Hybrid Electric Vehicle System Research Articles

Super-twisting sliding mode controller for energy storage system of a novel multisource hybrid electric vehicle: Simulation and hardware validation

The rapid depletion of fossil fuels, oil, and natural gas due to their excessive use is a source of motivation for finding alternative solutions. Global warming is also an important issue caused by the emission of harmful gases. Consequently, extensive research has been conducted on Fuel Cell-based Hybrid Electric Vehicles (FCEVs) to address the twin challenges of resource depletion and harmful emissions. This study presents a novel model comprising of four sources i.e. Fuel Cell (FC), Photo Voltaic panel (PV), battery, and Supercapacitor (SC). The Proton exchange membrane fuel cell (PEMFC) in which hydrogen is used as fuel is the primary source whereas the other three sources act as secondary sources. These sources are interconnected via DC converters to a DC bus. To regulate this Hybrid Energy Storage System (HESS), a Super-Twisting Sliding Mode Controller (ST-SMC) has been developed, ensuring global stability through the application of Lyapunov criteria. In addition, the proposed controller is compared with a conventional Sliding Mode Controller (SMC) and Integral Sliding Mode Controller (ISMC). Both simulation (MATLAB/Simulink 2021b) and hardware tests (MicroLabBox dSPACE RTI1202) confirm the system's stability, resilience, and efficient functionality across various dynamic scenarios. Comparative analysis between the three demonstrates the superiority of ST-SMC over the SMC and ISMC, as verified by the results.

Bidirectional DC - DC Converter Topology for Electric Vehicles Using Fuzzy Logic Controller

This project organizes an application of hybrid electric vehicle systems operated with novel designed bidirectional dc-dc converter (BDC) which interfaces a main energy storage (ES1), an auxiliary energy storage (ES2) and dc bus of different voltage levels. Proposed BDC converter can operate both step up and step-down mode. In which step up mode represents low voltage dual source -powering mode and step-down mode represents high voltage dc link energy –regenerating mode, both the modes are operated under the control of bidirectional power flow. This model can independently control power flow between low voltage dual source buck/boost modes. Here in, the circuit configuration, operation, steady-state analysis, and closed-loop control of the proposed BDC are discussed according to its three modes of power transfer. In this project fuzzy logic controller is used and also system results are validating through MATLAB/SIMULINK software. Index Terms—Bidirectional dc/dc converter (BDC), dual battery storage, hybrid electric vehicle, Fuzzy logic controller.

Hybrid Electric Vehicle Powertrain Mounting System Optimization Based on Cross-Industry Standard Process for Data Mining

The meticulously engineered powertrain mounting system of hybrid electric vehicles plays a critical role in minimizing vehicle vibrations and noise, thereby enhancing the longevity of vital powertrain components. However, developing and designing such a system demands substantial time and financial investments due to intricate analysis and modeling requirements. To tackle this challenge, this study integrates data mining technology into the design and optimization processes of the powertrain mount system. The research focuses on the powertrain mounting system of a transverse four-cylinder hybrid electric vehicle, employing the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology to establish a data-mining prediction model for mounting stiffness. This model utilizes three data mining algorithms—Multi-SVR, MRTs, and MLPR—to assess their predictive accuracy concerning mounting system stiffness estimation. A comparative analysis reveals that the MRTs algorithm outperforms others as the most effective prediction model. The proposed predictive model elucidates the quantifiable correlation between vibration isolation performance and installation stiffness, overcoming complexities associated with traditional modeling approaches. Applying this model in powertrain mounting system design showcases the efficacy of the CRISP-DM-based approach, significantly enhancing design efficiency without compromising prediction accuracy.

Research on the Performance Characteristics of a Waste Heat Recovery Compound System for Series Hybrid Electric Vehicles

In this paper, a waste heat recovery compound system for series hybrid electric vehicles is established. The existing components of vehicle air conditioning are used in the organic Rankine cycle (ORC) to realize miniaturization. The waste heat recovery compound system is constructed using GT-SUITE, and the objective of the analysis is to increase the power output and engine thermal efficiency increase ratio (ETEIR). The effects of the expander speed, pump speed, working fluid mass flow rate, and working fluid type on the waste heat recovery compound system are analyzed. The simulation results show that the optimal schemes for the ORC system and compound system corresponding to the expander speed and pump speed are 1000 pm, 2500 rpm, 1200 rpm, and 2500 rpm, respectively. Compared with the ORC system, the maximum power output of the compound system with the same working fluid in three states (1500 rpm, 2500 rpm, and 3500 rpm) of the engine is increased by 21.67%, 24.05%, and 28.23%, respectively. Working fluid supplies of 0.4 kg/s, 0.4 kg/s, and 0.6 kg/s in the three engine states are also considered the best solutions. The working fluid R1234yf and R1234ze are the preferred choices for a waste heat recovery compound system, which have a high system power output and ETEIR and are environmentally friendly.

