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Many types of electric vehicle powertrains are being researched. Among them, devices that can transition to two-wheel drive (2WD) are being introduced because full-time all-wheel drive (AWD) is inefficient in power consumption. In addition, transmissions are being applied to reduce the required motor capacity and increase the performance of the vehicle. In this study, we propose a method to transition between AWD and 2WD using the neutral state of the transmission. Because the drive mode transition is based on the driver's demanded torque with the maximum torque of the front wheel motor, the drive mode transition can be delayed. To this end, we propose a method to predict the driver's demanded torque using a time series deep learning model and transition the drive mode in advance. The time series deep learning model is trained using data generated in a virtual environment that includes external sensor information. By comparing the predictive value-based algorithm using the learned model and the typical algorithm based on driver input, we found that the predictive value-based algorithm predicts the transition faster than the typical algorithm, improving acceleration response and regenerative braking energy. [ABSTRACT FROM AUTHOR]

In a traction power supply system, the design of traction substations significantly influences both the system's operational stability and investment costs, while the energy management strategy of the flexible substations affects the overall operational expenses. This study proposes a novel two-stage system optimization design method that addresses both the configuration of the system and the control parameters of traction substations. The first stage of the optimization focuses on the system configuration, including the optimal location and capacity of traction substations. In the second stage, the control parameters of the traction substations, particularly the droop rate of reversible converters, are optimized to improve regenerative braking energy utilization by applying a fuzzy logic-based adjustment strategy. The optimization process aims to minimize the total annual system cost, incorporating traction network parameters, power supply equipment costs, and electricity expenses. The parallel cheetah algorithm is employed to solve this complex optimization problem. Simulation results for Metro Line 9 show that the proposed method reduces the total annual project costs by 5.8%, demonstrating its effectiveness in both energy efficiency and cost reduction. [ABSTRACT FROM AUTHOR]

Simulation is a cornerstone of planning and facilitating the transition towards electric mobility in sub-Saharan Africa's informal public transport. The primary objective of this study is to validate and refine the electro-kinetic model used to simulate electric versions of the sector's minibuses. A systematic simulation methodology is also developed to correct the simulation parameters and improve the high-frequency GPS data used with the model. A retrofitted electric minibus was used to capture high-frequency GPS mobility data and power draw from the battery. The method incorporates key refinements such as corrections for gross vehicle mass, elevation and speed smoothing, radial drag, hill-climb forces, and the calibration of propulsion and regenerative braking parameters. The refined simulation demonstrates improved alignment with measured power draw and trip energy usage, reducing error margins and enhancing model reliability. Factors such as trip characteristics and environmental conditions, including wind resistance, are identified as potential contributors to observed discrepancies. These findings highlight the importance of precise data handling and model calibration for accurate energy simulation and decision making in the transition to electric public transport. This work provides a robust framework for future studies and practical implementations, offering insights into the technical and operational challenges of electrifying informal public transport systems in resource-constrained regions. [ABSTRACT FROM AUTHOR]

The paper presents a method for managing the energy storage and use of a mobile supercapacitor energy storage system (SC ESS) on a tram vehicle for the purpose of active voltage stabilization of the power grid. The method is based on an algorithm that identifies the need to utilize the energy of the SC ESS depending on changes in the voltage of the power grid caused by the driving of other nearby tram vehicles. The waveform of the current flowing into or out of the SC ESS during this control is determined based on Pontryagin's minimum principle, which optimizes the minimum change in the voltage level at the pantograph and the minimum temperature of the supercapacitor. In this way, this approach aims to minimize the changes in the voltage of the power grid caused by other vehicles and to maximize the lifespan of the supercapacitor. The algorithm was tested within the MATLAB/Simulink R2022b programming environment and experimentally validated with an HIL simulation experiment in a laboratory setup to emulate the rail vehicle system, the supercapacitor, and the power supply network. [ABSTRACT FROM AUTHOR]

