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A new topology of multi-input bidirectional DC-DC converters is proposed in this paper. The converter has a boost behavior, i.e., the output voltage is higher than the sum of the input voltages. This family of converters is particularly suited for hybrid energy storage systems, where different DC sources are connected together and where the output voltage is significantly higher than the voltage of a single storage. The proposed converter reduces the number of required switches, leading to higher efficiency and reduced complexity compared to traditional n-input converters. The new topology demonstrates superior performance by enabling higher efficiency with fewer components. A dedicated control, based on PI controllers, is provided to ensure stable operation under dynamic conditions. The effectiveness of the proposed solution is tested using experimental results on a four-input 20 A/100 V converter prototype. [ABSTRACT FROM AUTHOR]

The goal is to develop an adaptive energy management method that can predict driving circumstances, reducing stress on the primary storage battery in a hybrid electric vehicle (HEV). To improve battery lifespan and overall efficiency, power allocation among multiple energy storage sources (ESS) must go beyond traditional rule-based schemes. This paper offers a promising solution to address these challenges. Standardized driving cycles, such as the Urban Dynamometer Driving Schedule (UDDS), are used as load torque to evaluate the power demand in hybrid electric vehicles (HEV). First, feature extraction techniques improve the extraction of important features from historical driving cycle data, thereby facilitating the long short-term memory (LSTM) based on multistep velocity predictors using a warm-up algorithm. This predicted future velocity profile serves as input to the HEV system. Second, within the hybrid electric vehicle (HEV) system, the battery is linked to a modified interleaved DC-DC boost converter. The results show that this converter affects factors such as ripple, converter loss of 29%, state of charge (SOC) level, and efficiency improvement to 98%. Finally, the energy management system (EMS) with the modified rule-based scheme is implemented with neural networks for optimization. This EMS is responsible for continuously adjusting the power allocation. Simulation results show a significant average reduction of about 33.33% at peak battery level, thereby extending battery life. Compared with conventional rule-based EMS methods, the proposed strategy significantly improves efficiency by reducing the total energy loss by about 10.36%. These results emphasize the reliability and robustness of the proposed strategy. [ABSTRACT FROM AUTHOR]

Abstract The design and construction of an adaptive energy management system incorporating a 12 V–2 Ah battery and a 1F ultracapacitor for solar powered hybrid electric vehicles are presented in this paper. The primary storage battery’s longevity and overall system efficiency are intended to be increased by the EMS’s ability to forecast driving circumstances and lessen the load on it. The method optimizes power distribution among numerous energy storage sources by using sophisticated hardware configurations. Using Arduino Uno software, the EMS continuously modifies power distribution in response to driving circumstances and vehicle energy needs. To conduct a thorough assessment of the system, a mechanical configuration was created to replicate the vehicle’s dynamics. This included situations in which the vehicle moved only by using its moment of inertia and not the accelerator. Testing under diverse operating conditions, such as braking, acceleration, and varying slopes is made possible by this configuration. According to experimental data, peak battery stress is significantly reduced by about 33.33%, resulting in longer battery life. With minimal energy losses of 4.3–4.9%, the system regularly achieves power efficiency ranging from 95.1 to 95.7%. Furthermore, the suggested hardware-based EMS lowers overall energy loss by roughly 10.36%, highlighting its dependability and effectiveness in contrast to traditional techniques. The longevity and efficiency of the system are enhanced by the balanced power distribution between the battery, ultracapacitor, and solar sources, which prevents any one source from being overloaded. Overall, the suggested energy management system is successful in improving energy distribution and extending the lifespan and performance of HEV components.

Abstract An improved method of clutch coordinated control based on the Kalman filter was proposed to solve the problem that the existing mode switching strategy of hybrid electric vehicles could not adapt to engine temperature changes and clutch wear. First, taking advantage of the relationship between the torque transmitted by the clutch and the starting resistance of the engine, combined with the characteristics of the clutch, the clutch wear was roughly calculated. Accordingly, the control strategy of the clutch in the existing mode switching was improved to adapt to the clutch wear. The adaptive control strategy proposed for clutch wear included the fuzzy control module of the initial engagement pressure, the fuzzy inference module of the clutch engaging pressure change, the clutch wear estimation module and so on. Second, the Kalman filter was used to process the results to improve the estimation accuracy of clutch wear. The engine starting resistance related to starting speed and temperature was modeled to enhance the adaptability of the control strategy to engine temperature. Finally, the designed control strategy was verified in simulation. The results show that the improved control strategy can complete the mode switching when the engine temperature is variable and the clutch is worn. The maximum impact degree increased from 5 m/s3 without wear to 8.5 m/s3 with wear, but it is still less than the index limit, and it can be considered that the proposed strategy can achieve the desired control effect. The fuzzy control algorithm proposed enhances the vehicle's ride comfort during mode switching from pure electric driving to hybrid driving.

