Hybrid Electric Vehicle Architecture Research Articles

Driving Towards Sustainability: A Comprehensive Review of Electric Vehicles

This research summarizes research on electric vehicles (EVs) and sustainable transit solutions, highlighting advancements in battery technologies, motor efficiency, and vehicle-to-grid (V2G) integration. It explores the conversion of a Rover Mini into the electric E-MINI and discusses the potential of proton exchange membrane (PEM) fuel cells and hybrid electric vehicle (HEV) architectures. The study emphasizes the importance of effective power management and infrastructure upgrades to support EV adoption. Despite inherent complexities, innovative designs offer promising solutions for future transportation sustainability. advanced strategies for optimizing electric powertrains, transit networks, and fast charging systems to enhance sustainability in electromobility. Additionally, analysis is done on how quick charging affects Li-ion battery deterioration, revealing insights into mitigating effects and identifying cost-effective solutions for various battery technologies. Overall, this comprehensive investigation contributes crucial insights for advancing sustainable electric mobility.

An efficient soft switching synchronous buck converter for battery charging application in hybrid electric vehicle architecture

SummaryThis paper proposes a novel topology based on a zero voltage soft switching (ZVS) synchronous buck DC‐DC converter using an auxiliary unidirectional cell comprising a diode, a switch, and an inductor. This unidirectional cell aids in achieving the soft‐switching condition for the synchronous switches, thereby abating switching losses and enhancing the system efficiency. Furthermore, the presence of the unidirectional cell results in no additional current or voltage stress on the switches. This topology is well suited to hybrid electric vehicle (HEV) architecture. Detailed analysis and operation of the proposed topology are discussed. The results of the proposed topology are analyzed using MATLAB/Simulink software and validated experimentally.

Dynamic performance analysis of front-wheel drive hybrid electric vehicle architectures under different real-time operating conditions

Dynamic performance analysis of front-wheel drive hybrid electric vehicle architectures under different real-time operating conditions

Dynamic performance analysis of front-wheel drive hybrid electric vehicle architectures under different real-time operating conditions

Dynamic performance analysis of front-wheel drive hybrid electric vehicle architectures under different real-time operating conditions

Integration of a Model Predictive Control with a Fast Energy Management Strategy for a Hybrid Powertrain of a Connected and Automated Vehicle

In the years to come, Connected and Automated Vehicles (CAVs) are expected to substantially improve the road safety and environmental impact of the road transport sector. The information from the sensors installed on the vehicle has to be properly integrated with data shared by the road infrastructure (smart road) to realize vehicle control, which preserves traffic safety and fuel/energy efficiency. In this context, the present work proposes a real-time implementation of a control strategy able to handle simultaneously motion and hybrid powertrain controls. This strategy features a cascade of two modules, which were implemented through the model-based design approach in MATLAB/Simulink. The first module is a Model Predictive Control (MPC) suitable for any Hybrid Electric Vehicle (HEV) architecture, acting as a high-level controller featuring an intermediate layer between the vehicle powertrain and the smart road. The MPC handles both the lateral and longitudinal vehicle dynamics, acting on the wheel torque and steering angle at the wheels. It is based on a simplified, but complete ego-vehicle model, embedding multiple functionalities such as an adaptive cruise control, lane keeping system, and emergency electronic brake. The second module is a low-level Energy Management Strategy (EMS) of the powertrain realized by a novel and computationally light approach, which is based on the alternative vehicle driving by either a thermal engine or electric unit, named the Efficient Thermal Electric Skipping Strategy (ETESS). The MPC provides the ETESS with a torque request handled by the EMS module, aiming at minimizing the fuel consumption. The MPC and ETESS ran on the same Microcontroller Unit (MCU), and the methodology was verified and validated by processor-in-the-loop tests on the ST Microelectronics board NUCLEO-H743ZI2, simulating on a PC-host the smart road environment and a car-following scenario. From these tests, the ETESS resulted in being 15-times faster than than the well-assessed Equivalent Consumption Minimization Strategy (ECMS). Furthermore, the execution time of both the ETESS and MPC was lower than the typical CAN cycle time for the torque request and steering angle (10 ms). Thus, the obtained result can pave the way to the implementation of additional real-time control strategies, including decision-making and motion-planning modules (such as path-planning algorithms and eco-driving strategies).