The efficiency of the DC microgrid can be improved by adding additional energy storage to recover regenerative braking energy. Supercapacitors are well suited for such applications. To make such devices easily connectable to the existing grid, it is beneficial to measure the voltage of the DC bus. The paper proposes a method on how to estimate load current indirectly from this voltage measurement. Estimated current is used in the developed energy management algorithm that adapts current reference of an energy storage system to discharge stored energy in the most energy efficient way. The paper presents simulation and experimental results and shows in some cases efficiency improvement even by more than five percent. [ABSTRACT FROM AUTHOR]

This work presents a sensorless brushless DC motor (BLDCM) drive control, optimized for solar photovoltaic (PV)- and battery-fed light electric vehicles (LEVs). A back-electromotive force (EMF) observer integrated with an enhanced quadrature-phase-locked-loop (QPLL) structure is proposed for accurate rotor position estimation, addressing limitations of existing control methods at low speeds and under dynamic conditions. The study replaces the conventional arc-tangent technique with a QPLL-based approach, eliminating low-pass filters to enhance system adaptability and reduce delays. The experimental results demonstrate a significant reduction in commutation error, with a nearly flat value at 0 degrees during steady-state and less than 8 degrees under dynamic conditions. Furthermore, the performance of a modified single-ended primary-inductor converter (SEPIC) for maximum power point tracking (MPPT) in solar-powered LEVs is verified, minimizing current ripple and ensuring smooth motor operation. The system also incorporates a regenerative braking mechanism, extending the vehicle's range by efficiently recovering kinetic energy through the battery with 30.60% efficiency. The improved performance of the proposed method and system over conventional approaches contributes to the advancement of efficient and sustainable solar-powered BLDC motor-based EV technologies. [ABSTRACT FROM AUTHOR]

Based on the real vehicle configuration driven by parallel hub motor, the main factors affecting the braking energy recovery efficiency were analyzed, and the Federal Kalman filter longitudinal speed estimation algorithm was used to propose the three-layer compound braking control strategy. By the braking decision-making layer, the braking conditions were identified and entered into the corresponding braking mode according to the pedal input and driving state. According to the decision, by the braking control layer, the electric braking torque distribution of the front and rear wheels was optimized through particle swarm optimization (PSO) under conventional braking conditions to maximize the effective battery recovery efficiency. Under emergency braking conditions, the fuzzy self-tuning PID algorithm was used to realize the safe and effective control of the slip rate of each wheel on different adhesion roads, and the robustness of the anti lock braking control was optimized with improved braking safety. The command of the control layer was responded by the braking executive layer, and the electric braking compensation mechanism was designed to quickly compensate and adjust the pressure error of each wheel cylinder for improving the braking stability of the vehicle. The control strategy was verified by the real vehicle. [ABSTRACT FROM AUTHOR]

The research work proposes optimal energy management for batteries and Super-capacitor (SCAP) in Electric Vehicles (EVs) using a hybrid technique. The proposed hybrid technique is a combination of both the Enhanced Multi-Head Cross Attention based Bidirectional Long Short Term Memory (Bi-LSTM) Network (EMCABN) and Remora Optimization Algorithm (ROA), termed the EMCABN-ROA technique. This approach involves an EMCABN technique that was trained online using load power requirements to match hybrid energy storage dataset characteristics for low-frequency power requirement prediction for the battery. The proposed technique is implemented in the Matlab and compared with several other benchmarks, such as Proportional Integral Derivative (PID) and Fractional-Order Proportional-Integral-Derivative (FOPID). The efficiency of regenerative braking using the proposed technique is reported to decrease to 4.5%. Efficiencies for different SOC ranges are analyzed, with the proposed approach at 48% and 43.5%, underscoring the significance of the initial voltage of the SCAP in the utilization of braking energy. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A smart regenerative braking system for EVs can reduce unnecessary brake operations by assisting in the braking of a vehicle according to the driving situation, road slope, and driver's preference. Since the strength of regenerative braking is generally determined based on calibration data determined during the vehicle development process, some drivers could encounter inconveniences when the regenerative braking is activated differently from their driving habits. In order to solve this problem, various deep learning-based algorithms have been developed to provide driving stability by learning the driving data. Among those artificial intelligence algorithms, anomaly detection algorithms can successfully separate the deceleration data in abnormal driving situations, and the resulting refined deceleration data can be used to train the regression model to achieve better driving stability. This study evaluates the performance of a personalized driving assistance system by applying driver characteristic data, obtained through an anomaly detection algorithm, to vehicle control. [ABSTRACT FROM AUTHOR]

