Search Results (302)

This paper presents an advanced energy management system (EMS) for optimizing power distribution in a battery/ultracapacitor (UC) hybrid energy storage system (HESS) for electric vehicles (EVs). The proposed EMS accounts for all energy flow scenarios within a practical driving cycle. A regenerative braking control strategy is developed to maximize kinetic energy recovery using an induction motor, efficiently distributing the recovered energy between the UC and battery. Additionally, a power flow management approach is introduced for both motoring (discharge) and braking (charge) operations via bidirectional buck–boost DC-DC converters. In discharge mode, an optimal distribution factor is dynamically adjusted to balance power delivery between the battery and UC, maximizing efficiency. During charging, a DC link voltage control mechanism prioritizes UC charging over the battery, reducing stress and enhancing energy recovery efficiency. The proposed EMS is validated through simulations and experiments, demonstrating significant improvements in vehicle acceleration, energy efficiency, and battery lifespan. Full article

This paper proposes a speed-adaptive autonomous driving path-tracking framework based on the soft actor–critic (SAC) and pure pursuit (PP) methods, named the SACPP controller. The framework first analyzes the obstacles around the vehicle and plans an obstacle-free reference path with the minimum curvature using the hybrid A* algorithm. Next, based on the generated reference path, the current state of the vehicle, and the vehicle motor energy efficiency diagram, the optimal speed is calculated in real time, and the vehicle dynamics preview point at the future moment—specifically, the look-ahead distance—is predicted. This process relies on the learning of the SAC network structure. Finally, PP is used to generate the front wheel angle control value by combining the current speed and the predicted preview point. In the second layer, we carefully designed the evaluation function in the tracking process based on the uncertainties and performance requirements that may occur during vehicle driving. This design ensures that the autonomous vehicle can not only quickly and accurately track the path, but also effectively avoid surrounding obstacles, while keeping the motor running in the high-efficiency range, thereby reducing energy loss. In addition, since the entire framework uses a lightweight network structure and a geometry-based method to generate the front wheel angle, the computational load is significantly reduced, and computing resources are saved. The actual running results on the i7 CPU show that the control cycle of the control framework exceeds 100 Hz. Full article

This paper proposes a hybrid methodology for one-step-ahead torsional torque estimation in an electric drive with an elastic connection. The approach integrates Nonlinear Autoregressive Neural Networks with Exogenous Inputs (NARX NNs) and model-based feedback. The NARX model uses real-time and historical motor speed and torque signals as inputs while leveraging physics-derived torsional torque as a feedback input to refine estimation accuracy and robustness. While model-based methods provide insight into system dynamics, they lack predictive capability—an essential feature for proactive control. Conversely, standalone NARX NNs often suffer from error accumulation and overfitting. The proposed hybrid architecture synergises the adaptive learning of NARX NNs with the fidelity of physics-based feedback, enabling proactive vibration damping. The method was implemented and evaluated on a two-mass drive system using an IP controller and additional torsional torque feedback. Results demonstrate high accuracy and reliability in one-step-ahead torsional torque estimation, enabling effective proactive vibration damping. MATLAB 2024a/Simulink and dSPACE 1103 were used for simulation and hardware-in-the-loop testing. Full article

The presence of toxic gas emissions from conventional vehicles is worrisome globally. Over the past few years, there has been a broad adoption of electric vehicles (EVs) to reduce energy usage and mitigate environmental emissions. The EVs are characterized by limited range, cost, and short range. This prompts the need for hybrid electric vehicles (HEVs). This study describes the conversion of a 2022 Volkswagen Crafter (VW) 35 TDI 340 delivery van from a conventional diesel powertrain into a hybrid electric vehicle (HEV) augmented with synchronous electrical machines (motor and generator) and a BMW i3 60 Ah battery pack. A downsized 1.5 L diesel engine and an electric motor–generator unit are integrated via a planetary power split device supported by a high-voltage lithium-ion battery. A MATLAB (R2024b) Simulink model of the hybrid system is developed, and its speed tracking PID controller is optimized using genetic algorithm (GA) and particle swarm optimization (PSO) methods. The simulation results show significant efficiency gains: for example, average fuel consumption falls from 9.952 to 7.014 L/100 km (a 29.5% saving) and CO₂ emissions drop from 260.8 to 186.0 g/km (a 74.8 g reduction), while the vehicle range on a 75 L tank grows by ~40.7% (from 785.7 to 1105.5 km). The optimized series–parallel powertrain design significantly improves urban driving economy and reduces emissions without compromising performance. Full article