Optimal mesh discretization of the dynamic programming for hybrid electric vehicles

The maximum fuel economy achievable by a hybrid electric vehicle (HEV) on a specific driving mission can be attained through the identification of the best admissible control policy. In the last years, the Dynamic Programming (DP) algorithm has proved to be capable of identifying the optimal policy once the definition of a proper computational grid is performed. As far as the refinement of the latter is concerned, the results produced by the selected control strategy can be negatively affected by a rough mesh due to approximation errors chains. Still, too fine a grid can lead to unreasonable CPU times. Hence, a method for automatically detecting the optimal mesh discretization with respect to different HEV simulations should be found. In the present paper, a self-adaptive statistical approach based on a proper management of any admissible battery energy variation is developed to significantly improve the calculation times required for HEV architectures while still attaining the best possible accuracy in terms of CO2 emissions as well as total cost of ownership (TCO). For the purpose, a low-throughput battery model has been taken into account so that the number of cells, the curve power limit and the energy content could be accounted for. The proposed method was tested on two parallel HEVs belonging to different categories, specifically a passenger car and a heavy-duty vehicle. The robustness of the method was also assessed for by testing the effects of a variation in the number of control variables within the simulation.

Q-Learning-Based Supervisory Control Adaptability Investigation for Hybrid Electric Vehicles

As one of adaptive optimal controls, the Q-learning based supervisory control for hybrid electric vehicle (HEV) energy management is rarely studied for its adaptability. In real-world driving scenarios, conditions such as vehicle loads, road conditions and traffic conditions may vary. If these changes occur and the vehicle supervisory control does not adapt to it, the resulting fuel economy may not be optimal. To our best knowledge, for the first time, the study investigates the adaptability of Q-learning based supervisory control for HEVs. A comprehensive analysis is presented for the adaptability interpretation with three varying factors: driving cycle, vehicle load condition, and road grade. A parallel HEV architecture is considered and Q-learning is used as the reinforcement learning algorithm to control the torque split between the engine and the electric motor. Model Predictive Control, Equivalent consumption minimization strategy and thermostatic control strategy are implemented for comparison. The Q-learning based supervisory control shows strong adaptability under different conditions, and it leads the fuel economy among four supervisory controls in all three varying conditions.

Potential of hybrid powertrains in a variable compression ratio downsized turbocharged VVA Spark Ignition engine

After the diesel emissions scandal, also known as Dieselgate, Direct Injection Spark-Ignited (DISI) internal combustion engines (ICE) appears as the most promising alternative to mitigate the harmful tailpipe emissions from passenger cars. In spite of that, the current ICE technologies are not enough to achieve the fuel consumption/CO2 emissions targets set by the new transportation legislation (4.1 Lgasoline/100 km, 95 gCO2/km for 2021). In this complex scenario, the electrification of the powertrain using high efficiency electric motors and battery package together with sophisticated DISI engines appears as potential solution to meet these requirements. The aim of this work is to study the fuel consumption and pollutant emissions in transient conditions from a passenger car equipped with a variable compression ratio (VCR) DISI engine and electrified powertrain technologies. The vehicle behavior was simulated by means of a 0D GT-Suite model fed by experimental results obtained in an engine test bench. Mild hybrid electric vehicle (MHEV) and full hybrid electric vehicle (FHEV) architectures using a VCR DISI engine were studied. Moreover, an optimization methodology is presented to select the best vehicle configuration in terms of hardware and control strategies by means of a design of experiments (DoE). The results show that VCR allows a fuel improvement of 3% with respect to the conventional DISI fixed CR along the worldwide harmonized light vehicles test cycles (WLTC). The benefits found when combining the VCR technology with hybrid powertrains are even higher. In this sense, the fuel improvements were higher as the electrification levels increased, with 8% for MHEV-VCR and around 20% for FHEV-VCR. In terms of emissions, the two clear benefits with FHEV-VCR were the reduction of particle number (PN) and unburned hydrocarbons (HC) of around 60% and 15%, respectively, as compared to the conventional DISI.

