Hybrid Electric Vehicle Model Research Articles

Driver-Identified Supervisory Control System of Hybrid Electric Vehicles Based on Spectrum-Guided Fuzzy Feature Extraction

This article introduces the concept of the driver-identified supervisory control system, which forms a novel architecture of adaptive energy management for hybrid electric vehicles (HEVs). As a man-machine system, the proposed system can accurately identify the human driver from natural operating signals and provides driver-identified globally optimal control policies as opposed to mere control actions. To help improve the identifiability and efficiency of this control system, the method of spectrum-guided fuzzy feature extraction (SFFE) is developed. First, the configuration of the HEV model and its control system are analyzed. Second, design procedures of the SFFE algorithm are set out to extract 15 groups of features from primitive operating signals. Third, long-term and short-term memory networks are developed as a driver recognizer and tested by the features. The driver identity maps to corresponding control policies optimized by dynamic programming. Finally, the comparative study includes involved extraction methods and their identification system performance as well as their application to HEV systems. The results demonstrate that with help of the SFFE, the driver recognizer improves identifiability by at least 10% compared to that obtained using other involved extraction methods. The improved HEV system is a significant advance over the 5.53% reduction on fuel consumption obtained by the fuzzy-logic-based system.

DC-DC Converter used for Series-Parallel Hybrid Electric Vehicle

Recently, the area of Hybrid Electric Vehicles (HEV) has seen a tremendous growth all over the world. Due to increasing levels of emissions from the conventional vehicles, rising fuel prices and environmental concerns due to global warming, entire focus of automotive industry is shifted to development of HEV’s. The HEV’s are classified into various configurations depending upon the degree of hybridization which include the performance of the IC engine and electric battery simultaneously for the traction purpose. The physical overview and modeling of Hybrid electric vehicle is discussed. The importance of the dc-dc converter which acts as an interface between battery and the motor drive for bi-directional power flow is proposed .In this paper, bi-directional full bridge dc-dc converter and its implementation in Series-Parallel HEV is presented .This converter topology accounts for motoring as well as regenerative braking operations.

Feed-forward modeling and real-time implementation of an intelligent fuzzy logic-based energy management strategy in a series–parallel hybrid electric vehicle to improve fuel economy

A hybrid electric vehicle is powered by: the internal combustion engine and the battery-powered electric motor. These sources have specific operational characteristics, and it is necessary to match these characteristics for the efficient and smooth functioning of the vehicle. The nonlinearity and uncertainties in hybrid electric vehicle model require an intelligent controller to control the energy sharing between battery and engine. In this work, a fuzzy logic-enabled energy management strategy for the hybrid electric vehicle based on torque demand, battery state of charge and regenerative braking is designed and implemented. The proposed energy management strategy allows engine and motor to maneuver in their efficient operating regions. The designed hybrid electric vehicle and its control strategy follow the driver commands and regulations on vehicle performance and liquid fuel consumption. MATLAB/Simulink is used to carry out simulations, and then, the whole system is validated in real time on hardware-in-the-loop testing platform. This work employs an FPGA-based MicroLabBox hardware controller to validate real-time behavior. The proposed scheme results in better fuel economy, faster response and almost nil mismatch between desired and achieved vehicle speeds.

Modeling and Dynamic Response Analysis of a Compound Power-Split Hybrid Electric Vehicle During the Engine Starting Process

This study is aimed at revealing the dynamic response characteristics of a power-split hybrid electric vehicle (HEV) during the mode transition process with the engine starting. In this paper, for a hybrid powertrain system whose engine is directly connected to a compound power-split transmission, the equivalent lever method is first used to analyze the mode transition process with the engine starting. Then, based on the theoretical formulations and lookup tables, a dynamic model of the compound power-split HEV is established in detail, taking into consideration the engine ripple torque, battery-motor characteristics, dynamic meshing force between gears, and shaft spring-damping characteristics. The powertrain dynamic model is verified by a bench test. Moreover, taking the vehicle jerk and engine starting time as the evaluation indexes of the dynamic response characteristics, the influencing factors of the dynamic response characteristics in the engine starting process are analyzed comprehensively from the aspects of the engine, battery-motor, and engine starting condition. Finally, the engine starting process is synthetically optimized based on the obtained impact law of different influencing factors. The simulation results show that the synthesis optimization method can effectively reduce vehicle jerks and the engine starting time and improve driving comfort during the engine starting process.