In the context of the "dual carbon" goals, to address issues such as high energy consumption, high costs, and low power quality in the rapid development of electrified railways, this study focused on the China Railways High-Speed 5 Electric Multiple Unit and proposed a mathematical model and capacity optimization method for an on-board energy storage system using lithium batteries and supercapacitors as storage media. Firstly, considering the electrical characteristics, weight, and volume of the storage media, a mathematical model of the energy storage system was established. Secondly, to tackle problems related to energy consumption and power quality, an energy management strategy was proposed that comprehensively considers peak shaving and valley filling and power quality by controlling the charge/discharge thresholds of the storage system. The capacity optimization adopted a bi-level programming model, with the series/parallel number of storage modules as variables, considering constraints imposed by the Direct Current to Direct Current converter, train load, and space. An improved Particle Swarm Optimization algorithm and linear programming solver were used to solve specific cases. The results show that the proposed onboard energy storage system can effectively achieve energy savings, reduce consumption, and improve power quality while meeting the load and space limitations of the train. [ABSTRACT FROM AUTHOR]

Copyright of Automobile Technology is the property of Automobile Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Greater efficiency in the consumption of energy in the transport sector would mean lower emissions of CO2 and other gases and particles, which contribute to climate change. One of the ways to reduce the impact that the transport sector has on climate change turns out to be the achievement of services with a more efficient consumption of energy per passenger‐km or t‐km transported. Energy consumption has been an ongoing concern in the railway sector practically since its inception. There has always been an interest in finding solutions to reduce it, such as improvements in rolling stock design and the use of regenerative braking which will not be analyzed in this article, and optimal train movement, operation and timetabling, and infrastructure design. This article analyses the existing methodologies that allow the improvement of energy efficiency, and we verified that the developed studies focus more on LRT and Metro systems, and existing infrastructures, namely Urban Rail Transit, characterized by high levels of traffic and energy consumption. However, regarding the analysis of conventional railway infrastructure characteristics and design from an energy efficiency point of view, we found that less has been done relative to strategies to improve energy efficiency. [ABSTRACT FROM AUTHOR]

Fuel cell hybrid electric vehicles employ fuel cells along with batteries or ultracapacitors as power sources, and energy management systems can control their power split. This study investigates energy management strategies for lightweight plug-in extended-range fuel cell hybrid electric vehicles to optimise fuel economy, extend component lifespans, and enhance vehicle efficiency. Comparisons between power follower and dynamic programming-based strategies are conducted, focusing on incorporating PEMFC health constraints to prolong stack durability via on-off binary mode operation. Using the New European Driving Cycle, performance evaluation is conducted on a parallel architecture featuring a 16-kW motor, regenerative braking system, eight 420-Wh batteries in series, and six 1-kW fuel cell stacks. Dynamic programming strategies demonstrated potential improvements, with savings in hydrogen consumption of 62–72 L per driving cycle and SOC levels of 3.9%. Moreover, optimised battery lifespan enabled the system to achieve approximately 18,087 -18,745 charge/discharge cycles. Implementing PEMFC health constraints facilitated reductions in average accumulated active time to 205 seconds per driving cycle and startup/shutdown cycle frequency to 36.5 cycles per hour, promoting enhanced system durability and operational efficiency. [ABSTRACT FROM AUTHOR]

Due to the rapid development of power electronics and energy storage technologies, the trend toward electrified railway systems with onboard energy storage systems (OESS) is being followed by main train manufactures. In such systems, the control of the dc-link can be critical for stability and optimal power sharing between different energy sources. This paper presents a dc-link control strategy intended for OESS-based traction chains. A distinguishing characteristic of the proposed method is its ability to smoothly share the power demand among the available power sources, including the kinetic energy of the train, based on predefined priority levels. The control smoothly reacts to changes in the power limits, including no power, of the available energy sources, without modifying the control structure and with negligible variations of dc-link voltage. Simulation results are provided using a real train model with an onboard battery system. Finally, the proposed control is validated on a hardware prototype.