An improved method of clutch coordinated control based on the Kalman filter was proposed to solve the problem that the existing mode switching strategy of hybrid electric vehicles could not adapt to engine temperature changes and clutch wear. First, taking advantage of the relationship between the torque transmitted by the clutch and the starting resistance of the engine, combined with the characteristics of the clutch, the clutch wear was roughly calculated. Accordingly, the control strategy of the clutch in the existing mode switching was improved to adapt to the clutch wear. The adaptive control strategy proposed for clutch wear included the fuzzy control module of the initial engagement pressure, the fuzzy inference module of the clutch engaging pressure change, the clutch wear estimation module and so on. Second, the Kalman filter was used to process the results to improve the estimation accuracy of clutch wear. The engine starting resistance related to starting speed and temperature was modeled to enhance the adaptability of the control strategy to engine temperature. Finally, the designed control strategy was verified in simulation. The results show that the improved control strategy can complete the mode switching when the engine temperature is variable and the clutch is worn. The maximum impact degree increased from 5 m/s3 without wear to 8.5 m/s3 with wear, but it is still less than the index limit, and it can be considered that the proposed strategy can achieve the desired control effect. The fuzzy control algorithm proposed enhances the vehicle's ride comfort during mode switching from pure electric driving to hybrid driving. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative Hybrid Electric Vehicle (HEV) configuration that employs a fuel cell as the basic energy source and an onboard Photovoltaic (PV) array as the supplementary source. The system is controlled using an advanced Model Predictive Control (MPC) system enhanced by a Recurrent Neural Network (RNN), specifically future to manage the induction motor of the EV. The HEV system includes three major components: a PV array, fuel cell and an electrolyzer, each with a specific role in powering the vehicle. The fuel cell can be assisted by PV array by supplying extra power when sunlight conditions are optimal. When the vehicle is idle, any excess power generated by the PV array is switched into hydrogen by the electrolyzer and kept in an aboard hydrogen tank for later use. To meet the voltage requirements of both the energy sources and the motor, a quadratic bidirectional buck-boost converter (QBBC) is used. It effectively manages the voltage output and ensures compatibility across different energy sources. The system's performance and efficacy are assessed under various sunlight levels and speed conditions. The RNN-based MPC system is benchmarked against traditional control methods, with an Artificial Neural Network-based MPC (ANN-MPC) and a Proportional-Integral (PI) controller. An incremental conductance algorithm for is adopted as Maximum Power Point Tracking (MPPT) algorithm, which adjusts power extraction from the PV array. The traction system of HEV includes an induction motor with indirect vector control, with the RNN-based MPC system providing precise speed control and enhanced efficiency. The RNN model predicts motor speed, contributing to the finer execution of the control system. Comparative analysis using MATLAB/SIMULINK demonstrates the efficacy of the RNN-based MPC system, showing its advantages over ANN-MPC and PI controllers in real-world conditions. This paper significantly advances the expansion of intelligent and efficient electric vehicle systems. [ABSTRACT FROM AUTHOR]

This paper unveils a groundbreaking wide-range DC-DC converter with significant voltage gain and bidirectional capability, engineered explicitly for Hybrid Electric Vehicle (HEV) chargers. This converter facilitates both Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) operations. It aims to revolutionize efficiency, voltage range, and bidirectional power flow capabilities, marking a significant leap forward from existing solutions. A meticulous comparative analysis between established systems and the proposed converter highlights its distinct advantages and evolutionary strides within the HEV charging infrastructure landscape. By enhancing the versatility and performance of HEV chargers, this converter promises to address critical challenges in energy management and integration. Its innovative design not only optimizes energy transfer but also supports future advancements in smart grid technology and sustainable transportation. The results of this study underscore the converter's potential to drive forward the next generation of electric vehicle infrastructure, paving the way for more efficient and resilient energy systems. [ABSTRACT FROM AUTHOR]

Energy management strategies play a crucial role in enhancing the fuel efficiency of hybrid electric vehicles (HEVs) and mitigating greenhouse gas emissions. For the current commonly used time horizon optimization methods that only target the trend curve of the optimal battery state of charge (SOC) trajectory obtained offline, which are only suitable for buses with known future driving conditions, this paper proposed an energy management strategy based on an adaptive network-based fuzzy inference system (ANFIS) that optimizes the time horizon length and enhances adaptability to driving conditions by integrating historical vehicle velocity, accelerations, and battery SOC trajectory. First, the vehicle velocity prediction model based on the radial basis function (RBF) neural network is used to predict the future velocity sequence. After that, ANFIS was used to optimize and update the length of the forecast time horizon based on the historical vehicle velocity sequence. Finally, compared with the fixed time horizon energy management strategy, which is based on model predictive control (MPC), the average calculation time of the energy management strategy is reduced by about 23.5%, and the fuel consumption per 100 km is reduced by about 6.12%. [ABSTRACT FROM AUTHOR]

