental pollution associated with road transport. However, to benefit from the enormous advantages presented by FCHEVs, an appropriate energy management system (EMS) is necessary for effective power distribution between the fuel cell and the energy storage systems (ESSs). The past decade has brought a significant increase in the number of FCHEVs, with different EMSs having been implemented due to technology advancement and government policies. These methods are broadly categorised into rule-based EMS methods, machine learning methods and optimisationbased control methods. Therefore, this paper presents a systematic literature review on the different EMSs and strategies used in FCHEVs, with special focus on fuel cell/lithium-ion battery hybrid electric vehicles. The contribution of this study is that it presents a quantitative evaluation of the different EMSs selected by comparing and categorising them according to principles, technology maturity, advantages and disadvantages. In addition, considering the drawbacks of some EMSs, gaps were highlighted for future research to create the pathway for comprehensive emerging solutions. Therefore, the results of this paper will be beneficial to researchers and electric vehicle designers saddled with the responsibility of implementing an efficient EMS for vehicular applications. [ABSTRACT FROM AUTHOR]

In this paper, an observer-based type-2 fuzzy method is proposed for control and energy management strategy (EMS) of the hybrid energy storage system (HESS) which can be composed of the fuel cell (FC), battery (BA), and supercapacitor (SC). The objective and main contribution of the suggested strategy is to provide: 1) Appropriate tracking performance of power sources by an observer-based control method in the presence of noise and signal ripples. 2) An observer-based composite adaptive type-2 fuzzy (OCAT2F) to approximate the voltage of power sources. 3) A dynamical model of DC-bus to guarantee the stability of closed-loop system. 4) An intelligent EMS. To have a high-power supply, the proposed EMS includes two parts; a type-2 fuzzy logic control rule table (T2FLCRT), and an observer-based robust adaptive fuzzy type-2 fuzzy (ORAT2F). Furthermore, stability analyses of the closed-loop system are provided by the input-output linearization (I-OL) approach and based on the Lyapunov theorem. The simulation results of the proposed control scheme under MATLAB/Simulink indicate that the suggested strategy can provide a suitable control performance, and stability of the whole system is achieved. • An observer-based control method in the presence of noise and signal ripples. • Type-2 adaptive fuzzy estimation of power source voltages to reduce costs and sensors. • Improvement of the energy management strategy by type-2 fuzzy system. • Dynamical modeling of hybrid energy systems including fuel cell, battery, supercapacitor, and DC-bus system. [ABSTRACT FROM AUTHOR]

Polyphase permanent magnet synchronous motors (PMSM) model outstanding performance in comparison to three phase permanent magnet synchronous motors and this is possible because of the recent development in power electronics. Five phase permanent magnet synchronous motors have significant applications in aerospace and electric vehicles where highly accurate speed and torque control of the motor are a major requirement. This paper presents a detailed analysis of vector controlled five phase permanent magnet synchronous motors in terms of efficiency, power, speed and torque ripple analysis along with the power factor analysis at different speeds. The average torque, torque ripple, speed, speed ripple and power factor for same are calculated and analyzed. The analysis has been carried out through MATLAB Simulink Power Graphical User Interface. [ABSTRACT FROM AUTHOR]

Energy management strategy (EMS) is the core of hybrid electric vehicle (HEV) and directly determines the road performance of HEV. The transient characteristics of traffic information greatly affect the driving condition of the vehicle, making the demand torque or power of the vehicle change regularly with the traffic information. Therefore, based on traffic information fusion and power management to realize multi-objective optimization of EMS for HEV is of practical significance for reducing energy consumption and improving driving performance. The development status of EMS integrated with traffic information has been reviewed in this paper. First, EMS based on rules and optimizations and intelligent algorithms are discussed and compared. Second, the influential mechanism of traffic information on EMS is analyzed from driving condition prediction, multi-objective solution and energy management algorithm optimization. Finally, the main development directions of EMS integrated with traffic information are prospected from various aspects including traffic information acquisition, driving condition determination, energy management architecture, and online update mechanism optimization. [ABSTRACT FROM AUTHOR]

