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Road gradients not only affect the actual performance of control strategies but also impact battery life due to the drastic changes in power demands. To balance battery degradation with fuel economy using gradient information, this study proposes a gradient-aware trade-off control strategy. Initially, a vehicle dynamics model and a battery degradation model are established. Based on the characteristics of known road information and remaining driving distance, state of charge planning of the battery is conducted. Subsequently, the Non-dominated Sorting Genetic Algorithm-II is applied for bi-objective optimization, yielding a set of Pareto solutions that represent different levels of energy consumption and battery degradation. Thereafter, by introducing a real-time battery degradation severity factor, an optimized bias coefficient is obtained, which adjusts in accordance with the gradient changes. Through the optimization of the bias line, the optimal bias solution set under different working conditions is determined, achieving the optimal control for power system. The fuel economy of the proposed strategy is improved by 6.8% relative to the mileage adaptive Equivalent Consumption Minimization Strategy, and the battery degradation inhibition is improved by 9.3%. After real-world conditions validation, the proposed strategy demonstrates good performance in both economic efficiency and battery life. [Display omitted] • The global optimal SoC is planned according to the characteristic of the energy required by the slope. • Battery degradation and energy consumption are considered for generating Pareto optimal solutions. • Optimized bias lines are introduced to adaptively trade-off and select the optimal solutions for different conditions. [ABSTRACT FROM AUTHOR]

A genetically optimized fuzzy control algorithm is used to realize the parallel hybrid system's real-time energy distribution between the engine and the electric motor. This paper first adopts fuzzy control to improve the robustness and real-time performance of the entire system. Then, a membership function optimization method based on a genetic algorithm is proposed by simulating the working state of CYCUDDS. Simulation experiments show that compared with unoptimized fuzzy control, the improved fuzzy controller can improve the vehicle's fuel economy and prevent continuous battery discharge, thereby improving battery endurance. [ABSTRACT FROM AUTHOR]

In order to maintain the battery SOC, the fuel cell power will fluctuate dramatically, as well as frequent start-stop, which will greatly increase the life attenuation of the fuel cell and reduce the durability. An optimization-based energy management strategy with a real-time model predictive control and pontryagin's maximum principle for FCHEV is proposed in this paper, both the fuel economy and the fuel cell durability are considered in the optimization. A novel model predictive control is studied to achieve energy distribution. After the calculation of predicted speed sequence through back propagation neural network, pontryagin's maximum principle is introduced to solve the optimal control problem in each prediction horizon and obtain the ideal control strategy. In addition, the fuel cell degradation model is introduced in the modeling process, the minimum power point of the fuel cell system is designed to improve the fuel economy and durability of the fuel cell. Compared with the rule-based strategy, the proposed MPC strategy has better performance to reduce the total equivalent hydrogen consumption, which can save up to 8.44% in the test case of the mid-size fuel cell passenger car while maintaining the stability of the battery's state of charge. [ABSTRACT FROM AUTHOR]

Renewable energy sources are now being focused on for usage in electric vehicles. Although majority of electric vehicles run solely on batteries, they have eliminated local emissions but not pollution. To tackle this issue, this research looks into the performance and power management aspects of an electric vehicle's hybrid power system, with a particular emphasis on the interactions between fuel cells, batteries, and photovoltaic (PV) cells. Stable operation is ensured via a bi-directional buck-boost converter, which maintains the voltage from all sources around reference 430 V. Initially, the fuel cell supplies electricity under low light conditions. The PV system and battery take over when the irradiance surpasses 100 W/m2, with the battery first draining and then charging in tandem with the increase in irradiance. 1750 RPM is the constant motor speed that is reached after making the required torque adjustments. A model full electric vehicle with multiple sources has been proposed with an efficient power management algorithm. When the battery is in stationary mode and the state of charge (SOC) is less than 100%, extra solar power is used to charge the battery. The model also takes into consideration using the car for entertainment or gadget charging when the ignition is off. The biogas output can be regulated using an inferential control technique using artificial neural network (ANN) based on the fuel demand or consumption. The system also includes anaerobic digestion of organic waste to provide hydrogen for the fuel cell sustainably from biogas. Power, current, SOC profiles, motor performance graphs, and other validations show that the extensive simulations exhibit efficient power management and a strong model for hybrid electric car power systems. The model has been simulated in the MATLAB Simulink environment and has been tested under realistic conditions. • Proposed new powertrain strategy including PV-battery-PEMFC. • Analysed the heuristic power management for the proposed powertrain strategy. • Utilized biogas to produce Hydrogen for the Vehicle. • Utilized ANN for inferential control of hydrogen generation from biogas plant. [ABSTRACT FROM AUTHOR]

Lithium-ion (Li-ion) battery has played a key role for the development of electric vehicle (EV) at present, while the Li-ion batteries in the market come from different manufactures. Verifying the performance of the battery management system (BMS) for various battery chemistries is a complex undertaking. This paper proposes a high-fidelity Li-ion battery emulator for EV applications. The emulator utilizes a battery model parameterized by a series of performance tests and a special-designed hardware platform. A three-order battery equivalent circuit model (ECM) is selected to provide the voltage and current reference signal in the processor. Subsequently, the hardware generates the high voltage and current signal in accordance with the reference. To ensure the high accuracy of the battery ECM, a 37 Ah nickel manganese cobalt (NMC) battery was selected for testing under both charge and discharge conditions, as well as across a temperature range of − 30℃ to 45℃. The battery emulator is verified on charge and discharge mode for both accuracy and dynamic performance validations. [ABSTRACT FROM AUTHOR]

