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The shift towards sustainable mobility has increased the demand for energyefficient and environmentally friendly vehicles, such as Hybrid Electric Vehicles (HEVs). However, designing HEVs that simultaneously meet high product quality, minimize environmental impact, and adhere to social responsibility standards remains a complex challenge. This study presents a decision-making model aimed at integrating these key sustainability criteria into the design and improvement of HEVs. The model combines three indices: the Aggregated Quality Index (AQI), Environmental Impact Index (EII) based on Life Cycle Assessment (LCA), and Social Responsibility Index (SRI), to assess and compare different HEV prototypes. By processing customer expectations, environmental impacts, and social responsibility considerations, the model predicts the optimal prototype that balances quality, environmental sustainability, and social standards. The findings demonstrate that applying this model can significantly enhance decision-making in sustainable vehicle development and support the creation of HEVs that better align with global sustainability goals. This approach has practical implications for automotive manufacturers aiming to innovate responsibly in the green mobility sector. [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.

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]

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]

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.

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.

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.

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.

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]

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]

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.

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]

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]

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 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]

• 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]

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.

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]

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]

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.

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]

With the development of recent artificial intelligence technology, especially after the great success of AlphaGo, there has been a growing interest in applying reinforcement learning (RL) to solve energy management strategy (EMS) problems for hybrid electric vehicles. However, the issues of current RL algorithms including deployment inefficiency, safety constraint, and simulation-to-real gap make it inapplicable to many industrial EMS tasks. With these in mind and considering the fact that there exists many suboptimal EMS controllers which can generate plentiful amounts of interactive data containing informative behaviors, an offline RL training framework that tries to extract policies with the maximum possible utility out of the available offline data is proposed. Furthermore, with connected vehicle technology standard in many new cars, rather than bringing all the data to the storage and analytics, a scheduled training framework is put forward. This cloud-based approach not only alleviates the computational burden of edge devices, but also more importantly provides a deployment-efficient solution to EMS tasks that have to adapt to changes of driving cycle. To evaluate the effectiveness of the proposed algorithm on real controllers, a hardware-in-the-loop (HIL) test is performed and the superiority of the proposed algorithm in contrast to dynamic programming, behavior cloning, rule-based, and vanilla off-policy RL algorithms is given. [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]

The paper proposes a Hybrid Electric Vehicle (HEV) design based on the installation of a fuel cell (FC) module in the existing Daewoo Tico electric vehicle to increase its range in urban areas. Installing an FC module supplied by a 2 kg hydrogen tank would not significantly increase the mass of the electric vehicle, and the charging time of the hydrogen tank is lower than the battery charging time. For design analysis, a model was created in the MATLAB/Simulink software package. The model simulates vehicle range at different HEV speeds for Absorbent Glass Mat (AGM) and Proton Exchange Membrane Fuel Cell (PEMFC) power sources. The greatest anticipated benefit derived from the model analysis relates to velocities ranging from 20 km/h to 30 km/h, although the optimal HEV velocity in an urban area is in the range of 30 km/h to 40 km/h. The results indicate that this conversion of Electric Vehicle (EV) to HEV would bring a benefit of 87.4% in terms of vehicle range in urban areas. Therefore, the result of the conversion in this case is a vehicle with sub-optimal characteristics, which are nevertheless very close to optimal. [ABSTRACT FROM AUTHOR]

A power management framework for hybrid electric vehicles (HEVs) is proposed based on deep reinforcement learning (DRL) with a Long Short-Term Memory (LSTM) network to minimize the fuel consumption through determining the power distribution between the two propulsion sources, the internal combustion engine (ICE) and the electric motor (EM). DRL is effective for handling the high-dimensional state and action spaces in the HEV power management problem, and the LSTM structure leverages temporal dependencies of input information, providing internal state predictions automatically without introducing extra state variables. This technique is entirely online, meaning that the framework is constructed in real time during the training phase, independently of a prior knowledge of driving cycles. The learned information stored in the LSTM network is utilized efficiently, and the computational speed is enhanced by making multiple predictions simultaneously in each step. Simulation over various driving cycles demonstrates the efficacy of the proposed framework in fuel economy improvement. [ABSTRACT FROM AUTHOR]