Copyright of International Journal of Engineering Transactions C: Aspects is the property of International Journal of Engineering (IJE) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract The co‐phase traction power supply system (TPSS) with hybrid energy storage system (HESS) and photovoltaic (PV) is proposed to eliminate the neutral section and improve the regenerative braking energy (RBE) utilization. Although the integration of HESS and PV facilitates the energy saving and cost reduction of the co‐phase TPSS, the high cost and configuration of HESS should be considered, which is the key to affect the optimal operation strategy of co‐phase TPSS. Here, the optimal operation strategy of co‐phase TPSS with HESS and PV is proposed to design the HESS configuration, recycle RBE and improve power quality. The proposed model aims to minimize the total system cost, including HESS investment cost, electricity cost and operation and maintenance cost. Moreover, the proposed model is formulated as a mixed integer linear programming by employing linearization approaches. Finally, case studies verify that the 29.2% cost reduction rate is achieved and the three‐phase voltage unbalance meets the standard requirements.

Abstract Fuel cell vehicles are a reliable solution to address energy shortages. However, when the road conditions are complex, the system distributes power unevenly between fuel cells and lithium batteries, and cannot effectively absorb the energy generated by braking. In response to this issue, an adaptive control strategy is adopted to allocate the required power of the car to two types of batteries in real time. Fuzzy logic is used to continuously optimize the relevant parameters of the controller based on the vehicle state, and a multi-island genetic algorithm is used to optimize the control strategy, enhancing the global search ability of the control strategy and increasing the vehicle’s ability to absorb and reuse the energy generated by braking. The experiment findings denote that the optimized control strategy increases the remaining capacity of lithium batteries by an average of 1.67%, increases energy recovery by an average of 135 W, increases the overall energy recovery rate by an average of 2.8%, and reduces vehicle fuel consumption by an average of 0.24 L/100 Km. It can be concluded that the optimized adaptive fuzzy control strategy can reduce the probability of over-charging and discharging of lithium batteries and improve the battery life. Meanwhile, the optimized strategy can improve the energy reuse rate, reduce vehicle fuel consumption, lower usage costs. The optimized strategy provides a reference for subsequent research on energy management of fuel cell vehicles.

The flexible traction power supply system (FTPSS), integrating power flow controller (PFC), energy storage system, and photovoltaic (PV) system, is able to cancel the neutral section, realize the collaborative operation of multiple traction substations (MTSs), and enhance the efficient utilization of regenerative braking energy (RBE) and renewable energy in electrified railways. However, co‐dispatching energy among MTSs influences the power flow distribution within the traction network, impacting the traction network voltage (TNV). Maintaining optimal energy dispatch while ensuring compliance with TNV standards is critical. Therefore, a two‐stage energy optimal dispatch and control strategy is proposed for FTPSS here. The first stage develops a synergistic energy optimal dispatch model for MTSs considering TNV constraint and power flow constraints, aimed at maximizing the utilization of RBE and PV while minimizing the electricity cost of FTPSS. In the second stage, a control strategy for multiple converters is designed to ensure the dynamic response of FTPSS. Finally, the effectiveness of the proposed strategy and model is substantiated through field load data and experimental validation, and the electricity cost of the FTPSS is reduced by about 21.4%, meanwhile the TNV is maintained in the range of 22.5–29 kV. [ABSTRACT FROM AUTHOR]

The aim of this paper is to examine the relationship between the operating parameters of electric vehicles (EVs) and parameters related to road conditions. The data for analysis came from urban driving trips conducted at different times of day. The average energy consumption was the dependent variable in the model. The following parameters were used as independent variables: the stopping time;; the journey time; the average speed; the quartiles of speed, acceleration, and deceleration, and their maximum values; the number of braking; stops; and the acceleration time and regenerative braking. A multiple linear regression model was developed to predict the average energy demand of an EV based on its kinematic parameters. The proposed model enables the analysis of the influence of various factors related to the route and driving style of the driver on the average energy consumption in the vehicle. [ABSTRACT FROM AUTHOR]