The worldwide drive towards low-carbon transportation has made Hybrid Electric Vehicles (HEVs) a crucial component of sustainable mobility, particularly in areas with limited charging infrastructure. The core of HEV efficiency lies in the Energy Management Strategy (EMS), which regulates the energy distribution between the internal combustion engine and the electric motor. While rule-based and optimization methods have formed the foundation of EMS, their performance constraints under dynamic conditions have prompted researchers to explore artificial intelligence (AI)-based solutions. This paper systematically reviews four main AI-based EMS approaches—the knowledge-driven, data-driven, reinforcement learning, and hybrid methods—highlighting their theoretical foundations, core technologies, and key applications. The integration of AI has led to notable benefits, such as improved fuel efficiency, enhanced emission control, and greater system adaptability. However, several challenges remain, including generalization to diverse driving conditions, constraints in real-time implementation, and concerns related to data-driven interpretability. The review identifies emerging trends in hybrid methods, which combine AI and conventional optimization approaches to create more adaptive and effective HEV energy management systems. The paper concludes with a discussion of future research directions, focusing on safety, system resilience, and the role of AI in autonomous decision-making. Full article

Due to climate change, the electric vehicle (EV) industry is rapidly growing and drawing researchers interest. Driving conditions like mountainous roads, slick surfaces, and rough terrains illuminate the vehicles inherent nonlinearities. Under such scenarios, the behavior of power sources (fuel cell, battery, and super-capacitor), power processing units (converters), and power consuming units (traction motors) deviates from nominal operation. The increasing demand for FCHEVs necessitates control systems capable of handling nonlinear dynamics, while ensuring robust, precise energy distribution among fuel cells, batteries, and super-capacitors. This paper presents a DSMC strategy enhanced with Robust Uniform Exact Differentiators for FCHEV energy management. To optimally tune DSMC parameters, reduce chattering, and address the limitations of conventional methods, a hybrid metaheuristic framework is proposed. This framework integrates moth flame optimization (MFO) with the gravitational search algorithm (GSA) and Fractal Heritage Evolution, implemented through three spiral-based variants: MFOGSAPSO-A (Archimedean), MFOGSAPSO-H (Hyperbolic), and MFOGSAPSO-L (Logarithmic). Control laws are optimized using the Integral of Time-weighted Absolute Error (ITAE) criterion. Among the variants, MFOGSAPSO-L shows the best overall performance with the lowest ITAE for the fuel cell (56.38), battery (57.48), super-capacitor (62.83), and DC bus voltage (4741.60). MFOGSAPSO-A offers the most accurate transient response with minimum RMSE and MAE FC (0.005712, 0.000602), battery (0.004879, 0.000488), SC (0.002145, 0.000623), DC voltage (0.232815, 0.058991), and speed (0.030990, 0.010998)—outperforming MFOGSAPSO, GSA, and PSO. MFOGSAPSO-L further reduces the ITAE for fuel cell tracking by up to 29% over GSA and improves control smoothness. PSO performs moderately but lags under transient conditions. Simulation results conducted under EUDC validate the effectiveness of the MFOGSAPSO-based DSMC framework, confirming its superior tracking, faster convergence, and stable voltage control under transients making it a robust and high-performance solution for FCHEV. Full article

Hybrid tractors, as an efficient and environmentally friendly power system, are gradually becoming an important technical choice in the agricultural field. Compared to conventional powertrain systems, hybrid electric powertrains can achieve a 15–40% reduction in fuel consumption. By optimizing the engine operating range and incorporating electric-only driving modes, these systems further contribute to a 20–35% decline in CO₂ emissions, along with a significant mitigation of nitrogen oxides (NOx) and particulate matter (PM) emissions. In this paper, the energy management technology of hybrid tractors is reviewed, with emphasis on the energy scheduling between the internal combustion engine and electric motor, the optimization control algorithm, and its practical performance in agricultural applications. Firstly, the basic configuration and working principle of hybrid tractors are introduced, and the cooperative working mode of the internal combustion engine and electric motor is expounded. Secondly, the research progress of energy management strategies is discussed. Then, the application status and challenges of hybrid power systems in agricultural machinery are discussed, and the development trend of hybrid tractors in the fields of intelligence, low carbonization, and high efficiency in the future is prospected. This paper extracts many experiences and methods from the references over the years and provides a comprehensive evaluation. Full article

T-type three-level inverters have been extensively utilized in renewable energy generation, motor drive systems, and other power conversion applications. However, failures in semiconductor devices critically reduce the operational reliability of power conversion systems. While significant progress has been made in the diagnosis of single-switch open-circuit (OC) faults, the precise location and detection of simultaneous double-switch OC faults remain challenging. Therefore, this paper proposes a fault diagnosis method, integrating an improved adaptive sliding mode observer (IASMO) and dynamic current threshold detection. First, the IASMO is constructed through the hybrid logic dynamic model, achieving accurate and rapid estimation of phase currents. Then, integrating estimated with actual currents accomplishes the design of detection variables and adaptive thresholds. Subsequently, fault location variables are formulated to achieve accurate localization of both single-switch and double-switch faults. Finally, Simulation and experimental results demonstrate that the proposed method effectively identifies 18 types of OC faults within 75% of the current cycle, with high efficiency and robustness. Full article

