Hybrid Electric Vehicle System Research Articles

Energy Management System and Control of Plug-in Hybrid Electric Vehicle Charging Stations in a Grid-Connected Microgrid

In the complex environment of microgrid deployments targeted at geographic regions, the seamless integration of renewable energy sources meets a variety of essential challenges. These include the unpredictable nature of renewable energy, characterized by intermittent energy generation, as well as ongoing fluctuations in load demand, the vulnerabilities present in distribution network failures, and the unpredictability that results from unfavorable weather conditions. These unexpected events work together to disturb the delicate balance between energy supply and demand, raising the alarming threat of system instability and, in the worst cases, the sudden advent of damaging blackouts. To address this issue, a fuzzy logic-based energy management system has been developed to monitor, manage, and optimize energy consumption in microgrids. This study focuses on the control of diesel generators and utility grids in a grid-connected microgrid which manages and evaluates numerous energy consumption and distribution features within a specified system, e.g., building or a microgrid. An energy management system is suggested based on fuzzy logic as a swift fix for complications with effective and competent resource management, and its presentation is compared with both the grid-connected and off-grid modes of the microgrid. In the end, the results exhibit that the proposed controller outclasses the predictable controllers in dropping sudden variations that arise during the addition of sources of renewable energy, supporting the refurbishment of the constant system.

Optimization of power system parameters for multi-mode hybrid electric vehicles based on regional demand

The performance of the vehicle power system for multi-mode hybrid electric vehicle (M-MHEV) can be improved through meticulous parameter matching and optimization. This paper developed the powertrain parameter design and dynamic performance calculation program based on the structure design, parameter matching calculation, and powertrain system selection. A HOQM of M-MHEV is formulated to ascertain the weight coefficient of vehicle performance indicators according to different regional requirements and a parameter matching and optimization method for power system, employing the Particle Swarm Optimization (PSO) algorithm, is suggested to assess and harmony the vehicle’s performance. Firstly, a house of quality model of M-MHEV is constructed to ascertain the weight coefficient of vehicle performance indicators derived from different regional demands. Additionally, a method is introduced for optimization of power system parameters of M-MHEV based on regional demand, the PSO algorithm is employed to optimize the characteristic parameters. And sensitivity analysis of characteristic parameters is conducted relying on user needs in different regions. The simulation results show that users in the northern and southern regions have different final weight coefficients for vehicle performance indicators and the parameters not only exert a substantial individual influence but also exhibit an interaction effect on the vehicle performance. Finally, a series of comparative simulations, are carried out with two optimization parameters respectively, the simulation outcomes demonstrate that the solution obtained through optimized design parameters solution could markedly enhance the technical parameters of the vehicle. The efficacy and viability of the proposed method based on user needs in different regions are verified. The conclusion provides a useful reference for differentiated design.

Bidirectional circulating preheating method harnessing engine and battery waste heat reuse in hybrid electric vehicles

In hybrid electric vehicles (HEVs), both the engine and power batteries experience reduced performance at low temperatures, necessitating the consumption of significant energy for preheating. The bidirectional preheating system method, which for the first time applies waste thermal energy to preheat both the engine and power battery in HEVs, utilizes the engine’s residual thermal energy to preheat the power battery and the thermal energy from the power battery to preheat the engine. To implement this method, a numerical model of the engine radiator and power battery waste heat is first established, and the temperature rise characteristics and temperature distribution of the waste heat system are simulated and analyzed. This analysis reveals the heat transfer law of the engine radiator and power battery waste heat. An automatic bidirectional thermal control device based on waste heat reuse is designed. The device utilizes a multi-stage series thermal energy transfer system with phase change materials (PCM) as the medium. A low-temperature experiment is conducted to assess the integration of heating and cooling between the engine and power battery. The results indicate that this method effectively utilizes the engine’s waste heat for both heating and cooling purposes. Specifically, a heat exchanger is used to preheat the power battery using the engine’s cooling residual heat, maintaining the internal battery temperature at 30 °C. Simultaneously, the residual heat from the power battery’s cooling system is circulated back to the engine body, preheating the engine coolant to 42 °C. Compared with existing HEVs, it can save more than 90% of the preheating energy consumption of the engine and power battery. This method can reduce power battery and engine warm-up time by more than 50%, while reducing emissions by more than 40% during engine cold start. This approach effectively addresses the challenge of low-temperature preheating for both power batteries and engines by utilizing cooling residual heat bidirectionally, thereby maximizing energy utilization efficiency in HEV systems and optimizing both battery and engine performance.