The collaborative optimization of speed and energy management for fuel cell hybrid electric vehicles (FCHEVs) in car-following scenario is an important and effective means to achieve vehicle energy conservation. In view of the unclear coupling relationship between dual energy source degradation and energy consumption in existing strategies, and the difficulty of considering both global optimization and real-time performance, this paper proposes a Stackelberg game based FCHEV following energy management strategy. Firstly, models for energy consumption and performance degradation of fuel cells/lithium batteries are established and incorporated into a unified dimensional comprehensive vehicle cost function. Secondly, a hierarchical decoupling based energy management strategy for vehicle following is proposed to achieve decoupling control of vehicle following speed and power distribution. Thirdly, considering safety, comfort, fuel economy, and energy durability, a bi-level programming model corresponding to the car-following control layer and energy management layer is established. Based on the Stackelberg game theory, a bi-level differential genetic algorithm is designed to offline optimize the core parameters of the strategy. Finally, simulation and experimental results show that compared to the model predictive control method, this method can reduce average vehicle spacing error by 37.7%, average impact by 2.4%, equivalent hydrogen consumption by 9.3%, and energy degradation cost by 13.9%, achieving a balance between optimized performance and real-time performance. [ABSTRACT FROM AUTHOR]

This research paper introduces the Double Stator (DS) Hybrid Excitation (HE) Halbach Permanent Magnet (HPM) Flux Switching (FS) machine. The machine construction and its optimization specifically designed for electric vehicle (EV)/hybrid electric vehicle (HEV) traction applications are investigated. The optimization using a multi-objective Genetic Algorithm is conducted following a sensitivity analysis-based identification of key optimization parameters and constraints. The finite element results are compared with the performance of a state-of-the-art benchmark FSPM machine having identical PM volume and winding current densities. The proposed design is shown to outperform the benchmark with 16.2% increase in back-electromotive force and 14.7% reduction in cogging torque. Furthermore, the average torque is improved at flux-enhancing operation by 20.8%, and the torque ripple is reduced by 9.9%. Notably, the proposed machine also is capable of flux regulation thereby having the ability to operate in a wide speed range. A detailed explanation of the reasons for the significant improvements in the proposed machine structure is provided to offer a comprehensive understanding of its rationale. These research findings indicate that this innovative DS-HE-HPM-FS machine can enhance the performance of EVs and HEVs. [ABSTRACT FROM AUTHOR]

Significant resources and diligent research have been dedicated to the investigation and enhancement of energy storage devices utilising hydrogen, lithium, or sodium. Efforts of this nature strive to offer sustainable energy storage solutions that are both dependable and environmentally friendly. This report presents a review a thorough an examination of the aforementioned technologies, with a particular emphasis on cost-effectiveness and State of Charge (SoC) administration. Furthermore, it comprehensively assesses their specific advantages and downsides. Sodium-based systems, such as sodium-sulfur batteries, exhibit remarkable stability and efficiency in sustaining desired charge levels, starting from the control of SoC. Lithium-based alternatives, such as Lithium-ion batteries (LIBs) have gained extensive usage adopted in several industries because to their enhanced energy density and improved SoC accuracy. The complexity associated with SoC is a challenge for hydrogen-based systems, including hydrogen fuel cells, due to their dependence on gas storage and conversion techniques. The assessment places significant emphasis on cost analysis, which evaluates the economic feasibility of each storage method. Sodium-based systems, known for their well-established technology and ample availability of raw materials, frequently present a financially viable option, especially for large-scale grid implementations. The pricing of lithium-based systems, particularly in the domains of portable devices and electric cars, is competitive because to their quick improvements and decreasing costs. Hydrogen-based systems, despite their initial expensive capital expenditure, exhibit potential through continuous advancements and the expansion of production capacity. [ABSTRACT FROM AUTHOR]

Battery electric vehicles (BEVs) are widely considered a pathway to achieve low carbon mobility. BEVs emit zero emissions from the tailpipe, but their life cycle carbon reduction compared to gasoline vehicles varies based on primary energy sources, electricity generation, and use efficiency. The Middle East and North Africa (MENA) region is an area rich in fossil fuels, meriting a detailed comparison between the emissions from BEV and other powertrains. We developed a MENA‐specific life cycle model that estimates well‐to‐wheel (WTW) greenhouse gas (GHG) emissions from passenger transport with internal combustion engine vehicles (ICEVs), hybrid electric vehicles (HEVs), plug‐in hybrid electric vehicles, and BEVs. MENA's average WTW GHG emissions for all supply chain steps including combustion emissions from vehicle operation are 767 g/kWh and 84 g CO2eq/MJ for electricity and gasoline, respectively, but are highly variable due to heterogeneity in upstream supply chains. The use of hybrid gasoline ICEVs provides the largest emission reduction opportunity for existing vehicle fleets in 9 of the 16 MENA countries. For these nine countries, replacing gasoline ICEVs with HEVs could, on average, reduce country‐level life cycle GHG emissions by 47%. There is a similar emission reduction opportunity for 14 of the 16 MENA countries when normalizing vehicle efficiencies irrespective of the powertrain shares and other trends in existing vehicle fleets. Future scenario analysis shows that BEVs would have the lowest WTW GHG emissions among all powertrains in most MENA countries only if significantly reduced electricity transmission losses and cleaner grid mix are realized, although a high cost of infrastructure developments is expected. [ABSTRACT FROM AUTHOR]

This paper presents a capacity planning framework for a microgrid based on renewable energy sources and supported by a hybrid battery energy storage system which is composed of three different battery types, including lithium-ion (Li-ion), lead acid (LA), and second-life Li-ion batteries for supplying electric vehicle (EV) charging stations. The objective of this framework is to determine the optimal size for the wind generation systems, PV generation systems, and hybrid battery energy storage systems (HBESS) with the least cost. The framework is formulated as a mixed integer linear programming (MILP) problem, which incorporates constraints for battery ageing and the amount of unmet load for each year. The system uncertainties are managed by conducting the studies for various scenarios, generated and reduced by generative adversarial networks (GAN) and the k-means clustering algorithm for wind speed, global horizontal irradiation, and EV charging load. The studies are conducted for three levels of unmet load, and the outputs are compared for these reliability levels. The results indicate that the cost of hybrid energy storage is lower than individual battery technologies (21% compared to Li-ion, 4.6% compared to LA, and 6% compared to second-life Li-ion batteries). Additionally, by using HBESS, the capacity fade of LA batteries is decreased (for the unmet load levels of 0, 1%, 5%, 4.2%, 6.1%, and 9.7%, respectively), and the replacement of the system is deferred proportional to the degradation reduction. [ABSTRACT FROM AUTHOR]