The paper aims to present an analysis of the component sizes of commercially available vehicles with electrified powertrains. The paper provides insight into how the powertrain components (an internal combustion engine, an electric motor and a battery) of mass production electrified vehicles are sized. The data of wide range of mass production electrified vehicles are collected and analyzed. Firstly, the main requirements to performance of a vehicle are described. The power values to meet the main performance requirements are calculated and compared to the real vehicle data. Based on the calculated values of the power requirements the minimum sizes of the powertrain components are derived. The paper highlights how the sizing methodologies, described in the research literature, are implemented in sizing the powertrain of the commercially available electrified vehicles. [ABSTRACT FROM AUTHOR]

Deep reinforcement learning (DRL) has been widely studied in the energy management of hybrid electric vehicles (HEVs) for its remarkable energy efficiency improvement compared to conventional methods. However, how to alleviate the time consumption of training a stable reinforcement learning agent still needs to be solved in real-world implementation. This study presents a human expert knowledge encoded 'warm start' method with the flexibility to change the neural network architecture. The expert knowledge is encoded in a decision tree which then initializes the weights and bias of the DRL neural network. Compared with another fixed architecture warm start method, the proposed FlexNet exhibits improved learning speed by 60.8 % and 88.8 % in action space 50 and 100, respectively. The energy consumption by the proposed FlexNet EMS method is 12.2 % and 6.4 % better than rule-based and equivalent consumption minimization strategy, respectively. This proposed warm start method can reduce learning time and increase energy efficiency in various energy management applications. • Expert knowledge is encoded in a decision tree to warm-start reinforcement learning. • The expert knowledge encode process allows flexible neural network architecture (FlexNet). • The proposed FlexNet reduces learning time by 60–80 % compared with existing method. • The proposed FlexNet reduces energy consumption compared with baseline methods. [ABSTRACT FROM AUTHOR]

Incorporating cold-start emissions from hybrid electric vehicles (HEVs) into on-road emission modeling is fundamental to accurately assess the environmental impacts aligned with their projected deployment. This study introduces a cold-start emission model, integrating travel time, dynamic emission rates, and cold-start durations by fuel type. This study further explores the anticipated spatiotemporal patterns of annual cold-start emissions. Findings indicate a reliance on gasoline for most on-road vehicles in the next decade, but vehicle electrification could reduce carbon monoxide (CO), hydrocarbon (HC), and nitrogen oxide (NO X) emissions by over 60% by 2035. Cold-start emissions will remain significant (67%-79% of CO, 59%-71% of HC, and 42%-54% of NO X emissions), especially on local roads. Cold-start emissions from HEVs will contribute to 14%, 25%, and 13% of total CO, HC, and NO X emissions in 2035, respectively. This study highlights the importance of accurate modeling in understanding the environmental benefits of vehicle electrification. [ABSTRACT FROM AUTHOR]

A novel hierarchical mode transition control (NHMTC) algorithm utilizing traffic information is investigated for realizing a superior dynamic and ride comfort performance of a connected hybrid electric vehicle (CHEV). The upper level includes the target vehicle velocity planning by model predictive control (MPC) considering less vehicle jerk based on the vehicle-to-infrastructure (V2I) information and the driving intention prediction by a type-2 fuzzy neural network (T2FNN) model using the planned target velocity. The lower level involves the automatic clutch system's prescribed performance fixed-time controller (PPFTC) design and the desired velocity of the clutch engagement calculated by the predicted driving intention to improve the clutch position trajectory's tracking accuracy and stability under the actual mode transition process. The comparison results with existing schemes under a co-simulation environment demonstrate the advantage and effectiveness of the designed NHMTC method. • Model predictive control considering less vehicle jerk is designed to plan the target vehicle. • Type-2 fuzzy neural network model is utilized to predict the driving intention. • Prescribed performance fixed-time controller achieves the satisfactory clutch control performances. [ABSTRACT FROM AUTHOR]