DESIGN AND SIMULATION OF PHASE SHIFTED DC-DC FULL BRIDGE CONVERTER

Electric and hybrid electric vehicle (EV/HEV) architectures require a small DC-DC converter to replace any conventional vehicle’s alternator. The DC-DC converter, also described as the vehicle Auxiliary Power Module (APM), provides power flow between the vehicle high voltage (HV) and low voltage (LV) DC bus. This abstract represents the phase shifted full bridge DC-DC converter scheme to design, simulate DC/DC Converter for a power rating of 80W intended for low power/auxiliary applications in electric vehicles The proposed converter is very beneficial as it can be used for 4 quadrant operation much adaptive to the unknown parameters in the PV system and these unknown parameters are estimated through the adaptation laws in the algorithm which guarantees maximum power extraction possible from the power converter for a PV system. The converter supplies AC current as per requirement. The overall stability of the converter output is analyzed by simulation. The results are compared with other existing converter results for improving power quality even further. The proposed results indicate the robustness of the proposed scheme.

A review on opportunities of thermionic regeneration system in hybrid electric vehicle

A hybrid electric vehicle utilizes power from both engine and battery to drive the wheels. Hybrid electric vehicle architectures are micro, mild, full and plug in hybrids. This review covers recent optimization in technologies and systems of hybrid electric vehicle. It is evident from the literature that utilizing waste energy such as braking losses for battery charging is more effective for micro and mild hybrid electric vehicle. The increase in electrification increases the demand of alternate battery charging methods in vehicle. Therefore, the opportunity lies in recovering waste heat of engine for battery charging. This could be done by direct energy converters such as thermoelectric and thermionic converters. The paper comprehensively studies these direct energy converters on the basis of their working principle and conversion efficiencies. The results from this study show that thermionic energy conversion stands better for hybrid electric vehicles as compared with other direct energy conversion methods.

Gearshift control strategies for hybrid electric vehicles: A comparison of powertrains equipped with automated manual transmissions and dual-clutch transmissions

This article describes gearshift control strategies developed for a series-parallel hybrid electric vehicle architecture in which an electric machine is connected to the output of the transmission, thus obtaining torque filling capabilities during gearshifts. Two transmission systems are analyzed: hybrid automated manual transmission and hybrid dual-clutch transmission. The article focuses on the gearshift control strategies for the two transmission, splitting each gearshift into several phases. The objectives of each control phase, the equations for computing the set points for powertrain actuators, and the conditions that determine the passage from one shifting phase to the next are reported. Moreover, nonlinear dynamic models are described and used to verify through simulation the effectiveness of the controllers. Promising results, in terms of vehicle dynamic performance, are obtained for both transmission systems.

Assessing additional fuel consumption from cabin thermal comfort and auxiliary needs on the worldwide harmonized light vehicles test cycle

Standards for fuel consumption and carbon dioxide emissions are implemented worldwide in most light-duty vehicle markets. Regulatory drive cycles, defined as specific time-speed patterns, are used to measure levels of fuel consumption and emissions. These measurements should realistically reflect real world driving performance, however there is increasing concern about their adequacy due to the discrepancies observed between certified and real world consumption and emissions values. One of the main reasons for the discrepancy is that current testing protocols do not account for non-mechanical vehicle energy needs, such as passengers’ thermal comfort needs and the use of electric auxiliaries on-board. Cabin heating and cooling can especially lead to considerable increase in vehicle energy consumption. This paper presents a simulation-based assessment framework to account for the additional fuel consumption related to the cabin thermal energy and auxiliary needs under the worldwide-harmonized light vehicles test procedure (WLTP). A vehicle cabin model is developed and the thermal comfort energy needs are derived for cooling and heating, depending on ambient external temperature under cold, moderate and warm climates. A modification to the WLTP is proposed by including the generated power profiles for thermal comfort and auxiliary needs. Dynamic programming is used to compute the fuel consumption on the modified WLTP for a rechargeable series hybrid electric vehicle (SHEV) architecture. Results show consumption increases of 20% to 96% compared to the currently adopted WLTP, depending on the considered climate.