Bi-LSTM predictive control-based efficient energy management system for a fuel cell hybrid electric vehicle

Energy management systems (EMS) in hybrid electric vehicles play a vital role in achieving optimal energy utilization, improved fuel efficiency, lower emissions, and improved vehicle performance. Thus, this paper proposes a Bidirectional Long-Term Memory (Bi-LSTM) model based on an efficient EMS for a hybrid electric vehicle that manages the powertrain elements of the IC engine, electric motor(s), fuel cell, and energy storage system. The proposed EMS also continuously monitors various parameters, including vehicle speed, driver inputs, battery state of charge, engine load, and environmental conditions, to make real-time decisions on power distribution and operation modes. Further, the proposed system leverages the capabilities of the Bi-LSTM model to capture intricate temporal dependencies and bidirectional context in driving patterns using the real-time dataset. Empirical studies are conducted to substantiate the efficacy of the proposed energy management strategy vis-à-vis diverse controllers, encompassing Model Predictive Control and Sliding Mode Control. Real-world driving data is employed as the basis for these investigations, aiming to provide a robust assessment of the proposed energy management approach in practical scenarios. Results demonstrate the efficacy of the suggested methodology compared to traditional EMS approaches, showcasing improvements in energy utilization and reduced environmental impact. Finally, this research emphasizes a comparative analysis of the proposed topology with the existing approaches in a real-world fuel cell hybrid electric vehicle system. The findings provide insights into the feasibility of deploying intelligent EMS strategies for improved sustainability and efficiency in a modern energy-efficient environment.

KPI-related monitoring approach for powertrain system in hybrid electric vehicles

This paper introduces a novel monitoring method related to key-performance-indicators (KPIs), specifically tailored for the hybrid electric vehicle (HEV) powertrain system. The proposed method establishes a new KPI that better reflects the performance of the HEV powertrain system. Through the application of partial least squares and contribution plot method, it excels in minimizing data scale and precisely monitoring HEV powertrain faults. Diverging from current methodologies, this method demands minimal prior knowledge and solely relies on previously observed HEV data. The simulation results demonstrate the method's capability to detect engine and motor overheating faults while accurately diagnosing their root causes. In summary, this innovative method marks a significant departure from existing approaches, providing a robust and powerful tool for state monitoring in the HEV powertrain system.

A dynamic programming approach for energy management in hybrid electric vehicles under uncertain driving conditions

ABSTRACT This paper addresses the limited adaptability and the computational burden of energy management systems (EMSs) for hybrid electric vehicles (HEVs) implemented via dynamic programming (DP)-based approaches. First, a deterministic dynamic programming (DDP) framework is presented to solve HEV EMS problems subject to a specific driving cycle. To address this limitation, an improved DDP approach, integrating the actual travelled position of the vehicle into the control law, is proposed. This way, a given DDP-based EMS can be applied to all the driving cycles, yet still measured on the same road. Stochastic dynamic programming (SDP)-based EMSs are also developed and prove to be more adaptive to driving scenarios completely different from the ones used for their computation. Real-world driving cycles are employed in all the presented cases, while a reduced HEV powertrain model is used to alleviate the typical DP computational burden.

Optimization of the energy management system in hybrid electric vehicles considering cabin temperature

The air conditioning (AC) system provides passengers with a comfortable temperature environment and is one of the main energy consumers in modern plug-in hybrid electric vehicles (PHEV). However, the AC system is often overlooked when designing the energy management system (EMS) for PHEVs. To improve the energy efficiency of PHEVs, this paper integrates the Genetic Simulated Annealing Algorithm (GASA) with fuzzy control to propose an energy management system for PHEVs that considers the internal temperature of the vehicle. First, considering the uncertainty of the vehicle's driving environment, an optimized Interval Type-2 Fuzzy Controller (IT2-FC) was designed for real-time torque distribution. Second, an iteratively modified genetic simulated annealing algorithm was used to optimize control parameters, correcting the defect of genetic algorithms tending to get trapped in local optima and lingering around optimal positions. Then, considering the energy consumption of the AC system, a vehicle-integrated thermal management model oriented towards control was established. Lastly, the performance of the proposed EMS was discussed. The results show that, compared to the rule-based methods, the proposed EMS reduces fuel consumption by 17.77% and 17.727% in cooling and heating modes, respectively, and compared to the A-ECMS methods, it reduces fuel consumption by 7.38% and 6.31% in cooling and heating modes, respectively. At the same time, the proposed EMS ensures thermal comfort in the cabin. In cooling mode, the traditional rule-based strategy failed to maintain the cabin temperature within the preset range, while the proposed EMS showed significant improvement. In heating mode, the EMS reaches and maintains the cabin's set temperature 35% faster than the rule-based strategy, and 8.75% faster than the A-ECMS strategy.