The automobile industry is shifting from internal combustion engine vehicles (ICEVs) to hybrid electric vehicles (HEVs) or electric vehicles (EVs) extremely fast. Our calculation regarding the most popular private car brand in Bangladesh, Toyota, shows that the life cycle cost (LCC) of a Toyota BZ3 (EV), USD 43,409, is more expensive than a Toyota Aqua (HEV) and Toyota Prius (HEV), but cheaper than a Toyota Axio (ICEV) and Toyota Allion (ICEV). It has been found that about a 25% reduction in the acquisition cost of a Toyota BZ3 would lower its LCC to below others. EVs can be a good choice for those who travel a lot. Changes in electricity prices have little effect upon the LCC of EVs. With the expected decline in the annual price for batteries, which is between 6 and 9%, and the improvement of their capacities, EVs will be more competitive with other vehicles by 2030 or even earlier. EVs will dominate the market since demand for alternative fuel-powered vehicles is growing due to their environmental and economic advantages. [ABSTRACT FROM AUTHOR]

Hybrid Electric Vehicles (HEVs) are affected to a high extent by Intelligent Energy Management Systems (IEMS), especially during situations that are challenging and unpredictable including changes in traffic patterns, road gradients, and speed. These uncertainties are not easily solved using the existing energy management systems; therefore, this paper presents the design of an AFLC-IEMS employing Type 1 and Interval Type 2 Fuzzy Logic Controllers for energy distribution improvement. The AFLC-IEMS sustains the combustion of fuel and discharge of battery in a way that promotes efficiency in switching between the internal combustion engine and the electric motor. The simulation results with the one-way analysis of variance test confirm our finding that the proposed system is far superior to the traditional ones. The savings achieved by the AFLC-IEMS are a decrease in fuel consumption from 7.26 Liters/100 km down to 6.69 Liters/100 km, as well as an increase in the battery State of Charge (SoC) from 72.7% to 75.8%. The ANOVA analysis shows that the fuel consumption (p < 0.01), the motor torque (p < 0.01), as well as the SoC of the battery (p < 0.05) in the developed FLC are statistically superior to the Type 1 FLC and Type 2 FLC. These improvements are achieved by adapting the technology to the situation to adjust the control strategy; hence, the efficiency of the energy management system is optimized. Therefore, the AFLC-IEMS is more effective in improving the fuel economy and reducing emissions under various conditions.

To optimize the fuel consumption of hybrid electric vehicles (HEV) controlled by rule-based energy management strategies (EMS), multiple driving cycles are simulated. These driving cycles are simulated with different EMS calibrations and the optimizer compares the corresponding fuel consumptions. A drive cycle simulation usually ends with a different end state of charge (SOC) compared to the start SOC. Such an unbalanced SOC for the secondary energy source (battery) affects the consumption of the primary energy source (fuel). Therefore, it is crucial to consider the battery SOC difference when comparing fuel consumption in a drive cycle. In this paper, six different methods are presented to compensate the SOC difference or to achieve a balanced SOC, such as Multiple Sequential Drive Cycle Simulation, Variation of Start SOC, Linear Regression, Static Correction Factor, Individual Correction Factor and Linear Interpolation. These methods are compared in their applicability within a numerical optimization and, for a subset, also in their accuracy in SOC compensation using an exemplary hybrid electric vehicle model. It was determined, that Linear Interpolation requires twice as much computing time as either Static or Individual Correction Factor, but it is the most accurate method. In addition, it supports robust EMS behavior without strongly restricting the boundary conditions within the optimization.

Considering climate change, hybrid electric vehicles (HEVs) provide a clean alternative for transportation. This study presents an HEV with a fuel cell and ultra-capacitor connected in a parallel-type configuration. Direct model predictive control is used to optimize the power flow between the energy sources and the motor. Notably, the proposed controller uses a global approach, i.e., a single controller for the regulation of both power converters, thereby enhancing overall performance. Furthermore, the controller design leverages a non-averaged state space model that explicitly incorporates the switching nature of the converters. A method for computing reference currents for the fuel cell and ultra-capacitor is also introduced, which utilizes the ultra-capacitor current to manage power demand transients. Simulation results show that the proposed technique produces better results in terms of overshoot, steady-state error, and response time compared to recent studies in the literature.

In this paper, circular area movement optimization (CAMO), a novel global search algorithm, and its hybridization with the pattern search method (PSM) are proposed to solve the multimodal optimization problem. The CAMO is an optimization technique that creates a circular search area, moves the area, and searches the entire search area. Depending on the type of samples inside the area, two strategies are used to quickly and efficiently find the optimal point across the area. Also, the hybridization with the PSM supports fast convergence on adjacent optima from the points that are discovered in the global search using the CAMO. The effectiveness of the algorithm was verified by applying the CAMO to two test functions, and its superiority was confirmed through comparison with existing optimization algorithms. In addition, by applying the proposed algorithm to the optimal design of the cogging torque of an interior permanent magnet synchronous motor for a hybrid electric vehicle, a design that reduces the cogging torque by 95.65% was successfully derived. Lastly, stress analysis and demagnetization analysis were performed to examine the structural and thermal stability of the motor.