Energy Optimization and Fuel Economy Investigation of a Series Hybrid Electric Vehicle Integrated with Diesel/RCCI Engines

Among different types of low temperature combustion (LTC) regimes, eactively controlled compression ignition (RCCI) has received a lot of attention as a promising advanced combustion engine technology with high indicated thermal efficiency and low nitrogen oxides ( NO x ) and particulate matter (PM) emissions. In this study, an RCCI engine for the purpose of fuel economy investigation is incorporated in series hybrid electric vehicle (SHEV) architecture, which allows the engine to run completely in the narrow RCCI mode for common driving cycles. Three different types of energy management control (EMC) strategies are designed and implemented to achieve the best fuel economy. The EMC strategies encompass rule-based control (RBC), offline, and online optimal controllers, including dynamic programing (DP) and model predictive control (MPC), respectively. The simulation results show a 13.1% to 14.2% fuel economy saving by using an RCCI engine over a modern spark ignition (SI) engine in SHEV for different driving cycles. This fuel economy saving is reduced to 3% in comparison with a modern compression ignition (CI) engine, while NO x emissions are significantly lower. Simulation results show that the RCCI engine offers more fuel economy improvement in more aggressive driving cycles (e.g., US06), compared to less aggressive driving cycles (e.g., UDDS). In addition, the MPC results show that sub-optimal fuel economy is achieved by predicting the vehicle speed profile for a time horizon of 70 s.

Advanced Electrical Machines and Machine-Based Systems for Electric and Hybrid Vehicles

The paper presents a number of advanced solutions on electric machines and machine-based systems for the powertrain of electric vehicles (EVs). Two types of systems are considered, namely the drive systems designated to the EV propulsion and the power split devices utilized in the popular series-parallel hybrid electric vehicle architecture. After reviewing the main requirements for the electric drive systems, the paper illustrates advanced electric machine topologies, including a stator permanent magnet (stator-PM) motor, a hybrid-excitation motor, a flux memory motor and a redundant motor structure. Then, it illustrates advanced electric drive systems, such as the magnetic-geared in-wheel drive and the integrated starter generator (ISG). Finally, three machine-based implementations of the power split devices are expounded, built up around the dual-rotor PM machine, the dual-stator PM brushless machine and the magnetic-geared dual-rotor machine. As a conclusion, the development trends in the field of electric machines and machine-based systems for EVs are summarized.

Modelling and heuristic control of a parallel hybrid electric vehicle

Hybrid electric vehicles offer the potential for fuel consumption improvements when compared with conventional vehicle powertrains. The fuel consumption benefits which can be realised when utilising the hybrid electric vehicle architecture are dependent on how much braking energy is regenerated, and how well the regenerated energy is utilised. A number of power management strategies have been proposed in literature. Owing to the prospect of real-time implementation, many of these proposals have centred on the use of heuristics. Despite the research advances made, the key challenge with heuristic strategies remains achieving reasonable fuel savings without over-depleting the battery’s state of charge at the end of the trip. In view of this challenge, this paper offers two main contributions to existing energy management literature. The first is a novel, simple but effective heuristic control strategy which employs a tuneable parameter (the percentage of the maximum motor tractive power) to decide the control sequence, such that impressive fuel savings are achieved without over-depleting the final state of charge of the battery (the battery energy). The second is the quantitative exploration of braking patterns and its impact on kinetic energy regeneration. The potential of the proposed heuristic control strategy was explored over a range of driving cycles which reflect different driving scenarios. The results from this analysis show that fuel savings of as much as 19.07% can be achieved over the Japan 10–15 driving cycle. In comparison with a suboptimal controller whose control signals were derived from dynamic programming optimal control, our proposed strategy was found to be outperforming, in that it achieved impressive real-time fuel savings without much penalty to the final state of charge of the battery. Gentle braking patterns were also found to significantly improve brake energy regeneration by the electric motor.

Architectural study of diesel hybrid propulsion systems to meet future fuel economy and emission regulations

Among conventional propulsion systems, the diesel propulsion system is very efficient in terms of fuel economy. However, conventional diesel propulsion systems have to meet aggressive fuel economy and stringent emission regulations. Electrification of the diesel propulsion system can play a major role in achieving future fuel economy and emission standards. In this paper, diesel hybrid propulsion system architectures are presented that could potentially meet future fuel economy and emissions compliance standards for a given set of performance criteria. Three diesel hybrid electric vehicle (D-HEV) architectures; parallel hybrid (PH), power split (PS) and series hybrid (SH) are investigated and analyses of these architectures are conducted. These architectures are compared and evaluated considering trade-offs between fuel economy and emissions targets.