Power-split hybrid electric vehicles (power-split-HEVs) exhibit significant engine torque fluctuations due to their mechanical coupling with the driveline, leading to pronounced torsional vibration issues in the drive shaft. This study investigates an active torsional vibration suppression strategy based on drive motor control. First, a dynamic model of the power-split-HEV driveline is established, and its intrinsic characteristics are analyzed. Subsequently, an engine excitation torque model is developed to identify the dominant response orders, while a vehicle dynamics model is constructed to elucidate the torsional vibration mechanisms in both hybrid and pure electric driving modes. Next, a torsional vibration feedback control framework is proposed, utilizing the electric motor as a secondary-channel torque disturbance compensator. Furthermore, a novel frequency-decoupled dual-loop control framework is proposed, with rigorous derivation of the sufficient conditions for decoupling. Based on this framework, two distinct vibration suppression algorithms are developed for the secondary-loop controller, each tailored for specific operational modes. Finally, the proposed algorithms are validated through simulation and hardware-in-the-loop (HIL) testing. The results demonstrate a torque fluctuation suppression ratio of up to 72.2%, confirming that the active suppression algorithm effectively mitigates driveline torsional vibration induced by engine harmonic torque disturbances. Full article

Regenerative braking systems (RBS enhance energy efficiency and range in electric vehicles (EVs) by recovering kinetic energy during braking for storage in batteries or alternative systems. This literature review examines RBS advancements from 2005 to 2024, focusing on system design, control strategies, energy storage technologies, and the impact of external and kinematic factors on recovery efficiency. Based on a systematic analysis of 89 peer-reviewed articles from Scopus, it highlights a shift from basic PID controllers to advanced predictive algorithms like Model Predictive Control (MPC) and machine learning approaches. Technologies such as brake-by-wire and in-wheel motors improve safety and stability, with the latter excelling in all-wheel-drive setups over single-axle configurations. Hybrid Energy Storage Systems (HESS), combining batteries with supercapacitors or kinetic accumulators, address power peak demands, though cost and complexity limit scalability. Challenges include high computational requirements, component reliability in harsh conditions, and lack of standardized testing. Research gaps involve long-term degradation, autonomous vehicle integration, and driver behavior effects. Future work should explore cost-effective HESS, robust predictive controls for autonomous EVs, and standardized frameworks to enhance RBS performance and support sustainable transportation. Full article

Pure electric tractors face challenges in complex operating conditions, including the excessive peak motor torque caused by frequent start–stop cycles and insufficient energy utilization. To address these issues, this study proposes a hydraulic–electric hybrid drive system for tractor traveling systems which is based on a range-extended hybrid architecture. By combining the high-torque characteristics of hydraulic drive systems with the high control precision of electric motors, a hydraulic–electric dual-power coupling model was constructed. A logic-threshold-based operating mode division strategy and a hierarchical braking energy recovery mechanism were developed. The start–stop control dynamics and energy recovery efficiency of the system during plowing and transport operations were thoroughly analyzed. The simulation results demonstrate that while maintaining its acceleration and braking performance, the proposed system achieves 18.8% and 35.7% reductions in its peak motor torque during plowing and transport operations, respectively. Its braking energy recovery efficiency improved to 48.3% and 66.4% in the two scenarios; 18.5% and 25.7% reductions in overall energy consumption were seen. Full article

This paper evaluates the performance of strategies based on finite-control-set model predictive control (FCS-MPC) aimed at reducing or fixing the converter switching frequency or decreasing the spread of the harmonic spectrum in multilevel hybrid cascade converters (HCCs). These properties are desirable for medium- to high-voltage applications, where minimizing switching losses is crucial, as well as for applications employing passive filters, where resonance modes can be excited. The strategies evaluated are input restriction, notch filtering, period control, and PWM restriction. Key aspects considered in this work are (i) the evaluation of the steady-state and transient performance of FCS-MPC strategies proposed for two-level converters in a multilevel topology, and (ii) the evaluation of the computational cost associated with the implementation of these strategies on a multilevel converter with a high number of available inputs. As a typical application, the study is carried out employing a five-level HCC experimental prototype driving an induction motor through indirect vector control. To perform a fair comparison between the strategies, a control platform based on a cost-effective Zynq system on chip is proposed, which allows for achieving the hard timing constraints imposed by FCS-MPC strategies. The results show that the PWM restriction strategy achieves the best steady-state performance among the evaluated strategies, with an error 400 times smaller than that of the second-best strategy (input restriction), with an average switching frequency of 962.5 Hz, which differs from the desired average frequency by 3%, and a maximum difference in power distribution between modules of 0.8%. In addition, the system-on-chip hardware achieves a competitive execution time of 46 μs when the ARM Cortex solution is implemented and 20 μs when the ARM Cortex–FPGA solution is used instead, employing the 512 inputs available in the FCS-MPC algorithm. The studies, performed in steady-state and transient regimes, confirm (i) the feasibility of the evaluated algorithms in an HCC topology and (ii) the feasibility of the control platform for implementing high-computational-burden algorithms with a low sampling time. Full article