A new topology of multi-input bidirectional DC-DC converters is proposed in this paper. The converter has a boost behavior, i.e., the output voltage is higher than the sum of the input voltages. This family of converters is particularly suited for hybrid energy storage systems, where different DC sources are connected together and where the output voltage is significantly higher than the voltage of a single storage. The proposed converter reduces the number of required switches, leading to higher efficiency and reduced complexity compared to traditional n-input converters. The new topology demonstrates superior performance by enabling higher efficiency with fewer components. A dedicated control, based on PI controllers, is provided to ensure stable operation under dynamic conditions. The effectiveness of the proposed solution is tested using experimental results on a four-input 20 A/100 V converter prototype.

Copyright of Automotive Digest is the property of Automotive Digest 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.)

In this study, a parallel hybrid electric vehicle has been modeled, and a new rule-based and battery-priority control method has been proposed, which will reduce fuel consumption and carbon emission values to minimum values. This control method is based on running the electric motor more and operating the internal combustion engine in the most efficient region. In the proposed control method, it is also ensured that the electric motor is operated as a generator. The control method is used in the New European Driving Cycle (NEDC), ECE-15 (Urban Driving Cycle), and in Extra Urban Driving Cycle (EUDC) driving cycle conditions. In this study, two different simulation studies are achieved in accord with the critical state of charge (SOC) of the battery. The SOC value is selected as 55% and 65% for its effect on fuel consumption in these driving cycles. According to the results, the parallel hybrid electric vehicle which has a 65% SOC value, gasoline efficiency becomes executed 35.7% inside the NEDC cycle, 25.3% in the EUDC cycle, and 52.3% in the ECE-15 cycle. Furthermore, for the parallel hybrid electric vehicle with a 55% SOC value, fuel efficiency is 29.3% in the ECE-15 driving cycle, 17.6% in the NEDC driving cycle, and 9.6% in the EUDC cycle. The proposed control approach yields the parallel hybrid vehicle's fuel usage and fuel efficiency. [ABSTRACT FROM AUTHOR]

Combination hybrid electric vehicle (HEV) mainly includes two configurations: series-parallel and power-split. It is necessary to consider a variety of metrics to comprehensively evaluate the configuration performance of HEV and compare the two configurations. In order to fully evaluate the HEVs' potential in energy management, a practical and effective multi-objective method that can solve the global optimization-based energy management problem is needed. Based on the idea of dynamic programming (DP) and non-dominated sorting method, this paper proposes a global multi-objective optimization method of non-dominated sorting dynamic programming (NSDP) with multi-control variables. This algorithm can calculate a set of uniformly distributed Pareto solutions for the conflicting or coupling optimization objectives, and the performance of the solution set is improved due to the increase in the dimension of the control variables, which increases the strategy search space. NSDP is applied to two different configurations to fully evaluate the performance of fuel consumption and battery lifespan. The parameters of the configurations are optimized and comprehensively compared based on the implementation of NSDP. The above process can provide theoretical analysis for hybrid power system developers. [ABSTRACT FROM AUTHOR]

The rapid advancements in transportation technology have led to the development of Hybrid Electric Vehicles (HEVs) and smart hybrid electric vehicles (S-HEVs) as potential solutions for reducing fuel consumption, emissions, and dependency on fossil fuels. These vehicles combine conventional internal combustion engine propulsion systems with electric propulsion systems, offering various driving modes and the ability to adjust operation points for increased efficiency. Additionally, HEVs and S-HEVs contribute to the creation of alternative power sources for household applications, provide ancillary services to the grid, and integrate intermittent resources for vehicle charging. The reliability of Electric Vehicle (EV) batteries is a crucial aspect, involving failure recognition, testing methods, and life prediction techniques to ensure prolonged battery life. As countries worldwide strive to transition from gasoline vehicles to EVs, practical limitations such as "range anxiety" due to inadequate charging infrastructure and high costs of long-ranged EVs arise. One potential solution to address range anxiety is the use of range extenders, optimizing driving range, costs, and vehicle performance. These advancements in eco-friendly, safer, and cost-effective transportation contribute significantly to reducing carbon emissions and promoting sustainable development globally. [ABSTRACT FROM AUTHOR]

This current and pioneering work aimed to assess the on-road performance of selected hybrid electric vehicles (HEVs) and electric vehicles (EVs) in local urban road conditions following the World Harmonized Light Vehicles Test Procedure (WLTP) and the chase car protocol. An experimental research design was also implemented to investigate the effects of the different payload conditions on vehicle performance, and corresponding drive cycle patterns for the test vehicles were generated from each on-road test. From the series of these on-road tests, it was revealed that there was high variability in speed profiles, and vehicle speed was generally found to be inversely related to payload weight. The variations in the state of charge, fuel fill-up, and fuel and energy parameters exhibited no significant differences in terms of payload conditions. When compared to both the Canada fuel consumption guide and the US fuel consumption guide, the resulting fuel consumption and energy consumption indicated that the Mitsubishi Outlander PHEV and Mitsubishi iMiEV exceeded energy efficiency standards, unlike the Toyota Prius. Meanwhile, in terms of CO2 emissions, all vehicles demonstrated around 40–70% lower emissions compared to conventional vehicles according to the 2023 estimates of the US Environmental Protection Agency. Being the first of its kind in the Philippines, this study on the on-road performance assessments of HEVs and EVs is essential because it provides empirical data on these vehicles' actual performance in everyday driving conditions. The data are important for evaluating the potential to address environmental concerns, promote sustainable transportation solutions, influence consumer adoption, and shape government policies. With ongoing improvements in technology and expanding charging infrastructure, HEVs and EVs are poised for significant adoption in the coming years. [ABSTRACT FROM AUTHOR]