Artificial intelligence based robust nonlinear controllers optimized by improved gray wolf optimization algorithm for plug-in hybrid electric vehicles in grid to vehicle applications

The paper proposes a control strategy for a plug-in hybrid electric vehicles (PHEVs) system that integrates a photovoltaic (PV) based renewable energy system (RES) and battery and supercapacitor (SC) based energy storage systems (ESS). In the proposed system, a current fed converter is implemented for the photovoltaic and supercapacitor which is a power electronic converter that operates by controlling the current flow in the circuit. The benefit of these converters is that core saturation and shot through cannot lead to device failure or half cycle symmetry. This is a feature of silicon controlled rectifier (SCR) based converters and is a key factor in the increased durability of these converters. A power bi-directional converter is employed for the battery in the EV system. This converter enables the battery to both charge and discharge power as required by the system which facilitates the energy flow between the battery and the rest of the system, allowing efficient energy management and utilization within the plug-in hybrid electric vehicles. Additionally, to efficiently utilize the photovoltaic system, an artificial neural network (ANN) is employed to determine the maximum power points (MPPs). A robust nonlinear higher order super twisting sliding mode controller (STSMC) and integral terminal sliding mode controller (ITSMC) have been designed for PHEVs to reduced chattering phenomena. Tables V to X provides a detailed comparison of error performance metrics and transient response characteristics for the state of charge of a renewable energy system and energy storage systems under the control of the optimized STSMC and ITSMC. The global asymptotic stability of the control approach is verified using Lyapunov stability analysis. Finally, MATLAB/Simulink and a hardware-in-the-loop (HIL) experiments are done to show the behavior of proposed system (Pirouzi et al., 2022).

Dynamic Switching Control of Power-Split Hybrid Electric Vehicles Based on Time Delay Prediction and Interference Compensation

The deterioration of vehicle dynamics caused by the simultaneous action of time delay and interference is a key issue for the switching control system in a power-split hybrid electric vehicle (PS-HEV). This paper presents an innovative compound control method based on extended state observer (ESO) incorporating time delay during the mode transition process. First, the coupling effect of time-varying delay and external disturbance during the typical mode switching process is analyzed in depth, and the double deterioration mechanism of the two coupling effects on vehicle dynamics and ride comfort is revealed. To accurately estimate the system disturbance under the influence of time delay, an unconventional ESO is designed further by adopting the Markov chain time delay prediction model, and the main tool for interference observation is selected by comparing the practicability, reachable bandwidth and observation accuracy. Moreover, the stable mode switching performance and interference suppression are thoroughly investigated, and the smooth transmission of multi-power is realized through the multi-module fusion design of the compound coordinated controller. Finally, the hardware-in-the-loop test is implemented to validate the effectiveness of the control strategy, which also provides a novel idea and lays a theoretical foundation for the optimal design of the PS-HEV controller.

Hybrid Electric Vehicle Torque Distribution Control Method and System with Environmental Temperature Protection Battery Integration

Hybrid Electric Vehicle Torque Distribution Control Method and System with Environmental Temperature Protection Battery Integration

Approximate Global Energy Management Based on Macro–Micro Mixed Traffic Model for Plug-in Hybrid Electric Vehicles

A multi-scale physical process management system is presented in this paper, taking the plug-in hybrid electric vehicle system as the physical interface connecting the macro traffic system to the micro energy conversion process, with the ultimate goal of global energy management in the full temporal–spatial domain for autonomous plug-in hybrid electric vehicles. This novel method adopts a macro traffic flow model at a large time scale, in which only the initial conditions and the traffic information of key road sections are required, and a car following model at the micro scale. Furthermore, local replanning of energy management is carried out by adjusting the power threshold and the efficiency weight through the type of reinforcement learning that is closest to human learning, once a short term speed disturbance is induced by unknown disturbances in the macro traffic flow. Due to the nonlinear relationship between speed fluctuation and power fluctuation, it is necessary to map the vehicle speed and acceleration characteristics to the power characteristics, instead of directly utilizing the traffic model characterized by the speed and acceleration characteristics. The results show that novel multi-scale physical management can achieve a smaller deviation from the global optimal solution and enhanced robustness of global energy management. Additionally, close coupling between the dynamic characteristics of vehicle components and speed fluctuation ensures correct tracking of the optimized target value.