The permanent magnet synchronous motor (PMSM) avoids commutation-related torque ripples and produces smooth torque. Its great handling capacity and better efficiency make it an excellent choice for high-demand applications. A typical PM motor drive fed with pulse width-modulated voltages may cause the motor insulation to break down if rapid voltages (dv/dt) occur across the motor terminals. Applying variable voltage with low dv/dt and implementing multiple inverter topology can solve this issue. Multilevel converters have minimal switching losses, better power quality and the ability to operate at both fundamental and higher switching frequencies. In this study, a three phase stacked multilevel inverter-based FOC driven PMSM drive design is proposed. Here, the neutral point is a capacitor intermediate point on DC side, where current is naturally balanced throughout a switching cycle. This makes it possible to use downstream batteries and even lower voltage equipment, greatly increasing efficiency, improved performance and smother control at low speed. Therefore, direct-battery-driven electric vehicles will be able to use this. A 33 level inverter-based PMSM drive was used to implement the FOC, and the simulation results were used to validate it. The Matlab/Simulink tool is used to simulate the entire system. [ABSTRACT FROM AUTHOR]

Summary: With the advent and popularity of electric vehicles (EVs), the intelligent transportation system has adopted them as an alternative to fossil fuel or gasoline vehicles owing to their benefits of reduced greenhouse gas emissions. However, it becomes critical to schedule the vast EVs at the charging station (CS) efficiently while maintaining security, privacy, and optimality during the energy trading. Therefore, we propose a blockchain and zero‐sum game theory‐based energy trading scheme, that is, DEVS, for optimal and secure EV charging at the CS. We incorporate the Interplanetary File System with the blockchain to ameliorate the cost‐efficiency and reliability of EV energy trading. Furthermore, we implement the zero‐sum game theory between players EV and CS to yield an optimal payoff based on pure and mixed strategies during the energy trading procedure. Moreover, we deploy and execute the smart contract (SC) of the blockchain‐based proposed DEVS scheme in the Remix integrated development environment to secure EV energy trading transactions. Additionally, the security of the proposed DEVS scheme is checked and analyzed using a fuzzing‐based Echidna tool for protected energy trading. Finally, the performance evaluation of the proposed DEVS scheme is analyzed using various standard metrics such as profit comparison based on strategies, energy price comparison, CS profit comparison for pure strategy (based on arrival time), convergence comparison, and profit (EV and CS) at saddle point and mixed strategy to avail preserved and optimal energy trading for EVs. [ABSTRACT FROM AUTHOR]

A hybrid technique is proposed for electric vehicles (EVs) or hybrid electric vehicles (HEVs) with a battery thermal management system (BTMS). The proposed hybrid method is the combined execution of the Pelican Optimization Algorithm (POA) and Firebug Swarm Optimization (FSO). The hunting behavior of pelicans is improved with the help of the FSO technique; hence, it is named the POA-FSO strategy. Here, a battery of 36 lithium-ion (Li-ion) cells is considered. The proposed method of analyzing battery thermal performance analyzed factors like heat flux from the battery cell to the passage spacing size, cooling air and the cooling air's mass flow rate (MFR). The BTMS performance is studied under maximum battery unit temperature, pressure drop and temperature uniformity. The proposed method enhances the coolant passage spacing and decreases the temperature difference in the battery cells. The proposed approach is executed on the MATLAB platform, and its performance is compared with existing approaches. From the results, it is concluded that the MFR maximizes, and a nonuniform distribution of the MFR of the passage occurs. By using the proposed approach, the maximal temperature change of cooling air among the passages is 2.0 K, the maximal temperature change between the battery cells is 7.5 K and a pressure drop of 228.03 Pa is obtained. [ABSTRACT FROM AUTHOR]

The integration of supercapacitors as hybrid energy storage systems in electric vehicles has attracted the attention of many researchers and has been considered as a promising solution. Bidirectional DC/DC converters (BDDCs) play a fundamental role in HESS, as they manage the power flow by controlling currents and regulating the DC bus voltage. However, they encounter the challenge of uncertainties and high fluctuation power loads, necessitating the fast dynamics, stability, and high robustness of the controller. This paper proposes a novel hybrid proportional–integral and backstepping cascade controller to regulate the DC‐bus voltage under uncertainties and load variations, and to control the current references of the on‐boarded sources. To confirm the asymptotic stability of the whole system, a nonlinear stability analysis is conducted using the Lyapunov theorem. A power management strategy is applied to distribute the power loads and generate reference currents for the BDDCs controller. Simulations results under various driving cycles using MATLAB/Simulink demonstrate the superiority of the proposed controller compared to conventional proportional–integral and backstepping controllers. A real‐time controller‐hardware‐in‐the‐loop test bench is developed to validate the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

With a single-switched DC-DC converter, a hybrid approach is proposed for a fast-charging (FC) dc to dc converter with the controller for an electric vehicle charging station (EVCS) that combines solar photovoltaic (PV), fuel cells (FC), and a battery energy storage system (BESS). The proposed hybrid technique integrates a radial-basis-function-neural-network (RBFNN) and a student-psychology-optimization-algorithm (SPOA); commonly known as the RBFNN-SPOA technique. The PV-FC hybrid electric vehicle systems, like controllers, electric vehicle, and the vehicle’s power source, are displayed. The proposed converter architectures have less voltage waveform changes, a low voltage stress on semiconductor power sources, and a high-voltage ratio of conversion. The proposed method is to use FC successfully at battery charge states and varied radiation levels. The FC in the controller of SPOA may change the inputs and outputs depending on the battery state of charge (SoC) and PV irradiation. Similarly, the needed input-torque for the motor is forecasted by the recalling recurrent neural network controller using several manually produced torque signals. The performance of the proposed technique is evaluated in MATLAB and is compared to ant colony optimization (ACO), crow optimization algorithm (COA), and fuzzy logic controller (FLC) approaches. According to the results, the efficiency of the proposed technique is 99.17%. [ABSTRACT FROM AUTHOR]