Energy management strategies play a crucial role in enhancing the fuel efficiency of hybrid electric vehicles (HEVs) and mitigating greenhouse gas emissions. For the current commonly used time horizon optimization methods that only target the trend curve of the optimal battery state of charge (SOC) trajectory obtained offline, which are only suitable for buses with known future driving conditions, this paper proposed an energy management strategy based on an adaptive network-based fuzzy inference system (ANFIS) that optimizes the time horizon length and enhances adaptability to driving conditions by integrating historical vehicle velocity, accelerations, and battery SOC trajectory. First, the vehicle velocity prediction model based on the radial basis function (RBF) neural network is used to predict the future velocity sequence. After that, ANFIS was used to optimize and update the length of the forecast time horizon based on the historical vehicle velocity sequence. Finally, compared with the fixed time horizon energy management strategy, which is based on model predictive control (MPC), the average calculation time of the energy management strategy is reduced by about 23.5%, and the fuel consumption per 100 km is reduced by about 6.12%.

The automobile industry is shifting from internal combustion engine vehicles (ICEVs) to hybrid electric vehicles (HEVs) or electric vehicles (EVs) extremely fast. Our calculation regarding the most popular private car brand in Bangladesh, Toyota, shows that the life cycle cost (LCC) of a Toyota BZ3 (EV), USD 43,409, is more expensive than a Toyota Aqua (HEV) and Toyota Prius (HEV), but cheaper than a Toyota Axio (ICEV) and Toyota Allion (ICEV). It has been found that about a 25% reduction in the acquisition cost of a Toyota BZ3 would lower its LCC to below others. EVs can be a good choice for those who travel a lot. Changes in electricity prices have little effect upon the LCC of EVs. With the expected decline in the annual price for batteries, which is between 6 and 9%, and the improvement of their capacities, EVs will be more competitive with other vehicles by 2030 or even earlier. EVs will dominate the market since demand for alternative fuel-powered vehicles is growing due to their environmental and economic advantages.

Copyright of Automotive Digest is the property of Automotive Digest 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.)

Summary: As battery electric vehicles (BEVs), plug‐in hybrid electric vehicles (PHEVs), and hybrid electric vehicles (HEVs) gain popularity among the consumers, current research initiatives are targeted at developing battery charger structures that can exploit utility power to charge vehicle batteries and thus less dependent on fuel usage. This paper reviews the state of the art and implementation of battery charger structures, charger power levels, and the evolution of publications on electric vehicles in the digital libraries and Espacenet patent base. Charger systems addressed are categorized as a structure with a non‐isolated alternating current (AC)–direct current (DC) stage, structure with an isolated AC–DC stage, and structure with two non‐isolated stages. Advantages and disadvantages of each conductive battery charging structure have been discussed due to weight, volume, cost, necessary equipment, the complexity of topologies, and other factors. In addition, both off‐board and on‐board charger systems with unidirectional or bidirectional power flow are presented. Several electronic converters for power‐level chargers are being developed to allow plug‐in vehicles to be capable of vehicle to grid (V2G), where they can function as distributed resources and power can be returned to the grid. [ABSTRACT FROM AUTHOR]

The performance of Hybrid Electric Vehicles (HEVs), especially in series architecture, is highly dependent on the reliability of electric drive-motor systems. Any failure in power semiconductor devices, such as Insulated-Gate Bipolar Transistors (IGBTs), used in three-phase Voltage Source Inverters (VSIs) for Permanent Magnet Synchronous Motor (PMSM) drive systems, causes a reduction in the reliability and leads to unscheduled maintenance of HEVs. This paper aims to present a three-stage combined model-based and data-driven fault diagnosis approach, the so-called hybrid fault diagnosis approach, to detect, locate, and clear open-circuit faults in IGBTs used in VSI-fed PMSM drive systems in HEVs. Field-Oriented Control (FOC), which is a model-based technique, is used to control the electric drive-motor system. The proposed method, which is based on phase voltage analysis, estimates the current in each phase of VSI using the normal operating conditions dataset to detect open-circuit faults in IGBTs. Once a fault is detected, it is located using the faulty conditions dataset and an online data-driven approach, called the Modified Multi-Class Support Vector Machine (MMC-SVM) algorithm. Thereafter, the faulty IGBT is bypassed by closing the corresponding backup switch, ensuring the continuous operation of the electric drive-motor system. The proposed method can accurately and quickly detect, locate, and clear open-circuit faults in IGBTs without the need for additional sensors. Additionally, it demonstrates robustness against back-to-back and simultaneous faults in IGBTs used in VSI-fed PMSM drive systems in HEVs.