Robust Integral Backstepping Control for Unified Model of Hybrid Electric Vehicles

Exponential decrease in oil and natural gas resources, increasing global warming issues and insufficiency of fossil fuels has shifted the focus to fuel cell hybrid electric vehicles (FHEVs). FHEV model used in this work consists of fuel cell, ultracapacitor and battery. Non-linearities present in the vehicle model dominate because of extreme driving conditions like rough terrains, slippery roads or hilly areas. Behavior of components like energy sources, induction motors and power processing blocks deviate significantly from their normal behavior when driving in highly demanding situations. To tackle these shortcomings, non-linear controllers are preferred because of their efficiency. In literature, different controllers have been proposed for either the energy sources or the induction motor separately, whereas this research work focuses on a unified hybrid electric vehicle (HEV) model to simultaneously control the energy sources and the induction motor. The model used is a complete representation of electric system of FHEV and increases the performance of the vehicle. This unified model provides improved DC bus voltage regulation along with speed tracking when subjected to European extra urban drive cycle (EUDC). In this work, Robust Integral Backstepping and Robust Backstepping controllers have been designed. Lyapunov based analysis ensures the global stability of the system. Performance of proposed controllers is validated in MATLAB/Simulink environment. A comparative analysis is also given to illustrate the importance of the unified model proposed in this work.

Charge and discharge characteristics of different types of batteries on a hybrid electric vehicle model and selection of suitable battery type for electric vehicles

The choice of battery type for electric vehicles is of great importance. For electric vehicle manufacturers, this issue is the biggest engineering problem. An electric vehicle produced with the right battery type will undoubtedly be preferred by users. In this study, the charge and discharge characteristics of four different battery types with a fixed charger were investigated. The batteries were then placed on a hybrid electric vehicle model and the charge and discharge characteristics were examined. The batteries were then placed on a hybrid electric vehicle model and the charge and discharge characteristics were examined.

Energy Management Strategy Design and Simulation Validation of Hybrid Electric Vehicle Driving in an Intelligent Fleet

This paper proposes a combination method of longitudinal control and fuel management for an intelligent Hybrid Electric Vehicle (HEV) fleet. This method can reduce the fuel consumption while maintaining the distance and speed for each vehicle in the fleet. An HEV system efficiency model was established to simulate the impact of different working modes. Based on the principle of optimal vehicle system efficiency, the energy management control strategy of HEV was designed. Then, the driver model of the piloting vehicle and the following vehicle was built by using an intelligent fuzzy control method. Finally, the intelligent fleet model and energy matching model of HEV were integrated with the simulation platform that was developed based on MATLAB/Simulink/Stateflow. The validity of the energy matching strategy of HEV under the principle of optimal system efficiency was verified by simulation results, and the purpose of improving the driving safety, traffic efficiency, and fuel economy of the fleet was achieved. Comparing with the conventional control strategy, the proposed method saved 7.79% of fuel for the HEV fleet. Meanwhile, the distance ranges between the vehicles were from 12 meters to 15 meters, which improved the driving safety, passing rate, and fuel economy.

Technological analysis and fuel consumption saving potential of different gas turbine thermodynamic configurations for series hybrid electric vehicles

Gas turbine systems are among potential energy converters to substitute the internal combustion engine as auxiliary power unit in future series hybrid electric vehicle powertrains. Fuel consumption of these auxiliary power units in the series hybrid electric vehicle strongly relies on the energy converter efficiency and power-to-weight ratio as well as on the energy management strategy deployed on-board. This paper presents a technological analysis and investigates the potential of fuel consumption savings of a series hybrid electric vehicle using different gas turbine–system thermodynamic configurations. These include a simple gas turbine, a regenerative gas turbine, an intercooler regenerative gas turbine, and an intercooler regenerative reheat gas turbine. An energetic and technological analysis is conducted to identify the systems’ efficiency and power-to-weight ratio for different operating temperatures. A series hybrid electric vehicle model is developed and the different gas turbine–system configurations are integrated as auxiliary power units. A bi-level optimization method is proposed to optimize the powertrain. It consists of coupling the non-dominated sorting genetic algorithm to the dynamic programming to minimize the fuel consumption and the number of switching ON/OFF of the auxiliary power unit, which impacts its durability. Fuel consumption simulations are performed on the worldwide-harmonized light vehicles test cycle while considering the electric and thermal comfort vehicle energetic needs. Results show that the intercooler regenerative reheat gas turbine–auxiliary power unit presents an improved fuel consumption compared with the other investigated gas turbine systems and a good potential for implementation in series hybrid electric vehicles.