In recent years, the adoption of alternative fuels and hybrid vehicles has necessitated radical changes to meet the energy needs of the automotive sector. Hybrid electric vehicles are used as a perfect step between traditional fossil fueled internal combustion engines and fully electric vehicles. Deeply related to this, complicated industry and manufacturers need simulation programs to reduce both the cost of test sets and to use time with the highest efficiency. Before the production, especially in automotive sector, the simulation and prototyping procedure is playing an indispensable role. For this study, the AVL-Cruise simulation tool was chosen. Due to its capability to keep up with evolving technological structures, this program can be easily implemented in hybrid vehicle simulations. In this study, a hybrid engine model was created based on a 1.5 L, four-cylinder gasoline engine and a 10 kW electric motor. Energy analysis, based on the variations in the CVT and AMT transmissions of a hybrid motor, was conducted under three different driving cycles and compared in detail. Additionally, energetic balance like exergy is executed for this model. Sankey diagrams were employed to illustrate the differences and energy consumptions of the vehicle based on the selected modeling criteria. As a result, the CVT is slightly better behaviors than AMT both fuel consumption (2.5–4%), emission reducing (1.5–4.6%) and energetic balances (3.6–5%). • Hybrid vehicles energy analyses are determined with Sankey diagrams. • Effects of CVT and AMT transmission to HEVs energetic outputs were compared. • Effects of various driving cycles on HEV components were discussed detailed. • CVT is slightly better than AMT with reducing the fuel consumption and emissions. • For this study, CVT is more energetic efficiency with 5% improvement vs AMT. [ABSTRACT FROM AUTHOR]

Hybrid supercapacitors (HSCs) bridge the unique advantages of batteries and capacitors and are considered promising energy storage devices for hybrid vehicles and other electronic gadgets. Lithium‐ion capacitors (LICs) have attained particular interest due to their higher energy and power density than traditional supercapacitor devices. The limited voltage window and the deterioration of anode materials upsurged the demand for efficient and stable electrode materials. Two‐dimensional (2D) molybdenum sulfide (MoS2) is a promising candidate for developing efficient and durable LICs due to its wide lithiation potential and unique layer structure, enhancing charge storage efficiency. Modifying the extrinsic features, such as the dimensions and shape at the nanoscale, serves as a potential path to overcome the sluggish kinetics observed in the LICs. Herein, the MoS2 nanoflowers have been synthesized through a hydrothermal route. The developed LIC exhibited a specific capacitance of 202.4 F g−1 at 0.25 A g−1 and capacitance retention of >90 % over 5,000 cycles. Using an ether electrolyte improved the voltage window (2.0 V) and enhanced the stability performance. The ex‐situ material characterization after the stability test reveals that the storage mechanism in MoS2‐LICs is not diffusion‐controlled. Instead, the fast surface redox reactions, especially intercalation/deintercalation of ions, are more prominent for charge storage. [ABSTRACT FROM AUTHOR]

The ambient temperature affects the operation of different powertrain systems, including electric, hybrid electric, and internal combustion engines. This study investigated the effect of the ambient temperature on the energy consumption and CO2 emissions of a plug-in hybrid electric vehicle running in different powertrain modes. The vehicle was driven for 4150 km following a selected route 199 times in different powertrain modes and in different ambient temperatures ranging from −24 °C to 32 °C. Instantaneous and cumulative fuel consumptions were measured using a fuel flow meter, and the battery energy usage was determined from the vehicle telematics during each test. The total energy consumption and total CO2 emissions were affected by the ambient temperature in all powertrain modes, including electric, hybrid electric (charge-depleting and charge-sustaining), and conventional internal combustion engine modes. The highest increase was associated with the charge-depleting hybrid electric mode, with 350% and 290% increases in energy consumption and CO2 emissions when the ambient temperature dropped from 29 °C to −24 °C. The conventional internal combustion engine mode was the least affected, with only 7% and 8% increased in energy consumption and CO2 emissions, respectively. [ABSTRACT FROM AUTHOR]

Tracked vehicles are integral for maneuvering diverse terrains, with hybrid propulsion systems offering potential benefits in terms of fuel efficiency and performance. However, research in hybrid electric tracked vehicles remains limited, thus necessitating a comprehensive analysis to maximize their advantages. This study presents a numerical analysis focusing on optimizing hybridization in speed-coupled parallel hybrid electric powertrains for tracked vehicles. A dynamic programming algorithm and custom drive cycle are utilized to determine optimal hybridization factors and assess parameter sensitivities. The study reveals that a hybridization factor of 0.48 is optimal for speed-coupled parallel configurations. Furthermore, the sensitivity analysis underscores the substantial impact of factors such as the engine displacement and bore-to-stroke ratio on the fuel economy, with a 10% change in these parameters potentially influencing the fuel economy by up to 2%, thus emphasizing the importance of thorough consideration during powertrain sizing. Parallel hybrid configurations exhibit considerable potential for tracked vehicles, thus highlighting the viability of choosing them over series configurations. [ABSTRACT FROM AUTHOR]

Hybrid electric vehicles (HEVs) are perceived as a first step toward the future of sustainable transport. Of course, battery electric vehicles (EVs) are currently ideal for what is wanted in the future of transport. However, the lack of infrastructure for these vehicles makes many potential users choose hybrid vehicles. This paper presents an analysis of the influence of hybrid vehicle engines and electric motors on their performance. Three engines with slightly different power levels and similar characteristics were considered for the configured models. Additionally, two electric motors with very different power levels, but a very close maximum continuous torque were used in our models. One was an induction motor and the other was a permanent magnet synchronous motor. The ADVISOR software was used for vehicle configuration and simulation. Series and parallel hybrid vehicles were considered. The main dynamic performances and the fuel consumption rates of the two driving cycles were compared for the configured models. Three conventional models with the same engines used in HEVs were also simulated for reference. The results highlight that, in the case of HEVs, the choice of a combination engine/electric motor is crucial for obtaining the best compromise between a dynamic performance and a low fuel consumption and, implicitly, a low negative impact on the environment. [ABSTRACT FROM AUTHOR]