A three-phase interior permanent magnet (IPM) machine with 18-stator-slots/12-rotor-poles and concentrated armature winding is commercially employed as a 10 kW integrated-starter-generator in a commercial hybrid electric vehicle. For comprehensive and fair evaluation, a pair of flux-switching permanent magnet (FSPM) brushless machines, namely one stator permanent magnet flux-switching (SPM-FS) machine, and one rotor permanent magnet flux-switching (RPM-FS) machine, are designed and compared under the same DC-link voltage and armature current density. Firstly, a SPM-FS machine is designed and compared with an IPM machine under the same torque requirement, and the performance indicates that they exhibit similar torque density; however, the former suffers from magnetic saturation and low utilization of permanent magnets (PMs). Thus, to eliminate significant stator iron saturation and improve the ratio of torque per PM mass, an RPM-machine is designed with the same overall volume of the IPM machine, where the PMs are moved from stator to rotor and a multi-objective optimization algorithm is applied in the machine optimization. Then, the electromagnetic performance of the three machines, considering end-effect, is compared, including air-gap flux density, torque ripple, overload capacity and flux-weakening ability. The predicted results indicate that the RPM-FS machine exhibits the best performance as a promising candidate for hybrid electric vehicles. Experimental results of both the IPM and SPM-FS machines are provided for validation. [ABSTRACT FROM AUTHOR]

Hybrid electric vehicle technology (HEVT) is emerging as a reliable alternative to reduce the constraints of battery-only driven pure electric vehicles (EVs). HVET utilizes an electric motor as well as an internal combustion engine for its operation. These components would work on battery power and fossil fuels, respectively, as a source of energy for vehicle mobility. The power is delivered either from battery or fuel or both sources based on user requirements, road conditions, etc. HEVT uses three major propelling systems, namely, front-wheel drive (FWD), rear-wheel drive (RWD), and four-wheel drive (4WD). In these propelling systems, the 4WD model provides torque to all four wheels at the same time. It uses all four wheels to propel thereby offering better driving capability, better traction, and a strong grip on the surface. The 4WD-based HEVs comprise four architectures, namely, series, parallel, series-parallel, and complex. The literature focuses primarily on any one type of architecture for analysis in the context of component optimization, fuel reduction, and energy management. However, a focus on dynamic analysis that gives a real performance insight was not conducted, which is the main motivation for this paper. The proposed work provides an extensive critical performance analysis of all four 4WD architectures subjected to various dynamic operating conditions (continuous, pulse, and step-up accelerations). Under these conditions, various performance parameters such as speed (of vehicle, engine, and motor), power (of engine and battery), battery electrical losses, charge patterns, and fuel consumption are measured and compared. Further, the 4WD architecture performance is validated with FWD and RWD architectures. From MATLAB/Simulink-based evaluation, 4WD HEV architectures have shown superior performance in most of the cases when compared to FWD type and RWD type HEVs. Moreover, 4WD parallel HEV architecture has shown superior performance compared to 4WD series, 4WD series-parallel, and 4WD complex architectures. [ABSTRACT FROM AUTHOR]

Abstract The battery/ultracapacitor hybrid power supply system can solve the problems of high cost and short life of a single power system, and the energy management of hybrid power system has become a vital issue in the field of electric vehicles. In this paper, a fuzzy energy management strategy on the state-of-charge (SOC) estimation of power battery is proposed. Particle filter (PF) algorithm is used to estimate SOC of power battery, then estimated result is regarded as the input variable of fuzzy energy management controller, and the energy distribution result is obtained after fuzzy logic operation. The simulation results show that the SOC estimation result of the PF algorithm is closer to the actual value of power battery SOC. When the SOC estimation result of PF is embedded into the fuzzy controller for joint simulation, it is found that the charge and discharge current, and SOC consumption of the power battery are reduced, which shows the algorithm’s effectiveness. It also provides a specific reference value for the further study of the power supply control strategy of hybrid electric vehicles.

In recent years, there has been a significant increase in braking energy regeneration for hybrid electric vehicles. To improve performance and reduce fuel consumption, a better control strategy composed of braking regeneration and gear shifting is needed. This work presents a braking energy regeneration control strategy for a hybrid electric vehicle (HEV). The mathematical model for the vehicle dynamic system is established, and the objective function of braking energy regeneration is presented based on system analysis. Taking the increased electric energy of a battery as the objective function of the economic downshift law, the multi-island genetic algorithm (MIGA) is used to solve the shifting condition factors corresponding to different deceleration speeds and motor torques and the optimal downshifting speed. The presented control strategy of braking energy regeneration is validated in a typical city cycle form in China, and the results show better energy efficiency. [ABSTRACT FROM AUTHOR]