Voltage Source Inverters (VSIs) are essential in variable-speed drive applications, where Pulse-Width Modulation (PWM) signals are typically generated using a fixed-carrier (FC) signal. Increasing the FC frequency helps smoothen the inverter’s output current, improving motor performance. However, this comes at the expense of increased switching losses, reduced efficiency, and potential thermal management challenges. The Hybrid Carrier-based PWM (HCPWM) technique presents an alternative by dynamically alternating between two sawtooth carrier signals with different frequencies. This method aims to achieve higher efficiency without compromising system performance. However, selecting optimal carrier pairs to maximize efficiency across various speed and load conditions while maintaining total harmonic distortion within acceptable limits remains a challenge. This study provides a comprehensive experimental evaluation of the HCPWM approach, benchmarking it against conventional FCPWM. The results demonstrate that HCPWM enhances energy efficiency under all tested conditions, making it a viable and cost-effective solution for VSI-driven motor applications without introducing additional system cost or complexity. Full article

As electric vehicles (EVs) are growing, the fault diagnosis in their drive motor becomes more important to have optimal performance and safety. Traditional fault detection methods suffer mainly from high false positive and false negative rates, computational complexity, and lack of transparency in decision-making methods. In addition, existing models are also heavy and inefficient. A lightweight framework for fault diagnosis in EV drive motors is presented with the aid of Recursive Feature Elimination with Cross-Validation (RFE-CV), parameter optimization, and in-depth preprocessing. We further optimize the models and their combination to a hybrid Soft Voting Classifier. These techniques were applied to a dataset of 40,040 data entries that had been simulated by a Variable Frequency Drive (VFD) model. We evaluated eight machine learning models, and our proposed Soft Voting Classifier has the highest test accuracy of 94.52% and a Kappa score of 0.9210 on diagnostic performance. Also, the model has minimal memory usage and low inference latency. In addition, Local Interpretable Model-Agnostic Explanations (LIME) were used to improve transparency and gain an understanding of decisions made through the Soft Voting Classifier. Also, the framework was validated by an additional real-world dataset, thereby further confirming its robustness and consistency in performance for different conditions, which indicates the generalizability of the framework in real-world applications. RFE-CV is found to be very effective in feature selection and helps to construct a lightweight and cost-effective ensemble voting model for enhancing fault diagnosis for EV Drive Motors, overcoming its unsatisfactory transparency, accuracy, and computational efficiency. Finally, it contributes to the development of safer and more reliable EV systems through the development of models supervised on fewer features to give the computing time that is a little lighter without compromising its diagnostic performance. Full article

This paper proposes a hybrid adaptive robust observation (HARO)-based sensorless control strategy of a five-phase fault-tolerant permanent-magnet (FPFTPM) motor for electric agricultural equipment applications under various operating conditions, including fault conditions. Regarding fault-tolerant sensorless control, the existing studies usually treat fault-tolerant control and sensorless control as two independent units rather than a unified system, which makes the algorithm complex. In addition, under the traditional fault-tolerant algorithm, the system needs to switch after diagnosis when the fault occurs, which leads to a degraded sensorless control performance. Hence, this paper proposes a fault-tolerant sensorless control strategy that can achieve the whole speed range without fault-tolerant switching. At zero/low speed, a disturbance adaptive controller (DAC) architecture is developed by treating phase faults as system disturbances, where robust controllers and extended state observer (ESO) collaboratively suppress speed and position errors. At medium/high speeds, this paper provides a steady-healthy SMO, which combines the enhanced observer and universal phase-locked loop (PLL) without phase compensation. With above designs, the proposed strategy can significantly improve the estimated accuracy of rotor position under normal conditions and fault circumstances, while simplifying the complexity of the fault-tolerant sensorless algorithm. Furthermore, the proposed strategy is verified based on the experimental platform of the FPFTPM motor drive system. The experimental results show that compared with the traditional method, the torque ripple and position error are reduced by nearly 20% and 60%, respectively, at zero-low speed and medium-high speed, and the torque ripple is reduced by 55% during fault operation, which verifies the robustness and effectiveness of the proposed method. Full article