This paper presents a novel nonlinearity-considered regenerative brake torque compensation control (NL-TCC) strategy, aimed at enhancing the brake stability for plug-in hybrid electric vehicles (PHEVs) with dual-motor powertrain. The dynamics models of hybrid brake system considering gear backlash and hydraulic brake nonlinearity are established in MATLAB/Simulink environment. Furthermore, the fluctuation of half-shaft torque is adopted as performance indexes to design the NL-TCC strategy for PHEVs with dual-motor powertrain, and the grey wolf optimizer (GWO) is used to obtain the optimal weight coefficients of different stage error functions. Finally, by means of hardware-in-the-loop (HiL) test, it is concluded that the vehicle jerk of the proposed NL-TCC strategy decreases by a minimum of 51.2% than the strategy without torque compensation control. [ABSTRACT FROM AUTHOR]

To improve the fault-tolerant ability of switched reluctance motor (SRM) drive systems and ensure the braking performance of the electric vehicle driven by in-wheel SRMs, a novel fault-tolerant control method that includes the open circuit and the short circuit is presented in this study. First, a novel modular fault-tolerant power converter, which includes traditional driving topology and modular fault-tolerant construction, is built in accordance with the traditional half-bridge power converter. This novel converter provides an advanced fault-tolerant solution for the faults of SRM drive systems. Then, combined with the corresponding detection signals, a new fault detection method is presented for accurately detecting the faults of the system. Furthermore, a new fault-tolerant control method is proposed to improve the fault-tolerant ability of the system. Notably, the fault-tolerant process is completed by employing the circuit of the traditional power converter to replace the fault circuit of the modular construct when faults are detected. Moreover, simulation results are obtained and compared. The results indicate that the proposed method can ensure vehicle braking performance, including braking comfort, braking safety, endurance mileage, and braking stability. Finally, hardware-in-the-loop is conducted, and the results demonstrate the real-time performance and validity of the proposed method. [ABSTRACT FROM AUTHOR]

Regenerative braking energy recovery is of critical importance for electric vehicles due to their range limitations. To further enhance regenerative braking energy recovery, a dual-fuzzy regenerative braking control strategy based on braking intention recognition is proposed. Firstly, the distribution strategy for braking force is devised by considering classical curves like ideal braking force allocation and ECE regulations; secondly, taking the brake pedal opening and its opening change rate as inputs, the braking intention recognition fuzzy controller is designed for outputting braking strength. Based on the recognized braking strength, and considering the battery charging state and the speed of the vehicle as inputs, a regenerative braking duty ratio fuzzy controller is developed for regenerative braking force regulation to improve energy recovery. Furthermore, a control experiment is established to evaluate and compare the four models and their respective nine braking modes, aiming to define the dual fuzzy logic controller model. Ultimately, simulation validation is conducted using Matlab/Simulink R2019b and CRUISE 2019. The results show that the strategy in this paper has higher energy savings compared to the single fuzzy control and parallel control methods, with energy recovery improved by 26.26 kJ and 96.13 kJ under a single New European Driving Cycle (NEDC), respectively. [ABSTRACT FROM AUTHOR]

To solve the problem of asynchronous speed between the coaxial in-wheel motors of distributed drive electric vehicle caused by changes in the road surface, load, and other factors during the regenerative braking of the vehicle, which may result in a yaw motion of the vehicle and a reduction in vehicle stability, a synchronization control strategy of regenerative braking for distributed drive electric vehicles is proposed. Firstly, a ring-coupled synchronous control strategy with the current compensation module is designed. Then, the speed controller of a permanent magnet synchronous in-wheel motor and a compensation controller of synchronous control are designed based on the non-singular fast terminal sliding mode control. Combining this with the regenerative braking control strategy, a regenerative braking synchronization control strategy is designed. The simulation results show that compared with the existing synchronization control strategy, the designed new ring-coupled synchronization control strategy can improve the speed synchronization performance between the motors after the disturbance. Moreover, compared with the conventional regenerative braking control strategy, the regenerative braking synchronization control strategy can reduce the speed synchronization error between the motors during the regenerative braking process, so as to improve the synchronization and output stability of the motors during the braking process. [ABSTRACT FROM AUTHOR]