The design and integration of intelligent energy management systems in hybrid electric vehicle (EV) charging stations, leveraging industry 4.0 and renewable energy sources, is crucial for advancing sustainability, efficiency, and technological development. The innovative hybrid EV charging station described in this study uses a combination of fuel cells, batteries, and solar panels that run at 14 amps a piece at 240 volts. The system consists of five essential components that work together to transfer power wirelessly: an EV battery bank, a boost converter, an HF inverter, transfer coils, and a power supply. Two crucial phases make up the optimization process. In phase 1, the boost converter's maximum power point is tracked and optimized to generate the most power possible by varying the duty cycle between 10% and 90%. In phase 2, the HF uses a class ϕ2 inverter at 30 MHz to synchronize with the resonant frequency of wireless power transfer coils. Zero-voltage switching is used by a digital signal processor card to carry out control for effective operations. By utilizing hybrid sources to optimize power transmission, this design improves the sustainability of EV charging options. [ABSTRACT FROM AUTHOR]

The global energy crisis has raised environmental concerns, leading governments to invest in renewable energy and address electric vehicle (EV) and hybrid EV (HEV) difficulties. One of the main challenges is the interface between the energy storage system and the DC-link of the three-phase inverter. For this aim, an improved voltage gain bidirectional DC/DC converter based on a voltage multiplier cell (VMC) is proposed in this paper, which acts like a high boost converter when the energy storage system is powering the EV and a buck converter when regenerative braking is required. The proposed bidirectional converter has advantages like low device account, simple structure, reduced switching power losses, high voltage gain, common ground, and increased efficiency. An output voltage of 10 times the input voltage in boost mode is provided by the proposed converter. Also, the proposed converter can generate a voltage 0.26 times lower than the input voltage in buck mode. The operating principle and comparative analysis with other existing converters are presented. Moreover, the efficiency calculation is discussed by assuming the non-ideal parameters. The proposed converter has a maximum efficiency of 97.78%. Finally, a 500 W simulation prototype is implemented in PSIM software to confirm the theoretical analysis. [ABSTRACT FROM AUTHOR]

The booming demand for electric vehicles, driven by the pursuit of enhanced efficiency, faces significant hurdles attributed to the costly integration of rare earth magnets like NdFeB. M-type strontium ferrite emerges as a promising alternative owing to its economic viability and exceptional magnetic characteristics, rendering it suitable for electric vehicle motor applications. This research focuses on the synthesis and characterization of SrFe 12 O 19 and Sr 1-x Pr x Fe 10 Al 2 O 19 (where x = 0.00 to 0.20) hexaferrites, with praseodymium (Pr) substitution. Magnetic particles were synthesized utilizing the sol-gel auto-combustion technique, employing a calcination temperature of 1100 °C for a duration of 4 h. Structural and magnetic studies of aluminium-praseodymium (Al–Pr) doped samples are performed using X-ray diffraction (XRD), scanning electron microscopy (SEM), and vibrating-sample magnetometry (VSM). The structural analysis reveals a decrease in both particle size from 42 nm to 31.76 nm and lattice parameter with increasing doping concentration. Magnetic analysis yields crucial insights: Al3+ doping induces a substantial rise in coercivity from 5500 Oe to 10,500 Oe which is quite comparable to NdFeB, while Pr doping shows a subtle impact, enhancing remanence from 17 emu/g to 23.05 emu/g. These findings underscore the potential for optimizing magnetic properties through meticulous control of dopant concentrations in M-type hexaferrites, thereby positioning them as promising substitutes for rare earth magnets. [ABSTRACT FROM AUTHOR]

The majority of uses for UAVs are in precision agriculture, cinematography, aerial surveillance, military applications, disaster relief, construction inspection, and other applications. Nevertheless, a precise localization method is needed to accomplish these tasks on their own. This study concentrated on autonomous ground vision-guided landing of a fixed-wing unmanned aerial vehicle (UAV) in situations where GNSS is unavailable. To improve image detection and accuracy for UAV auto-landing, optimum Deep Learning (DL) approaches would be used in the design and implementation. Therefore, this research presents a DL-based auto-landing scheme for UAVs. Once the flying vehicle's motion consistency is confirmed to be unsatisfactory, an optimized Long Term Short Memory (LSTM) would be constructed to improve detection accuracy. To adjust the weight of the LSTM, an RDSBWO (Red Deer struck Black widow optimization) technique should be employed. The normal BWO and RDA are conceptually combined to create the RDSBWO model. The proposed DL concept uses image detection and spatial placement to automatically improve the inaccurate coordinates within a given range. Moreover, object tracking and localization were accomplished using Extended Kalman Filter (EKF-based) object localization. Comparing the adopted established LSTM + RDSBWO scheme to existing approaches such as LSTM + BWO (0.36), LSTM + RDA (0.439), LSTM + LA (0.489), LSTM + BOA (0.690), LSTM + AOA (0.489), LSTM + PRO (0.389), GRU (0.489), DBN (~ 0.610), and LSTM (~ 0.690), it achieves lower MSE (0.235) with better performance for the number of frames 4. [ABSTRACT FROM AUTHOR]

Hybrid electric vehicles are increasingly recognized as a promising solution for reducing carbon dioxide emissions in the transportation sector. The current study examines the nexus between innovation in hybrid electric vehicle‐related technologies and transportation sector‐based carbon dioxide emissions from 1990 to 2019 using a variety of econometric techniques such as the structural breaks unit root test, Maki cointegration test, fully modified ordinary least squares, and dynamic ordinary least squares. The results show that innovation in hybrid electric vehicle‐related technologies, renewable energy consumption, information and communication technologies, and international collaboration in technology development could help reduce transportation sector‐based carbon emissions levels. By contrast, the findings indicate that gross domestic product, trade openness, and financial development have a positive nexus with transportation sector‐based carbon emissions. Higher trade openness encourages economic expansion, and foreign trade may lead to more manufacturing, shipping, and transportation sector‐based carbon emissions. Similarly, when gross domestic product and financial development rise, the need for highly energy‐intensive items and services increases transportation sector‐based carbon emissions. To minimize Japan's transportation sector‐based carbon emissions, governments should emphasize innovation and adoption of hybrid electric vehicle‐related technologies, boost renewable energy consumption, and prohibit emissions‐intensive imports and exports. [ABSTRACT FROM AUTHOR]