This paper investigates an adaptive dynamic programming (ADP)-based energy management control strategy for a series-parallel hybrid electric vehicle (HEV). This strategy can further minimize the equivalent fuel consumption while satisfying the battery level constraints and vehicle power demand. Dual heuristic dynamic programming (DHP) is one of the basic structures of ADP, combining reinforcement learning, dynamic programming (DP) optimization principle, and neural network approximation function, which has higher accuracy with a slightly more complex structure. In this regard, the DHP energy management strategy (EMS) is designed by the backpropagation neural network (BPNN) as an Action network and two Critic networks approximating the control policy and the gradient of value function concerning the state variable. By comparing with the existing results such as HDP-based and rule-based control strategies, the equivalent consumption minimum strategy (ECMS), and reinforcement learning (RL)-based strategy, simulation results verify the robustness of fuel economy and the adaptability of the power-split optimization of the proposed EMS to different driving conditions. [ABSTRACT FROM AUTHOR]

Vehicles have become an integral part of the modern era, but unfortunately conventional vehicles consume non-renewable energy resources which have associated issue of air pollution. In addition to that, global warming and the shortage of fossil fuels have provided motivation to look for alternative to conventional vehicles. In the recent era, hybrid electric vehicle (HEV) is becoming popular due to their attractive features of energy saving and less air pollution. This paper aimed at designing a hybrid electric vehicle control scheme with an effective energy management system under varying load conditions. The proposed hybrid energy storage system of the HEV in this work consists of two energy sources: (1) main source: fuel cell and (2) auxiliary source: ultra-capacitor and battery. Furthermore, a fuzzy logic-based nonlinear controller has been developed to effectively control the management of energy sources according to load demand. The proposed HEV model has been developed using MATLAB/Simulink environment and the simulation results verify that the proposed fuzzy logic-based controller provides better voltage regulation and efficient energy management for the complete drive range of the HEV. Moreover, a comparative study of the proposed controller with existing control methods affirms its better reference tracking with minimum overshoot, faster convergence, better stability, and enhanced regulation of DC bus bar voltages under varying load conditions. [ABSTRACT FROM AUTHOR]

Plug-in hybrid electric vehicles (PHEVs) are commonly operated with high-voltage (HV) components due to their higher power availability compared to 48 V-systems. On the contrary, HV-powertrain components are more expensive and require additional safety measures. Additionally, the HV system can only be repaired and maintained with special equipment and protective gear, which is not available in all workshops. PHEVs based on a 48 V-system level can offer a reasonable compromise between the greenhouse gas (GHG) emission-saving potential and cost-effectiveness in small- and medium-sized electrified vehicles. In our study, the lifecycle emissions of the proposed 48 V PHEV system were compared to a conventional vehicle, 48 V HEV, and HV PHEV for individual driving use cases. To ensure a holistic evaluation, the analysis was based on measured real-driving cycles including Global Position System (GPS) map-matched slope profiles for a parallel hybrid. Optimal PHEV battery capacities were derived for the individual driving use cases. The analysis was based on lifecycle emissions for 2020 and 2030 in Europe. The impact analysis revealed that 48 V PHEVs can significantly reduce GHG emissions compared to vehicles with no charging opportunity for all use cases. Furthermore, the findings were verified for two vehicle segments and two energy mix scenarios. The 48 V PHEVs can therefore complement existing powertrain portfolios and contribute to reaching future GHG emission targets. [ABSTRACT FROM AUTHOR]

Recent developments in emissions regulations are pushing Non-Road Mobile Machineries manufacturers towards the adoption of more efficient solutions to reduce the amount of pollutants per unit of work performed. Electrification can be a reasonable alternative to traditional powertrain to achieve this goal. The higher complexity of working machines architectures requires, now more than ever, better design and testing methodologies to better integrate electric systems into mechanical and hydraulic layouts. In this work, the attention focused on the use of a Hardware in the Loop (HIL) approach to test performance of an energy management strategy (called load observer) developed specifically for an orchard tractor starting from field characterization. The HIL bench was designed to replicate a scaled architecture of a parallel hybrid electric tractor at mechanical and electrical level. The vehicle behavior was simulated with a personal computer connected on the CAN BUS network designed for the HIL system. Several tasks were simulated starting from data gathered during field measurements of a daily use of the machine. Results showed good performance in terms of load split between the two power sources and stability of the speed control although the variability of the applied load.