The co‐phase traction power supply system (TPSS) with hybrid energy storage system (HESS) and photovoltaic (PV) is proposed to eliminate the neutral section and improve the regenerative braking energy (RBE) utilization. Although the integration of HESS and PV facilitates the energy saving and cost reduction of the co‐phase TPSS, the high cost and configuration of HESS should be considered, which is the key to affect the optimal operation strategy of co‐phase TPSS. Here, the optimal operation strategy of co‐phase TPSS with HESS and PV is proposed to design the HESS configuration, recycle RBE and improve power quality. The proposed model aims to minimize the total system cost, including HESS investment cost, electricity cost and operation and maintenance cost. Moreover, the proposed model is formulated as a mixed integer linear programming by employing linearization approaches. Finally, case studies verify that the 29.2% cost reduction rate is achieved and the three‐phase voltage unbalance meets the standard requirements. [ABSTRACT FROM AUTHOR]

Reversible substations (RSs) permitting bidirectional power flows can recover the regenerative braking energy of trains in DC traction power supply systems (TPSSs), increasing the energy efficiency of railway systems. To predict their effects on system dynamics and energy savings, the paper develops multiresolution models (MRMs) to simulate the RS roles with different fidelities. A high‐resolution model for the transient simulation replicates a particular topology where a three‐level voltage source inverter is connected to the secondary winding of an existing 12‐pulse rectifier transformer and regulated to keep a constant DC voltage in the inverting mode. Furthermore, it can model the transient effects of pantograph‐to‐line arcing by inserting arc voltage profiles at the train's input stage. To increase the computation speed in the long‐term energy flow simulation, a low‐resolution model simplifies the rectifiers into a series connection of a diode and a controlled voltage source depicting their nonlinear output characteristics and then places a DC voltage source in parallel to form a reverse path for braking power recovery. In addition, nonlinear conversion efficiencies are introduced to calculate energy flows across substations. The MRMs are tested based on a 1.5 kV DC TPSS and discussed alongside system dynamics under normal operation or pantograph arcing and the consistencies between different models. The RS using bidirectional voltage source converters only is additionally modelled to compare the technical performance of the two topologies in terms of system dynamics and energy efficiencies. [ABSTRACT FROM AUTHOR]

ABSTRACT This work deals with the design and development of a dual source based propulsion architecture for electric vehicles. The converter utilized for the propulsion system is derived from a conventional CuK converter, which operates in a bidirectional power flow mode. The proposed propulsion system has two energy sources: a battery and a supercapacitor (SC). The proposed system has continuous input and output currents at low and high voltage sides, which improves the cycle life of hybrid energy storage (SC and battery) and the DC‐link capacitor. Further, the proposed converter has magnetic reduction compared to a conventional two‐input bidirectional CuK converter. Moreover, an effective and simpler control strategy (only one switch is pulse width modulated (PWM) operated at a time) has been developed for energy management between sources during charging (regenerative braking) and discharging (propulsion), which is effectively verified by simulation and experimental results. Further, a frequency domain (bode plot)‐based technique is used for controller design and stability analysis of the proposed system. [ABSTRACT FROM AUTHOR]

The electrification of towing and trailing units creates new torque allocation alternatives among different units of articulated heavy vehicles. To increase the power and energy efficiency, control algorithms can request propulsion or regenerative braking from a single unit while keeping the other units unbraked or unpropelled. However, this may lead to safety problems, such as jackknifing or trailer swing. This paper uses a high-fidelity simulation tool to formulate safe operating envelopes for a tractor and semitrailer combination for braking-in-turn cases. The effects of different vehicle and environment parameters on the safe operating envelope are studied. The safe operating envelope obtained is then validated using real vehicle tests and can be used with any control algorithm to avoid requesting unsafe unit force combinations. [ABSTRACT FROM AUTHOR]