Adaptive Rule-Based Energy Management Strategy for a Parallel HEV

This paper proposes an Adaptive Rule-Based Energy Management Strategy (ARBS EMS) for a parallel hybrid electric vehicle (HEV). The aim of the strategy is to facilitate the aftermarket hybridization of medium- and heavy-duty vehicles. ARBS can be deployed online to optimize fuel consumption without any detailed knowledge of the engine efficiency map of the vehicle or the entire duty cycle. The proposed strategy improves upon the established Preliminary Rule-Based Strategy (PRBS), which has been adopted in commercial vehicles, by dynamically adjusting the regions of operations of the engine and the motor. It prevents the engine from operating in highly inefficient regions while reducing the total equivalent fuel consumption of the vehicle. Using an HEV model developed in Simulink®, both the proposed ARBS and the established PRBS strategies are compared over an extended duty cycle consisting of both urban and highway segments. The results show that ARBS can achieve high MPGe with different thresholds for the boundary between the motor region and the engine region. In contrast, PRBS can achieve high MPGe only if this boundary is carefully established from the engine efficiency map. This difference between the two strategies makes the ARBS particularly suitable for aftermarket hybridization where full knowledge of the engine efficiency map may not be available.

An implementation perspective of hybrid electric vehicle

Hybrid electric vehicle (HEV) and Electric vehicle (EV) are currently needed to our transportation system due to low carbon emission and lack of fossil fuel in near future. Mathematical proof of efficiency enhancement in use of HEV and approximate mathematical model of EV in terms of Power, Energy, Force etc. are presented in this paper. Feasibility of HEV with Bidirectional Half Bridge DC-DC Converter as charger and Brush less DC (BLDC) motor, is presented in MATLAB simulation at prototype level of power scale 10:1 range with practical data of components.

Dynamical model of HEV with two planetary gear units and its application to optimization of energy consumption

A hybrid electric vehicle (HEV) that uses multiple planetary gear units with clutches as transmission system is advanced for the powertrain performance, because the operation of the clutches can lead to distinct operating modes, and the induced possible operating modes provide additional freedom to deal with the energy optimal control problem. Under each operating mode, the powertrain mechanical system has specific dynamical behavior. In order to develop model-based optimization schemes that can tackle the transient operations of the vehicle, exact dynamical modeling is investigated focusing on a hybrid powertrain system that uses a two-planetary-gear transmission box with two clutches. It shows that according to the states of the two clutches, the powertrain system has the power-split mode, parallel mode and the electric vehicle (EV) mode. Finally, an analysis for the calculation of the desired driving torque and its application to the dynamic programming (DP)-based energy management indicate the significance of the developed exact dynamical models.

Back-to-Back Competitive Learning Mechanism for Fuzzy Logic Based Supervisory Control System of Hybrid Electric Vehicles

This article proposes a novel back-to-back competitive learning mechanism (BCLM) for a fuzzy logic (FL) supervisory control system of hybrid electric vehicles (HEVs). This mechanism allows continuous competition between two fuzzy logic controllers during real-world driving. The leading controller will have the regulatory function of the supervisory control system. First, the configuration of the HEV model and its FL-based control system are analyzed. Second, the algorithm of chaos-enhanced accelerated particle swarm optimization (CAPSO) is developed for back-to-back learning of the membership function. Third, based on fuel-prioritized cost functions, the regulation of competitive assessment is designed to select a controller with a better fuel economy. Finally, the competitive performance of using the CAPSO algorithm is contrasted with other swarm-based methods and the BCLM-driven control system is validated by a hardware-in-the-loop test. The results demonstrate that the BCLM control system significantly reduces fuel consumption, at least 9% from charge sustaining and charge depleting based, and at least 7% from conventional FL-based systems.