Effective Energy Management Strategy with Model-Free DC-Bus Voltage Control for Fuel Cell/Battery/Supercapacitor Hybrid Electric Vehicle System

This article presents a new design method of energy management strategy with model-free DC-Bus voltage control for the fuel-cell/battery/supercapacitor hybrid electric vehicle (FCHEV) system to enhance the power performance, fuel consumption, and fuel cell lifetime by considering regulation of DC-bus voltage. First, an efficient frequency-separating based-energy management strategy (EMS) is designed using Harr wavelet transform (HWT), adaptive low-pass filter, and interval type–2 fuzzy controller (IT2FC) to determine the appropriate power distribution for different power sources. Second, the ultra-local model (ULM) is introduced to re-formulate the FCHEV system by the knowledge of the input and output signals. Then, a novel adaptive model-free integral terminal sliding mode control (AMFITSMC) based on nonlinear disturbance observer (NDO) is proposed to force the actual values of the DC-link bus voltage and the power source’s currents track their obtained reference trajectories, wherein the NDO is used to approximate the unknown dynamics of the ULM. Moreover, the Lyapunov theorem is used to verify the stability of AMFITSMC via a closed-loop system. Finally, the FCHEV system with the presented method is modeled on a Matlab/Simulink environment, and different driving schedules like WLTP, UDDS, and HWFET driving cycles are utilized for investigation. The corresponding simulation results show that the proposed technique provides better results than the other methods, such as operational mode strategy and fuzzy logic control, in terms of the reduction of fuel consumption and fuel cell power fluctuations.

Design and modelling of a neural network-based energy management system for solar PV, fuel cell, battery and ultracapacitor-based hybrid electric vehicle

Design and modelling of a neural network-based energy management system for solar PV, fuel cell, battery and ultracapacitor-based hybrid electric vehicle

Robust adaptive control for hybrid electric vehicles using sliding mode control

The complicated transient coupling driving performance of the electromechanical coupling driving system for hybrid electric vehicles can be guaranteed by reasonably distributing the power between the engine and the electric machine. However, the disturbances resulting from system parameter perturbation, model uncertainty, unknown road condition, and uncertain preceding vehicle acceleration would degrade the control performance. To this issue, this article investigates a robust adaptive control scheme based on sliding mode control techniques to handle the complicated transient coupling driving problem for the electromechanical coupling driving process. In the robust adaptive control method, the coordinated control mechanism is designed using the electric machine to compensate for the response deviation of engine torque. Meanwhile, the double-loop adaptive terminal sliding mode speed-tracking control scheme and the adaptive dynamic sliding mode torque tracking control method are designed for the engine and the electric machine, respectively. The proposed control scheme ensures accurate tracking of the desired trajectories of the engine and electric machine in the presence of parameter perturbation and the unknown driving condition. Both simulation and actual vehicle experimental results demonstrate the effectiveness and practicality of the proposed control strategy.

Hybrid power management and control of fuel cells‐battery energy storage system in hybrid electric vehicle under three different modes

AbstractIn most situations, fuel cells (FCs) are insufficient to supply power demands in hybrid electric vehicles (HEVs), thus battery storage systems (BSSs) are used to make the system more efficient like as rapid starting, high power density, and enhanced dynamic set response. How the power sources are regulated and distributed determines the accomplishment efficiency of the driving forces in such a configuration. The most critical aspect should be how to guarantee torque stability of the traction motor which is in our case a permanent magnet synchronous motor (PMSM) while attaining quick reaction. This study discusses a hybrid battery‐FCs energy storage and management system for a hybrid electric vehicle (HEV), as well as an integrated PMSM's passivity‐based control (PBC) technique to enable power integration and increase the hybrid electric vehicle (HEV)'s operating speed. The present paper is separated into two sections. First, a power management control (PMC) technology is used to manage the FCs‐battery system to guarantee that the HEV gets continuous power from the hybrid energy resources, where a fuzzy logic controller is used as an artificial intelligence system for the FCs converter to maximize the power produced. The second is dedicated to the new proposed PBC control of the EV's PMSM to rectify non‐linearities, external disturbances, and parametric fluctuations. The standard proportional‐integral (PI) control and sliding mode control (SMC) are built for comparison with the proposed PBC dedicated to the PMSM in order to properly assess the efficacy of the proposed control strategy. The simulation results obtained using MATALB/Simulink show that the proposed PBC technique and hybrid battery‐ proton‐exchange membrane PEMFCs energy management enables high power integration and increase the EV's operational speed.