This paper develops a parallel hybrid electric vehicle (PHEV) proportional integral controller with driving cycle. To improve fuel efficiency and reduce hazardous emissions in hybrid electric vehicles (HEVs) combine an electric motor (EM), a battery and an internal combustion engine (ICE). The electric motor assists the engine when accelerating, driving longer highways or climbing hills. This enables the use of a smaller, more efficient engine. It also makes use of the concept of regenerative braking to maximize energy efficiency. In a Hybrid Electric Vehicle (HEV), energy dissipated while braking is utilized to charge the battery. The proportional integral controller was used in this paper to analyze engine, motor performance and the New European Driving Cycle (NEDC) was used in the vehicle driving test using Matlab/Simulink. The proportional integral controllers were designed to track the desired vehicle speed and manage the vehicle’s energy flow. The Sea Lion Optimization (SLnO) methods were created to reduce fuel consumption in a parallel hybrid electric vehicle and the results were obtained for the New European Driving Cycle. [ABSTRACT FROM AUTHOR]

This research work deals with the problem of controlling a salient-pole permanent-magnet synchronous motor (SP-PMSM) used in hybrid electric vehicles. An adaptive nonlinear controller based on the backstepping technique is developed to meet the following requirements: control of the reference vehicle speed in the presence of load variation and changes in the internal motor parameters while keeping the reliability and stability of the vehicle. The complexity of the control problem lies on the system nonlinearity, instability and the problem of inaccessibility to measure all the internal parameters, such as inertia, friction and load variation. For this issue, an adaptive backstepping regulator is developed to estimate these parameters. On the basis of formal analysis and simulation, as well as test results, it is clearly shown that the designed controller achieves all the goals, namely robustness and reliability of the controller, stability of the system and speed control, considering the uncertainty parameters' measurements. [ABSTRACT FROM AUTHOR]

• A dynamics model of electromechanical hybrid transmission system is established. • Model accuracy is verified by experiment data under time and frequency-domain. • Electromechanical coupling and speed have obvious effect on inherent vibration. • A new method is proposed to analyze electromechanical coupling vibration. • Speed and dynamic meshing force are main factors affecting forced vibration. As the development of hybrid electric vehicle (HEV) to high-speed, heavy-load, the electromechanical coupling vibration becomes the bottleneck in high-speed electromechanical hybrid transmission (EHT) system. To explore the dynamics characteristics and optimization direction for high-speed EHT system, an electromechanical coupling dynamics model under multi-source excitations is established with lumped-distributed parameter method and verified with experiment data. The dynamics model shows higher accuracy in both time and frequency domain analysis. On this basis, firstly, the comparison between lumped-shaft and distributed-shaft model is studied under time and frequency domain. Comparing with the lumped-shaft model, the distributed-shaft model shows higher accuracy, and can better reflect the coupling vibration of multi-stage planetary gears (PGs). Secondly, the inherent vibration model is derived, and the effect of electromechanical coupling and high-speed working conditions on inherent vibration characteristics are studied. Thirdly, a new method called 'machine-electricity-magnet coupling interface' is proposed to reveal the coupling vibration phenomenon. In addition, the signal of stator current and electromagnetic torque includes the frequency of PGs, which is a basis on the fault diagnosis and state monitor of EHT system. Last but not the least, the vibration acceleration of EHT system is analysed under variable speed and load working conditions. Rotation speed and gear meshing force is found as the main influencing factor of series EHT system, and the specific optimization direction is also given. [ABSTRACT FROM AUTHOR]

In order to effectively monitor the operational status and detect faults within a hybrid electric vehicle during mode transitions, this study focuses on a specific power-split hybrid electric vehicle model known as the multimode electromechanical composite transmission system (MM-EMCTS HEV). Taking into account the actual occurrence frequency and simulatability of faults in MM-EMCTS HEV, key faults are extracted. Based on the distinctive characteristics and properties of critical system faults, a fault diagnosis framework is constructed by leveraging global analytical redundancy relations (GARRs). This framework encompasses strategies for residual generation across sensors, actuators, and controlled components, alongside residual evaluation techniques employing mode-dependent adaptive thresholds. The detectability and isolatability of the MM-EMCTS are thoroughly analyzed. Subsequently, simulation and verification of the residual responses under typical faults in the MM-EMCTS are implemented in Matlab/Simulink and Simscape. The results demonstrate the efficacy of the fault diagnosis method, rooted in global analytical redundancy relations and mode-dependent adaptive thresholds, in successfully detecting and isolating faults within multimode electromechanical composite transmission systems. Furthermore, compared to conventional fixed threshold residual evaluation approaches, this method significantly mitigates the occurrence of missed detections, thus verifying the effectiveness and superiority of the proposed method. • The fault diagnosis model for the MM-EMCTS HEV is constructed. • A structural framework is proposed for fault diagnosis in MM-EMCTS HEV, utilizing global redundancy relationships. • A mode-dependent adaptive threshold for residual evaluation is designed based on statistical theory. [ABSTRACT FROM AUTHOR]