The benefits of utilizing Switched Reluctance Motor (SRM) drives in traction applications can be realized fully by improving the electromagnetic performance of the machine in the generating mode of operation. This is because the generating capability of an SRM drive could be utilized for regenerative braking and also for the machine to generate power for the vehicle while the engine is in operation. In this paper, a current profiling-based control strategy is proposed to reduce the torque ripple in an SRM drive in the generating mode. The reference current profile is determined using a multi-step computation method to minimize torque ripple and maximize the average torque. The reference current profile is derived based on the reference torque command by utilizing the torque–current–angle look up table. The flux linkage characteristics of the SRM are considered when deriving the phase reference current profile. Then, the performance of the proposed profiling method, analytical linear and cubic torque sharing functions (TSFs), and the average torque optimization scheme are compared using simulation results. Finally, an experimental correlation is performed to validate the efficacy of the proposed control scheme. [ABSTRACT FROM AUTHOR]

Copyright of Rolling Stock (1002-7602) is the property of Rolling Stock Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Fuel cell vehicles are a reliable solution to address energy shortages. However, when the road conditions are complex, the system distributes power unevenly between fuel cells and lithium batteries, and cannot effectively absorb the energy generated by braking. In response to this issue, an adaptive control strategy is adopted to allocate the required power of the car to two types of batteries in real time. Fuzzy logic is used to continuously optimize the relevant parameters of the controller based on the vehicle state, and a multi-island genetic algorithm is used to optimize the control strategy, enhancing the global search ability of the control strategy and increasing the vehicle's ability to absorb and reuse the energy generated by braking. The experiment findings denote that the optimized control strategy increases the remaining capacity of lithium batteries by an average of 1.67%, increases energy recovery by an average of 135 W, increases the overall energy recovery rate by an average of 2.8%, and reduces vehicle fuel consumption by an average of 0.24 L/100 Km. It can be concluded that the optimized adaptive fuzzy control strategy can reduce the probability of over-charging and discharging of lithium batteries and improve the battery life. Meanwhile, the optimized strategy can improve the energy reuse rate, reduce vehicle fuel consumption, lower usage costs. The optimized strategy provides a reference for subsequent research on energy management of fuel cell vehicles. [ABSTRACT FROM AUTHOR]

Electric trains equipped with efficient electric motors and powered by high-capacity hydrogen fuel cells are a choice of the global transportation industry to accelerate the global transition to a low-carbon transportation system. The operation of distinct vehicle types in varying geographical regions is characterized by unique features. Concurrently, the continually fluctuating demand for electricity necessitates the utilization of fuel cells in conjunction with energy storage devices. Thus, this study centers on examining the role of the PEMFC-supercapacitor direct hybridization system within the authentic driving profile of the subway in the Bangkok metropolitan region. The supercapacitor within the hybridization system serves as an energy management device, enhancing the durability of fuel cells when subjected to diverse load demands associated with train propulsion. In order to validate this hypothesis, an MRT (Mass Rapid Transit) protocol was formulated to emulate and serve as the load demand for the investigation, and then the PEMFC-supercapacitor direct hybridization system was operated using the created profile. Subsequently, the hybridization system undergoes electrochemical characterizations and fuel cell performance tests for diagnostic purposes. Empirical findings validated that the supercapacitor operates as a filter, generating a stable voltage response to varying load requirements, and possesses the capability to assimilate energy originating from regenerative braking. The integration of supercapacitors in stabilizing the operational parameters of proton exchange membrane fuel cells (PEMFCs) has markedly prolonged their operational lifespan. This assertion is substantiated by voltage signal analysis, which demonstrated a negligible increase in surface resistance of merely 0.23%, as depicted by Nyquist plots. Furthermore, cyclic voltammetry revealed that the system attained a peak electrochemical surface area (ECSA) of 0.0089 (106 cm2/mg of Pt). The information presented in this article constitutes data that has been minimally explored in existing research. Specifically, the study concentrates on the practical driving pattern of electric trains. Consequently, experimental results were deemed advantageous for the advancement of heavy-duty electric vehicles. [Display omitted] • Exploring the relationship between the utilization of an energy storage device as a SC and energy generation as a PEMFC. • Establishing a connection between the authentic current profile of an MRT metro system and an accelerated stress test. • Examining the electrochemical reactions within fuel cells, drawing upon operational data from the transportation sector. [ABSTRACT FROM AUTHOR]