Modeling and control system optimization for electrified vehicles: A data-driven approach

Learning-based intelligent energy management systems for plug-in hybrid electric vehicles (PHEVs) are crucial for achieving efficient energy utilization. However, their application faces system reliability challenges in the real world, which prevents widespread acceptance by original equipment manufacturers (OEMs). This paper begins by establishing a PHEV model based on physical and data-driven models, focusing on the high-fidelity training environment. It then proposes a real-vehicle application-oriented control framework, combining horizon-extended reinforcement learning (RL)-based energy management with the equivalent consumption minimization strategy (ECMS) to enhance practical applicability, and improves the flawed method of equivalent factor evaluation based on instantaneous driving cycle and powertrain states found in existing research. Finally, comprehensive simulation and hardware-in-the-loop validation are carried out which demonstrates the advantages of the proposed control framework in fuel economy over adaptive-ECMS and rule-based strategies. Compared to conventional RL architectures that directly control powertrain components, the proposed control method not only achieves similar optimality but also significantly enhances the disturbance resistance of the energy management system, providing an effective control framework for RL-based energy management strategies aimed at real-vehicle applications by OEMs.

Research on fault diagnosis of multi-mode electromechanical compound transmission system for hybrid electric vehicle based on global analytical redundancy relations

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

Review of Hybrid Energy Storage Systems for Hybrid Electric Vehicles

Energy storage systems play a crucial role in the overall performance of hybrid electric vehicles. Therefore, the state of the art in energy storage systems for hybrid electric vehicles is discussed in this paper along with appropriate background information for facilitating future research in this domain. Specifically, we compare key parameters such as cost, power density, energy density, cycle life, and response time for various energy storage systems. For energy storage systems employing ultra capacitors, we present characteristics such as cell voltage, cycle life, power density, and energy density. Furthermore, we discuss and evaluate the interconnection topologies for existing energy storage systems. We also discuss the hybrid battery–flywheel energy storage system as well as the mathematical modeling of the battery–ultracapacitor energy storage system. Toward the end, we discuss energy efficient powertrain for hybrid electric vehicles.

A Hybrid RBFNN-SPOA Technique for Multi-Source EV Power System with Single-Switch DC-DC Converter

With a single-switched DC-DC converter, a hybrid approach is proposed for a fast-charging (FC) dc to dc converter with the controller for an electric vehicle charging station (EVCS) that combines solar photovoltaic (PV), fuel cells (FC), and a battery energy storage system (BESS). The proposed hybrid technique integrates a radial-basis-function-neural-network (RBFNN) and a student-psychology-optimization-algorithm (SPOA); commonly known as the RBFNN-SPOA technique. The PV-FC hybrid electric vehicle systems, like controllers, electric vehicle, and the vehicle’s power source, are displayed. The proposed converter architectures have less voltage waveform changes, a low voltage stress on semiconductor power sources, and a high-voltage ratio of conversion. The proposed method is to use FC successfully at battery charge states and varied radiation levels. The FC in the controller of SPOA may change the inputs and outputs depending on the battery state of charge (SoC) and PV irradiation. Similarly, the needed input-torque for the motor is forecasted by the recalling recurrent neural network controller using several manually produced torque signals. The performance of the proposed technique is evaluated in MATLAB and is compared to ant colony optimization (ACO), crow optimization algorithm (COA), and fuzzy logic controller (FLC) approaches. According to the results, the efficiency of the proposed technique is 99.17%.

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.