Electrified vehicle batteries primarily use lithium-ion batteries. During vehicle operation, the charge and discharge cycles cause battery aging, leading to reduced battery lifespan. This issue is particularly pronounced in nickel-rich nickel cobalt manganese (NCM) cathodes. In addition, when the battery operates at low temperatures, increased internal resistance further reduces its lifespan. The electrification of internal combustion engine vehicles is underway. However, realistically it is not feasible. Therefore, research on hybrid electric vehicles that utilize both engines and motors is necessary. hybrid electric vehicles (HEVs) utilize various motor control strategies according to the state of charge (SoC) and accelerator pedals, which are influenced by various factors including driving patterns and routes. In this study, the energy efficiencies of hybrid and electric vehicles was compared under real-world driving conditions. In the eco driving mode, which is characterized by frequent motor operation, energy-consumption efficiency levels similar to those of electric vehicles were observed. However, during real-world road tests at ambient temperatures, the energy consumption efficiency of electric vehicles deteriorated compared to that of HEVs owing to internal battery resistance. Therefore, in energy efficiency evaluation studies, it is essential to validate the characteristics that reflect the various influencing factors of real-world driving environments. • SoC control strategies for HEVs and EVs were compared based on energy consumption. • The energy consumption of HEVs varies depending on the contribution of the engine. • HEVs and EVs consume energy at the same level. • Unlike EVs, HEVs are less affected by external temperatures. [ABSTRACT FROM AUTHOR]

This current and pioneering work aimed to assess the on-road performance of selected hybrid electric vehicles (HEVs) and electric vehicles (EVs) in local urban road conditions following the World Harmonized Light Vehicles Test Procedure (WLTP) and the chase car protocol. An experimental research design was also implemented to investigate the effects of the different payload conditions on vehicle performance, and corresponding drive cycle patterns for the test vehicles were generated from each on-road test. From the series of these on-road tests, it was revealed that there was high variability in speed profiles, and vehicle speed was generally found to be inversely related to payload weight. The variations in the state of charge, fuel fill-up, and fuel and energy parameters exhibited no significant differences in terms of payload conditions. When compared to both the Canada fuel consumption guide and the US fuel consumption guide, the resulting fuel consumption and energy consumption indicated that the Mitsubishi Outlander PHEV and Mitsubishi iMiEV exceeded energy efficiency standards, unlike the Toyota Prius. Meanwhile, in terms of CO2 emissions, all vehicles demonstrated around 40–70% lower emissions compared to conventional vehicles according to the 2023 estimates of the US Environmental Protection Agency. Being the first of its kind in the Philippines, this study on the on-road performance assessments of HEVs and EVs is essential because it provides empirical data on these vehicles’ actual performance in everyday driving conditions. The data are important for evaluating the potential to address environmental concerns, promote sustainable transportation solutions, influence consumer adoption, and shape government policies. With ongoing improvements in technology and expanding charging infrastructure, HEVs and EVs are poised for significant adoption in the coming years.

Hybrid electric vehicles (HEVs) are considered the most practical option for reducing emissions and fuel consumption of conventionally powered vehicles. Energy management strategies (EMSs) are the core technology of HEVs because of decreasing the cost of the system and limiting its negative effects. Equivalent consumption minimization strategy (ECMS) can achieve instantaneous optimal control and has attracted attention in recent years. In this study, an adaptive equivalent consumption minimization strategy (A-ECMS) based on driving cycle recognition is constructed for a parallel HEV. First, select the standard driving cycle and analyze its characteristic parameters. And then training learning vector quantization (LVQ) neural network-based driving cycle recognizer to achieve an average of 98% accuracy. At last, the optimal equivalent factor (EF) is selected for ECMS by recognizing the current driving cycle. It is jointly simulated and analyzed by AVL CRUISE and MATLAB/Simulink software under NEDC and CHTC-LT driving cycle. The results show that compared with the logic-based EMS, in the NEDC driving cycle the 100 km fuel consumption of A-ECMS decreases by 3.8%, and the battery state of charge (SOC) increases by 1.1%. In the CHTC-LT driving cycle, fuel economy improves by 3.6%, proving the superiority of the A-ECMS.

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Electric vehicles (EV) are promising alternate fuel technologies to curtail vehicular emissions. A modeling framework in a hybrid electric vehicle system with a joint analysis of EV in powering and regenerative braking mode is introduced. Bidirectional DC–DC converters (BDC) are important for widespread voltage matching and effective for recovery of feedback energy. BDC connects the first voltage source (FVS) and second voltage source (SVS), and a DC-bus voltage at various levels is implemented. The main objectives of this work are coordinated control of the DC energy sources of various voltage levels, independent power flow between both the energy sources, and regulation of current flow from the DC-bus to the voltage sources. Optimization of the feedback control in the converter circuit of HEV is designed using an artificial neural network (ANN). Applicability of the EV in bidirectional power flow management is demonstrated. Furthermore, the dual-source low-voltage buck/boost mode enables independent power flow management between the two sources—FVS and SVS. In both modes of operation of the converter, drive performance with an ANN is compared with a conventional proportional–integral control. Simulations executed in MATLAB/Simulink demonstrate low steady-state error, peak overshoot, and settling time with the ANN controller. [ABSTRACT FROM AUTHOR]