Dynamic Programming-Informed Equivalent Cost Minimization Control Strategies for Hybrid-Electric Vehicles

This paper presents a direct mathematical approach for determining the state of charge (SOC)-dependent equivalent cost factor in hybrid-electric vehicle (HEV) supervisory control problems using globally optimal dynamic programming (DP). It therefore provides a rational basis for designing equivalent cost minimization strategies (ECMS) which achieve near optimal fuel economy (FE). The suggested approach makes use of the Pareto optimality criterion that exists in both ECMS and DP, and as such predicts the optimal equivalence factor for a drive cycle using DP marginal cost. The equivalence factor is then further modified with corrections based on battery SOC, with the aim of making the equivalence factor robust to drive cycle variations. Adaptive logic is also implemented to ensure battery charge sustaining operation at the desired SOC. Simulations performed on parallel and power-split HEV architectures demonstrate the cross-platform applicability of the DP-informed ECMS approach. Fuel economy data resulting from the simulations demonstrate that the robust controller consistently achieves FE within 1% of the global optimum prescribed by DP. Additionally, even when the equivalence factor deviates substantially from the optimal value for a drive cycle, the robust controller can still produce FE within 1–2% of the global optimum. This compares favorably with a traditional ECMS controller based on a constant equivalence factor, which can produce FE 20–30% less than the global optimum under the same conditions. As such, the controller approach detailed should result in ECMS supervisory controllers that can achieve near optimal FE performance, even if component parameters vary from assumed values (e.g., due to manufacturing variation, environmental effects or aging), or actual driving conditions deviate largely from standard drive cycles.

Development of a comprehensive and flexible forward dynamic powertrain simulation tool for various hybrid electric vehicle architectures

This paper presents a comprehensive and flexible forward dynamic powertrain simulation tool, WARwick Powertrain Simulation Tool for ARchitectures 2 (WARPSTAR2), for modelling of conventional internal combustion engine, hybrid, and pure electric vehicles. WARPSTAR2 includes physical powertrain component models and their controllers, a hybrid supervisory controller, the driver, and the environment model. The physical powertrain component models are developed in Dymola, while the component controllers, the hybrid supervisory controllers, and the driver model are realized in MATLAB/Simulink. Thus the power of these two software tools is combined. A generalized fuzzy-logic-based supervisory controller is proposed for all hybrid electric vehicle (HEV) architectures so that all HEVs with different architectures share the same structure of supervisory controller. The generalized formation can be used for the supervisory controllers of different HEV architectures with varied parameter settings, thus facilitating the controller design process. The rule-based supervisory controller is also developed in WARPSTAR2. Simulation is carried out for different HEVs with these two supervisory controllers in the driving cycles. The results of engine and battery power usages with these two supervisory controllers are similar and the differences of predicted engine fuel consumptions between the two supervisory controllers are within 5 per cent.

Development of a Dual-Fuel Power Generation System for an Extended Range Plug-in Hybrid Electric Vehicle

In recent decades, there has been a growing global concern with regard to vehicle-generated greenhouse gas emissions and the resulting air pollution. In response, automotive original equipment manufacturers focus their efforts on developing ?greener? propulsion solutions in order to meet the societal demand and ecological need for clean transportation. Hydrogen is an ideal vehicle fuel for use not only in fuel cells (FCs) but also in a spark-ignition internal combustion engines (ICEs). The combustion of hydrogen ( H2) fuel offers vastly superior tail-pipe emissions when compared with gasoline and can offer improved performance. H2 is ideally suited for use in an extended range plug-in hybrid electric vehicle architecture where engine efficiency can be optimized for a single engine speed. H2 ICEs are significantly more cost effective then an equivalent-sized H2 FC making them a better near-term solution. Before hydrogen can replace gasoline and diesel as the main source of automotive fuel, a number of hurdles must first be overcome. One such hurdle includes developing a suitable hydrogen infrastructure, which could take decades. As such, dual-fuel capabilities will help to create a transition between gasoline- and hydrogen-powered vehicles in the near term, while a full-service hydrogen infrastructure is developed.