Energy storage systems (ESS) are indispensable in daily life and have two types that can offer high energy and high power density. Hybrid energy storage systems (HESS) are obtained by combining two or more energy storage units to benefit both types. Energy management systems (EMS) are essential in ensuring the reliability, high performance, and efficiency of HESS. One of the most critical parameters for EMS is the battery state of health (SoH). Continuous monitoring of the SoH provides essential information regarding the system status, detects unusual performance degradations and enables planned maintenance, prevents system failures, helps keep efficiency at a consistently high level, and helps ensure energy security by reducing downtime. The SoH parameter depends on parameters such as depth of discharge (DoD), charge and discharge rate (C-rate), and temperature. Optimal values of these parameters directly affect the lifetime and operating performance of the battery. The proposed adaptive energy management system (AEMS) uses the SoH parameter of the battery as the control input. It provides optimal control by dynamically updating the C-rate and DoD parameters. In addition, the supercapacitor integrated into the system with filter-based power separation prevents deep discharge of the batteries. Under the proposed AEMS control, HESS has been observed to generate 6.31% more energy than a system relying solely on batteries. This beneficial relationship between supercapacitors and batteries efficiently managed by AEMS opens new possibilities for advanced energy management in applications ranging from electric vehicles to renewable energy storage systems. [ABSTRACT FROM AUTHOR]

With the increase in the amount of actual operating data on electric vehicles, how to analyze and process useful information from existing battery charging and discharging data and apply it to subsequent state estimation is worthy of in-depth thinking and practice by researchers. This article proposes a collaborative estimation architecture for SOC and SOH based on the 1RC equivalent circuit model, recursive least squares, and adaptive extended Kalman filtering algorithms (AEKF), which combine offline data processing with online applications. By applying offline data processing, OCV–SOC polynomial fitting and average polarization resistance were determined, which reduced the time required for basic data measurement and improved the accuracy of model parameter identification, while a recursive estimation combining micro- and macro-time-scales of AEKF was used for the online real-time estimation of the SOC and actual available capacity of batteries, in order to eliminate interference from measurement and process noise. The results of the simulated and experimental data validation indicate that the proposed algorithm is applicable to the lithium-ion batteries studied in this paper, the average SOC deviation is less than 1.5%, the maximum deviation is less than 2.02%, and the SOH estimation deviation is less than 1% under different driving conditions in the multi-temperature range. This study lays the foundation for further utilizing offline data and improving SOC and SOH collaborative estimation algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a review of the most common power converters and torque ripple minimisation approaches for switched reluctance motors (SRMs). Unlike conventional three-phase AC motors, namely squirrel cage induction motors and permanent magnet synchronous motors, which require a typical three-phase inverter for operation, the switched reluctance motor requires a different topology power converter for reliable and efficient operation. In addition, due to the non-linear, discrete nature of SRM torque production, torque ripple is severely pronounced, which is undesirable in servo applications like electric vehicles. Hence, deploying a proper torque control function for smooth and quiet motor operation is crucial. This paper sheds light over the most popular SRM power converters as well as torque ripple minimisation methods, and it suggests an optimal SRM drive topology for EV applications. [ABSTRACT FROM AUTHOR]

The aim of the research presented in the article is to use the Metalog family of probability distributions to assess the technical condition of traction battery packs from electric and hybrid vehicles. The description of the research object, which is a battery pack from a hybrid vehicle, will be provided. Then, a system for controlled charging and discharging of individual cells in a battery pack will be reviewed. It is an essential diagnostic and research device used to determine the capacity of individual cells. The capacity values of all battery cells will then be analyzed using the Metalog probability distribution family. The use of this tool allows us to determine the Probability Density Function for the entire battery pack. Based on this, the diagnostician is able to assess the technical condition of the tested package and decide on its further fate. It can be intended for repair, employed as a stationary energy storage facility, or used for disposal. The algorithm for assessing the technical condition of traction batteries proposed by the authors can be used in all battery packs regardless of the type of cells used and their energy capacity. [ABSTRACT FROM AUTHOR]

Accurate forecasting of electric vehicle (EV) load is essential for grid stability and energy management. EV load forecasting is influenced by multiple factors. At present, the load forecasting model for EVs mainly uses collected sample data to build a data‐driven model. But these algorithms need to collect all the data together to train the model, ignoring the privacy of each data collection source. In a competitive market environment, each device service provider is not willing to share the sample data they store. Aiming at this problem, this paper proposes an EV load diagnosis algorithm considering data privacy. Firstly, a convolutional neural network with dual attention mechanism is constructed as the basic time series forecasting model. The association rule algorithm is used to select weather data with strong associations as the inputs of the model. Each service provider uses local data to perform deep learning network. All models are then trained using a federated learning framework. During the entire training process, historical data is stored locally, and only model parameter information is shared and interacted; thus data privacy is protected. Finally, the validity of the algorithm in this paper is verified by using real collected EV load data. [ABSTRACT FROM AUTHOR]

Recently, the transition from conventional to renewable energy sources (RESs), from internal combustion engine vehicles to electric vehicles (EVs), and from the main grid to microgrids (MGs) are essential goals to both reduce greenhouse gas emissions and ensure the stability of power systems. However, the transitions cause new concerns in the grid including technical challenges and financial viability. This study examines the RES-based MG under realistic conditions considering the uncertainty in fleet size of EVs, emergency generator capacity, solar irradiation, and wind speed in island mode. The work aims to provide effective solutions including conventional methods alongside today's trend namely doubly fed electrical generators (DFIG), vehicle-to-grid mechanism (V2G), maximum power point tracking controller (MPPT), voltage source inverter (VSI) with high switching frequency for technical challenges, and virtual synchronous generator (VSG) mechanism for financial viability. The paper provides a guide for resizing the emergency generators capacity depending on system instability. The observations verify that the control mechanisms reinforce the system to remain stable by decreasing the range of frequency fluctuation from 3.1 to <0.05 Hz, the peak point of frequency from 51.8 to 50.05 Hz, and the emergency generator capacity from 0.7605 to 0.3420 MVAr at MATLAB and Simulink. [ABSTRACT FROM AUTHOR]