Fuel efficiency optimization Techniques in Hybrid Vehicle

The hybrid electric vehicle (HEV) has the potential of sufficing drive needs with better fuel efficiency. The complex nature of hybrid vehicle provides multiple opportunities for obtaining better fuel efficiency. The mathematical modeling is a necessity for understanding the behavior of HEV. The fuel efficiency is obtained through various means including the design of energy management system, component sizing, emission control, and other methods. In the present paper, a mathematical model of HEV is presented for a parallel HEV with a review of various methods used for fuel efficiency optimization.

Development of Global Optimization Algorithm for Series-Parallel PHEV Energy Management Strategy Based on Radau Pseudospectral Knotting Method

The powertrain model of the series-parallel plug-in hybrid electric vehicles (PHEVs) is more complicated, compared with series PHEVs and parallel PHEVs. Using the traditional dynamic programming (DP) algorithm or Pontryagin minimum principle (PMP) algorithm to solve the global-optimization-based energy management strategies of the series-parallel PHEVs is not ideal, as the solution time is too long or even impossible to solve. Chief engineers of hybrid system urgently require a handy tool to quickly solve global-optimization-based energy management strategies. Therefore, this paper proposed to use the Radau pseudospectral knotting method (RPKM) to solve the global-optimization-based energy management strategy of the series-parallel PHEVs to improve computational efficiency. Simulation results showed that compared with the DP algorithm, the global-optimization-based energy management strategy based on the RPKM improves the computational efficiency by 1806 times with a relative error of only 0.12%. On this basis, a bi-level nested component-sizing method combining the genetic algorithm and RPKM was developed. By applying the global-optimization-based energy management strategy based on RPKM to the actual development, the feasibility and superiority of RPKM applied to the global-optimization-based energy management strategy of the series-parallel PHEVs were further verified.

Nonlinear Model Predictive Control of a Power-Split Hybrid Electric Vehicle With Consideration of Battery Aging

In this paper, the nonlinear model predictive control (NMPC) for the energy management of a power-split hybrid electric vehicle (HEV) has been studied to improve battery aging while maintaining the fuel economy at a reasonable level. A first principle battery model is built with simulation capacity of the battery aging features. The built battery model is integrated with an HEV model from autonomie software to investigate the vehicle and battery performance under control strategies. The NMPC has simplified battery models to predict the state of charge (SOC) change, the fuel consumption of the engine, and the battery aging index over the predicted horizon. The purpose of the NMPC is to find an optimized control sequence over the prediction horizon, which minimizes the designed cost function. The proposed control strategy is compared with that of an NMPC, which does not consider the battery aging. It is found that, with the optimized weighting factor selection, the NMPC with the consideration of battery aging has better battery aging performance and similar fuel economy performance comparing with the NMPC without the consideration of battery aging.

Segemented Driving Cycle Based Optimization of Control Parameters for Power-Split Hybrid Electric Vehicle With Ultracapacitors

As energy storage device of hybrid electric vehicles (HEVs), ultracapacitors feature the advantages of higher power density and longer life cycle compared with batteries. However, when ultracapacitors of the same power level are used instead of batteries, fuel consumption becomes more sensitive to changes in control parameters as ultracapacitors store much less energy, and the state of charge (SOC) is power-sensitive. In this paper, optimization of control parameters for a power-split HEV with ultracapacitors is addressed to achieve better fuel economy. First, a power-split HEV model and a corresponding control strategy are established under the MATLAB script environment for convenient analysis and application of an optimization algorithm. Second, focusing on the power-sensitive characteristic of the SOC, three key control parameters are determined, and their effects on fuel consumption are analyzed. Third, an improved particle swarm optimization (IPSO) algorithm is proposed to overcome the disadvantage of the PSO trapping into “local optimum” and improve optimization efficiency. To fully exploit the fuel-saving capability of the HEV, driving cycle segmentation is also considered. The IPSO is used to optimize three key control parameters under the segmented adapted world transient vehicle cycle. Finally, the optimal results are applied to hardware-in-the-loop test to verify the effectiveness of the proposed optimization method. Compared with the fuel consumption before optimization, the fuel saving rate reaches 9.20% in the urban section, 6.40% in the roadway section, and 5.40% in freeway section.