This article presents a solar photovoltaic (PV) array and battery powered electric rickshaw model. voltage source inverter (VSI) fed brushless direct current (BLDC) motors power e-rickshaws. The maximum power point tracking (MPPT) technique on dc–dc converter connected between the PV array and DC bus is used to ensure continuous input and output current with minimum ripple. The regenerating braking energy of the electric vehicle makes it more efficient and provides wider operational range in comparison to the conventional electric vehicle. During regenerating braking mode VSI is operated like boost converter, to boost the back electromotive force (back-emf) of the motor and charges the battery at faster rate. The motor kinematic energy is recovered and supplied to the battery. A noble close loop two-boost method is employed on VSI fed BLDC motor drive system. In a MATLAB/SIMULINK environment, the model is created. On the OPAL-RT platform, a model is tested in real-time. It has been observed that the real-time results validate the results of MATLAB/SIMULINK. Additionally, a comparison is made between the two-boost and single-boost methods. The result shows that the recovered energy is more than twice that of the single-boost method. [ABSTRACT FROM AUTHOR]

Supercapacitors are widely acknowledged as crucial devices for storing and converting electrical energy, alongside batteries and fuel cells. Their ability to rapidly charge and discharge, typically within seconds or even milliseconds, makes them ideal for high-power applications. This feature provides significant advantages for electric vehicles, such as regenerative braking and hill-climbing, where quick energy transfer is essential. To optimize the power performance of supercapacitor cells, it is essential to focus not only on the active material but also on the inactive components, including binders, conductive agents, and separators. The latter functions as an electronic insulating barrier between the cathode and the anode while facilitating optimal ionic transport across the cell. Therefore, particularly in high-power devices, selecting suitable separators is crucial to ensure fast charging kinetics and minimal cell resistance. Despite significant progress in developing high-power electrode materials, relatively few studies have been dedicated to membranes and their impact on the cell's electrochemical behavior. Herein, we provide a practical guide for choosing appropriate membranes for high-power supercapacitor applications. A comprehensive description of the main characterization methods for reliable evaluation of separators, alongside practical experimental examples, is given below. A special discussion is devoted to the evaluation of membrane impedance by various analytical approaches. [ABSTRACT FROM AUTHOR]

Purpose. To analyze the energy saving reserves under the conditions of implementation and integration of the system in order to find rational driving modes in the general system of managing underground electrified transport. Methodology. The work presents the method for processing data arrays obtained experimentally with the help of a measuring system and theoretically with the use of the “Rational Trajectory” software. Findings. Experimental studies were carried out using a testing system created on the basis of a refurbished train with energy recovery system. Theoretical studies were carried out using the “Rational Trajectory” software, which is based on the principle of solving a multi-criteria problem by the method of the main criterion. The minimum amount of electricity consumption from the overhead contact line was chosen as the main criterion. The software was developed in the LabVIEW graphical programming environment in order to determine the rational modes of driving rolling stock and energy indicators in a given area of its operation. The amount of electricity consumed for traction and the amount of electricity generated by the train during regenerative braking were determined based on the results of experimental and theoretical studies, respectively, under typical and rational modes of driving the train for given identical operating conditions. Originality. Further research on the analysis of energy saving reserves on the rolling stock of underground electrified transport was achieved due to the introduction of a system for finding a rational driving mode. Practical value. It has been established that the implementation and incorporation of the “Rational Trajectory” software into the train control system will save up to 14.7 % of the amount of electricity consumed for traction, compared to typical modes operation on a given track section. [ABSTRACT FROM AUTHOR]