The electric car is a solution designed as a zero-emission vehicle which is an alternative to reducing air pollution. There are various types of electric cars, this research focuses more on Hybrid Electric Vehicle (HEV). HEV is a vehicle that has at least two different energy sources. The most common combination today is the Internal Combution Engine (ICE) and an electric battery. HEV uses ICE with a smaller capacity than conventional vehicles, this results in more efficient fuel use. In HEV, there are several problems, including the response from ICE which is less than optimal when there is an increase in speed. ICE as the prime mover has a smaller capacity than conventional vehicles because it is assisted by the performance of the DC motor. When ICE is unable to maintain speed, the DC motor will help provide power. Therefore, it is necessary to regulate traction on a DC motor to help ICE achieve the desired speed, especially when there is an increase in speed. This study uses a neuro-fuzzy control method which has the advantage of being able to adapt to changes in parameters in the system. HEV itself requires a fast response, therefore, a predictive controller is used in order to predict the future torque value

This review article describes the basic concepts of electric vehicles (EVs) and explains the developments made from ancient times to till date leading to performance improvement of the electric vehicles. It also presents the thorough review of various components and energy storage system (ESS) used in electric vehicles. The main focus of the paper is on batteries as it is the key component in making electric vehicles more environment-friendly, cost-effective and drives the EVs into use in day to day life. Various ESS topologies including hybrid combination technologies such as hybrid electric vehicle (HEV), plug-in HEV (PHEV) and many more have been discussed. These technologies are based on different combinations of energy storage systems such as batteries, ultracapacitors and fuel cells. The hybrid combination may be the perspective technologies to support the growth of EVs in modern transportation. The advanced charging systems may also play a major role in the roll-out of electric vehicles in the future. The general strategies of advanced charging systems are explained to highlight the importance of fast charging time with high amount of power and its cost-effectiveness for electric vehicles. Furthermore, the battery pack designing calculation is briefly explained along with all mechanical, electrical and environmental battery tests, which helps in the evaluation of batteries. Moreover, this paper also has a brief summarizing with the help of a flow chart, which clearly demonstrates all the parts of electric vehicles in a much simpler way.

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.

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.

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.

This paper investigates the impact of environmental conditions on the possible output power of photovoltaic (PV) installations on top of hybrid electric vehicles (HEVs) and battery-powered electric vehicles (BEVs). First, we discuss the characteristics and behavior of PV cells in order to provide an understanding of the energy source that we aim to integrate into vehicles. Second, we elaborate on how PV cells and panels can be simulated to estimate the potential extension of the electrical driving range (ERE) of BEVs and HEVs. In particular, we concentrate on the impact of the vehicle's curved roof surface on the possible output of the PV installation. In this research, we present considerations for vehicles in both parking and driving conditions. More precisely, we demonstrate how the frequently changing environmental conditions that occur while driving represent significant challenges to the control of the operating voltage of PV cells. As the area for deploying PV cells on top of an electric vehicle is limited, attention needs to be paid to how to optimize and maximize the degree of efficiency of PV-powered electric vehicles. [ABSTRACT FROM AUTHOR]

The performance of hybrid electric vehicles (HEVs) greatly depends on the various sub-system components and their architecture, and designers need comprehensive reviews of HEVs before vehicle investigation and manufacturing. Simulations facilitate development of virtual prototypes that make it possible to rapidly see the effects of design modifications, avoiding the need to manufacture multiple expensive physical prototypes. To achieve the required levels of emissions and hardware costs, designers must use control strategies and tools such as computational modeling and optimization. However, most hybrid simulation tools do not share their principles and control logic algorithms in the open literature. With this motivation, the author developed a hybrid simulation tool with a rule-based topology. The major advantage of this tool is enhanced flexibility to choose different control and energy management strategies, enabling the user to explore a wide range of hybrid topologies. The tool provides the user with the ability to modify any sub-system according to one's own requirements. In addition, the author introduces a simple logic control for a rule-base strategy as an example to show the flexibility of the tool in allowing the adaptation of any logic algorithm by the user. The results match the experimental data quite well. Details regarding modeling principle and control logic are provided for the user's benefit. [ABSTRACT FROM AUTHOR]