Threshold-changing control strategy for series hybrid electric vehicles

This paper proposes a new set of design principles to classify and design rule-based control strategies for the powertrain energy management of series hybrid electric vehicles. The design principles proposed consider the two most established rule-based control strategies for series hybrid electric vehicles, the Thermostat and the Power follower control strategies, and also an optimization-based control strategy, the Equivalent consumption minimization strategy, in terms of the mechanisms they employ to ensure charge sustaining operation and fuel efficient driving. Thus, the work then reflects upon the most effective design principles and derives a novel and superior rule-based control strategy for series hybrid electric vehicles that is claimed to outperform all the existing rule-based schemes in terms of fuel economy: the optimal primary source strategy (OPSS). The OPSS is implemented and then compared on a high fidelity hybrid electric vehicle model to Thermostat, Power follower and Equivalent consumption minimization strategies, as well as to a recently developed rule-based control strategy, the Exclusive operation strategy. As compared to conventional rule-based control strategies, the OPSS is found to deliver significantly improved fuel economy and which is remarkably close to that achieved by the optimization-based Equivalent consumption minimization strategy, while the design of the OPSS is simple and robust as compared to optimization-based strategies. The impressive performance is partly attributed to the recent improvements in engine start stop system technology. It is also shown that the battery is operated in a more steady manner, with a lower depth of discharge, consequently reducing battery degradation.

Development Trends of Transmissions for Hybrid Electric Vehicles Using an Optimized Energy Management Strategy

Energy conservation and emissions reduction have become increasingly significant for automobiles due to the severity of the current energy situation. Hybrid electric vehicle (HEV) technology is one of the most promising solutions. This study investigated the total efficiency of a HEV powertrain. To improve the total efficiency, the engine should be regulated to work at its highest efficiency and drive the wheels directly as much as possible. To accomplish this, we developed an energy management strategy based on the direct drive area (DDA) of the engine’s efficiency map. Several typical HEV models were built to compare the fuel consumption using DDA and rule-based strategies. Furthermore, the function of the HEV transmission system with DDA was considered. The transmission in a HEV should regulate the engine to work at its highest efficiency as much as possible, which is rather different than the regulation in an internal combustion engine vehicle. The functional change may lead to transmission systems with fewer gears but optimal gear ratios. If this trend is realized, the manufacturing cost of HEVs could be largely reduced.

Modeling and computer simulation of a novel hybrid transmission

This work presents the modeling and computer simulation of a novel hybrid transmission with a mechanical reverse driving mode, including an engine, a motor, a simple planetary gear train, and a Ravigneaux planetary gear train. Based on the given teeth number, the reduction ratios of all the clutching condition are acquired. The feasibilities of mode shifts among the clutching conditions are analyzed. Then, a modified rule-based control strategy is introduced. Subject to the vehicle condition, speed command, and predicted equivalent fuel consumptions, the most fuel economy clutching condition is selected by the control strategy. And, a computer model is developed using SIMULINK. Two popular driving cycles are applied to the simulation model, and the simulation results of the novel hybrid transmission are competitive with the existing hybrid electric vehicle models.

Analysis and Control of Torque Split in Hybrid Electric Vehicles by Incorporating Powertrain Dynamics

Hybrid electric vehicle (HEV) energy management strategies usually ignore the effects from dynamics of internal combustion engines (ICEs). They usually rely on steady-state maps to determine the required ICE torque and energy conversion efficiency. It is important to investigate how ignoring these dynamics influences energy consumption in HEVs. This shortcoming is addressed in this paper by studying effects of engine and clutch dynamics on a parallel HEV control strategy for torque split. To this end, a detailed HEV model including clutch and ICE dynamic models is utilized in this study. Transient and steady-state experiments are used to verify the fidelity of the dynamic ICE model. The HEV model is used as a testbed to implement the torque split control strategy. Based on the simulation results, the ICE and clutch dynamics in the HEV can degrade the control strategy performance during the vehicle transient periods of operation by around 8% in urban dynamometer driving schedule (UDDS) drive cycle. Conventional torque split control strategies in HEVs often overlook this fuel penalty. A new model predictive torque split control strategy is designed that incorporates effects of the studied powertrain dynamics. Results show that the new energy management control strategy can improve the HEV total energy consumption by more than 4% for UDDS drive cycle.