With the advancements in electric vehicles in the market, it is necessary to rephrase the converter for effective gain. The proposed Two Input – Two Output (TITO) converter accepts input from two sources: Battery and Solar PV panel included with non-isolated buck, boost, and buck-boost DC–DC converters. Also, the proposed TITO converter topology uses only three switches when compared to conventional multiport converters where the number of semiconductor switches is higher. The conventional converter has a higher number of components, whereas the proposed converter has a reduced number of conduction components as well as switching semiconductors. As a result, there is a reduction in energy lost. The proposed topology is designed and simulated in MATLAB Simulink and observed with a lower ripple voltage during buck, boost and buck-boost mode. Experimental setup results are also obtained from a prototype of the proposed converter, which confirms the theoretical considerations and effectiveness of the proposed topology. [ABSTRACT FROM AUTHOR]

Engine lubricants globally face the challenge of meeting the demands of new engine technologies while enhancing energy efficiency and reducing emissions. Lubricants must enhance their performance and sustainability, improve reliability in complex and harsh environments, and minimize environmental impact and health risks. This study explores the influence of two different formulations of low viscosity lubricants, tested through actual road endurance trials, on a hybrid vehicle's aftertreatment system performance and overall emission levels. The study includes 120,000 km of endurance testing in four different challenging environments in China, as well as 90,000 km of endurance testing in a typical urban and highway driving cycle in a large city. Results indicate that emissions from the test vehicles during the 120,000 km and 210,000 km durable Worldwide harmonized Light vehicle Test Cycles (WLTCs) meet China's Stage 6 light-duty vehicle emission standards, with the 210,000 km Real Driving Emission test (RDE) results also conforming to these standards. Relative to fresh TWC, the light-off temperature increased by a mere 60 °C, and the oxygen storage capacity declined by around 19% following endurance testing. Additionally, the GPF exhibited satisfactory performance after 210,000 km of endurance testing, showing lower backpressure values compared to the fresh-coated samples, with no notable ash buildup observed in the substrate. Drawing on the outcomes of actual road endurance testing, this study illustrates that employing low-to-mid-ash-content, low-viscosity lubricants is both compatible and reliable for aftertreatment systems in present or advanced hybrid technologies. Premium lubricants facilitate vehicles in sustaining compliant and stable emission performance, even amid harsh environments and complex operating conditions. Furthermore, the tested lubricants effectively inhibit excessive aging of the aftertreatment system over prolonged mileage. Moreover, this study discusses the feasibility of rapid aging evaluation methods for aftertreatment systems based on engine test benches, juxtaposed with actual road endurance testing. These findings and conclusions offer crucial references and guidance for enhancing lubricant performance and sustainability. Subsequent studies can delve deeper into the correlation between lubricant performance and environmental impact, alongside optimization strategies for lubricants across various vehicle models and usage scenarios. [ABSTRACT FROM AUTHOR]

Hybrid wheel–track systems have found extensive applications due to the advantages a combination of wheels and tracks. However, the coupling influence between the wheeled and tracked mechanisms poses a challenge to stable and efficient controller design and implementation. This paper focuses on the lateral dynamic control of a vehicle in scenarios where both tracks and wheels are in contact with the ground. A dynamic model of a vehicle is first established based on the tire brush model and linearized general track model. Based on the dynamic model, a novel adaptive model predictive control (AMPC) method is designed considering the coupling and nonlinearity of the wheels and tracks to simultaneously regulate both mechanisms. Compared with traditional model predictive control approaches, the AMPC controller takes the side-slip angle and slip ratio as constraints to prevent the vehicle from reaching unstable states. Simulations are conducted to validate the effectiveness of the controller, and the results indicate that the controller has the capacity to optimize the objective's yaw-rate response while maintaining lateral vehicle stability and preventing slip by imposing constraints. [ABSTRACT FROM AUTHOR]

Hybrid nanofluid flow analyses have obtained a lot of attention due to their usefulness as coolants in hybrid electric vehicles, health care, thermal exchange systems, cancer thermo-therapy, nuclear reactors, microfluidics, aerospace, acoustics, naval structures and power generation in power plants, This research investigated the impacts of slip and induced magnetic field effects on tangential hyperbolic hybrid nanofluid over a stretching wedge with non-uniform heat source. The nanoparticles used in this work are aluminum oxide (Al2O3) and copper (Cu) while water is the based fluid. The fluid flow are modelled as partial differential equations (PDEs). These are transformed to their corresponding ordinary forms (ODEs), which are solved by the homotopy analysis technique with the aid of similarity variables. The impact of prominent physical factors on fluid temperature, velocity, induced magnetic field, local Nusselt number and drag coefficient is explored. Tables and graphs are used to display the results which agreed with the existing ones in the literature. [ABSTRACT FROM AUTHOR]

In order to enhance the performance of safety and fuel economy of connected hybrid electric vehicles (CHEVs), a novel distributed hierarchical platoon control scheme of CHEVs is proposed. First, the non‐linear dynamic model of CHEVs platooning is established to accurately depict the multi‐process coupling characteristics of CHEVs. Then, a distributed hierarchical control framework for CHEVs platooning is proposed, which is consisted of a upper model predictive control (MPC) law and a lower energy management control law. The upper MPC control law is built to produce the desired accelerations of every vehicle in the platoon and the lower fuzzy‐based energy management control law is constructed to ensure the engine maintain at the rang of optimum working point and the motor work with the high efficiency of CHEVs platooning. Finally, the results manifest that the effectiveness of proposed platoon control scheme for CHEVs. [ABSTRACT FROM AUTHOR]