A comparison of energy recovery systems using exhaust gases in hybrid electric vehicles

Due to the growing awareness of environmental protection, hybrid electric vehicles (HEV) have an increasing share in the automobile market. In order to offer customers HEV, which have long driven ranges, improvements must be made to enhance fuel efficiency. An energy Recovery System (ERS) is a newly developed technology aimed at fulfilling this target. This paper will compare multiple ERS that use exhaust gases as a source of energy. A clear understanding of each technology can be established by first reviewing and summarizing the advantages and challenges of each ERS. After that, a comparison between different technologies will be conducted to find the optimal ERS for compact HEV. Results show that integrated ETC is a more suitable method of energy recovery for the exhaust system in HEV based on the criterion that the combustion engines those energy recovery technologies apply to are the same.

Barrier function based adaptive sliding mode controller for the hybrid energy storage system of plugin hybrid electric vehicles

This paper proposes a hybrid energy storage system (HESS) and smart charging mechanism for plug-in hybrid electric vehicles (PHEVs) with the aim of reducing greenhouse gas emissions and fossil fuel consumption. The HESS includes a battery as the main energy source and an ultra-capacitor (UC) as an auxiliary source. Both the power sources are connected to the DC bus through power conditioning circuitry. To regulate the system’s output variable, a barrier function-based adaptive sliding mode controller is proposed, which will achieve finite-time convergence and robustness against disturbances and uncertainties without requiring prior knowledge of the upper bounds of the system. The controller aims to reduce chattering and ensure various control objectives, such as tight regulation of output voltage, perfect tracking of reference currents for the UC and battery, and power factor correction in the grid-to-vehicle (G2V) mode. Simulation results using MATLAB/Simulink show that the proposed controller outperformed sliding mode control (SMC) and finite-time synergetic control (FTSC). The proposed strategy has been validated through real-time hardware-in-the-loop (HIL) experiments using C2000 Delfino Microcontroller F28379D Launchpad.

Comprehensive Analysis of Power Systems for Hybrid Electric Vehicles

Now the pollution problem is becoming more and more serious, and the gas emitted by cars has seriously affected the environment. In order to reduce environmental pollution and Strength conservation and emission discount, sell the first-rate sustainable development of China's new electricity automobile industry, accelerate the development of vehicle strength, and the study of new energy vehicles is inevitable. New power vehicles are designed to use unconventional energy sources or indirectly use gasoline energy in a way that differs from conventional automotive internal combustion engines. The new power vehicle is the superior technology to complete the power control and driving of the vehicle which transcends the traditional technology and way. It is a vehicle with new technology as well as new structure. This paper mainly analyzes the energy conversion of hybrid electric vehicles, the advantages and disadvantages of the power system of hybrid electric vehicles, and also discusses the actual application of technology and the plate and management mode of power battery.

Development of an Intelligent Control Strategy for a Hybrid Energy System Integrated with the HEV Drive

Efficient and accurate performance of a control system plays a major role in developing an energy-efficient intelligent control system (ICS) for the Grid-connected Hybrid Electric Vehicle (HEV) applications. The ICS constitutes the control of hybrid energy split among the renewable sources (PV and Fuel Cell), HEV drive control is achieved by controlling the grid-connected inverter and bi-directional DC–DC converter. MPPT is designed in ICS to extract the maximum amount of power from solar PV. RESS control provides the maximum amount of extracted energy from PV and FC. In this proposed system, a bi-directional DCDC converter along with the storage system is designed to transfer energy in forward and reverse directions from source to storage element and storage element to load end in the process of charging and discharging of the battery which constitutes the bidirectional energy transfer. In addition to these control operations, ICS provides HEV drive control by controlling the grid-connected inverter. The operation of the developed ICS is compared with the conventional controllers and the results show that the proposed system with ICS is highly efficient than conventional controllers. The proposed system is tested with a three-phase load and HEV drive, both speed and torque controls are performed for the grid-connected HEV drive, and the system is tested with the different standard drive cycles.