Renewable energy systems (RESs) and electric vehicles (EVs) are critical components of current power systems for lowering greenhouse gas (GHG) emissions, combating climate change, and providing alternative energy sources. However, high initial costs and a lack of infrastructure, power storage, and power quality (PQ) are potential concerns for RESs and EVs in power systems. Among the numerous power quality (PQ) devices, the unified power quality conditioner (UPQC) can compensate for voltage and current-related PQ difficulties, correct the power factor, and is well suited for coordinative operation and control with RESs uncertainty. The position, size, and dynamic VAr control of UPQC, on the other hand, are key influencing variables for enhancing the performance and PQ of electrical distribution systems (EDSs). This paper describes an upgraded UPQC configuration known as open-UPQC (UPQCO) for reducing PQ difficulties caused by the inclusion of PVs and EVs. Further, an efficient gazelle optimization algorithm (GOA) is suggested to solve the multi-objective optimal allocation of the PV-UPQC-O issue with an emphasis on voltage quality, distribution losses, and voltage stability. Simulations were performed for various scenarios using a modified IEEE 33-bus test system. According to the comparison data, the PV-UPQC-O improved the feeder performance more effectively than the standard UPQC and UPQC-O. The proposed PV-UPQC-O results for total loss reduction of 76.15% when compared to base case without EV load penetration. On the other hand, it is around 76.52% reduction when compared to base case with EV load penetration. Furthermore, the proposed GOA outperforms the prairie dog optimization (PDO), pelican optimization algorithm (POA) and coati optimization algorithm (COA) in terms of global solution and convergence. [ABSTRACT FROM AUTHOR]

Integrating network expansion planning into electric vehicle (EV) smart charging solutions involves designing scalable infrastructure to accommodate the growing demand for electric mobility while considering grid capacity and energy distribution efficiency. This paper proposes a hybrid approach for EV smart charging with network expansion planning. The hybrid technique is the joint execution of the coati optimization algorithm (COA) and cascade‐correlation‐growing deep learning neural network, commonly known as the COA‐CCG‐DLNN technique. The objective of the proposed method is to minimize the cost of charging EVs, and it forecasts the best course of action. EV charging with network expansion is based on vehicle‐to‐building (V2B), vehicle‐to‐grid (V2G), and grid‐to‐vehicle (G2V). The COA approach is used to minimize the cost of EV charging and the CCG‐DLNN approach is used to predict the optimal solution for the system. The proposed method is executed on the MATLAB platform and is compared with existing techniques like particle swarm optimization (PSO), heap‐based optimization (HBO), and wild horse optimization (WHO). The proposed method achieves a low cost of $1.33 and a high accuracy of 99.5% compared with other existing techniques. The performance metrics for the proposed method include 2996.348 as the best result, 3000.100 as the mean, 3001.261 as the worst, and a standard deviation of 1.160348, along with a median of 2998.816, all of which outperform other existing methods. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are considered to be the most promising technology upgrades in the coming years to reduce the carbon emission, since the EVs could replace the conventional combustion vehicles as well as mitigate power fluctuations in power grids. This paper focuses on addressing the frequency stability issues in isolated hybrid grids (IHGs) by introducing a fractional order proportional-integral-derivative (FOPID) control for grid-connected power converters. A particle swarm optimization (PSO) technique is adopted to optimize the parameters of the controllers. The advantages of the proposed control method over the conventional proportional-integral-derivative (PID) control, proportional-integral with proportional-derivative (PI-PD) control, and proportional-integral-derivative with filter (PIDF) control schemes are verified by simulation case studies. Besides, a modified virtual rotor concept (MVRC) is proposed in this paper to enhance the inertia of the system. Furthermore, the performance of using EVs to regulate the frequency of IHGs is compared to the superconducting magnetic energy storage (SMES) units, capacitive energy storage (CES) units, and redox flow batteries (RFB) under fixed and variable load conditions. [ABSTRACT FROM AUTHOR]

This manuscript introduces a hybrid technique designed to enhance the simultaneous energy management (EM) of slow and fast-charge electric vehicles (EVs) within a smart parking lot. The proposed hybrid method combines the Archerfish Hunting Optimizer (AHO) and the Tree Hierarchical Deep Convolutional Neural Network (THDCNN), collectively referred to as the AHO-THDCNN technique. The primary objective of the AHO-THDCNN method is to reduce costs and alleviate the strain on the electrical grid. The THDCNN, a neural network model, plays a crucial role in forecasting valuable data by learning from and recognizing patterns within the training input-data samples. The AHO Approach is responsible for effectively managing the distribution of resources and addressing the energy requirements of both slow- and fast-charging EVs. The performance of the AHO-THDCNN approach is rigorously evaluated using the MATLAB software and compared with different existing approaches. The proposed method reaches its maximum energy level of 260 KW at 18 h, outperforming other existing methods in the comparison. In the proposed method, the efficiency is about 97% which is much better than the all other existing methods. This paper showcases the success of the AHO-THDCNN technique in achieving cost reduction and effective energy power management. It introduces an improvement strategy that modifies the existing algorithm for more efficient EM in smart parking lots. [ABSTRACT FROM AUTHOR]

Introduction. Claw pole machines (CPM) are commonly used in the automotive industry. Recently, importance has focused on the use and introduction of permanent magnets (PM) in this type of machine to increase the power density. This paper studies the performance of permanent magnet claw pole machines (PM-CPM) used in hybrid electric vehicle applications. The structure considers that the PMs are placed between the claws of the rotor. Purpose. The influence of the PM magnetization effect on the performance of synchronous PM-CPM is analyzed. Radial and tangential magnetizations are applied to obtain the best possible sinusoidal shape of the electromotive force and an acceptable cogging torque. Then, the electromagnetic performance of the PM-CPM is analyzed and evaluated. Furthermore, due to the complexity of the rotor armature, it seems difficult to give a direct relationship between the PM parameters and the machine torque. This led us to study the effects of magnets geometrical dimensions variations on the torque and its ripple. Method. 3D nonlinear model of the machine is analyzed using the finite element method and comparisons between some electromagnetic performances are processed. Results. It was found that the tangential magnetization of PMs makes it possible to obtain a better distribution of the flux density and a minimum of cogging torque mainly responsible for vibrations and acoustic noise. Also, we observed a non-linear variation between the torque and its ripples depending on the dimensions of the PM. In fact, electromagnetic torque increases linearly with PM size but this is not the case for torque ripples. References 22, tables 2, figures 16. [ABSTRACT FROM AUTHOR]