Hybrid Electric Vehicle System Research Articles

A Linear Program for System-Level Control of Regional PHEV Charging Stations

This paper studies dynamic control of a system of plug-in hybrid electric vehicle (PHEV) charging stations. A finite horizon stochastic program is presented. Based upon the 15-min updated period of the electricity market price, the objective function is to maximize profit, which is the revenue benefit from selling back to the grid and the charging of the vehicles minus the cost of buying electricity from the grid. The state variables in each 15-min time period consist of the total wind purchased from the system, solar power generation at each charging station, total demand at each station, and nodal market price at stations’ location. A stochastic program is formulated, and the mean value problem as a deterministic linear program is solved. Potential strategies are presented to provide insight into the behavior of the system.

Cooperative control for friction and regenerative braking systems considering dynamic characteristic and temperature condition

The braking system of hybrid electric vehicle (HEV) is composed of friction and regenerative braking system, meaning that braking torque is generated by the collaboration of the friction and regenerative braking system. With the attributes, there are two problems in the HEV braking system. First, rapid deceleration occurs due to dynamic characteristic difference when shifting the friction and regenerative braking systems. Second, the friction braking torque alters with temperature because the friction coefficient changes with the temperature. These problems cause the vehicle to be unstable. In this paper, the concurrence control and compensation control were proposed to solve these problems. And also, the concurrence control and compensation control were combined for the stability of the braking system. In order to confirm the effect of these control algorithms, the experiment and simulation were conducted. Consequently, it was confirmed that the control algorithm of this study improved the vehicle safety and stability.

Truncated Approximate Dynamic Programming with Task-Dependent Terminal Value

We propose a new class of computationally fast algorithms to find close to optimal policy for Markov Decision Processes (MDP) with large finite horizon T.The main idea is that instead of planning until the time horizon T, we plan only up to a truncated horizon H << T and use an estimate of the true optimal value function as the terminal value. Our approach of finding the terminal value function is to learn a mapping from an MDP to its value function by solving many similar MDPs during a training phase and fit a regression estimator. We analyze the method by providing an error propagation theorem that shows the effect of various sources of errors to the quality of the solution. We also empirically validate this approach in a real-world application of designing an energy management system for Hybrid Electric Vehicles with promising results.

E-4WD 시스템 개발

e-4WD(Electric-4WD) system is a 4WD(4-Wheel Drive) System that can transform a car into a Hybrid System. e-4WD consists of a Motor, Inverter, Speed reducer and Clutch. The Motor, Speed reducer and Clutch are installed on the rear sub-frame as a chassis module type. The inverter is installed separately. Compared to a mechanical 4WD, the e-4WD system has many advantages. For example, the reduced number of drivetrain components makes better use of the space. Driving with a motor only at low speed improves fuel economy and reduces exhaust gas. Engine downsizing is available because the motor assists the engine. The performance of a conventional HEV(Hybrid Electric Vehicle) system can also be maintained. This paper proposes the specifications of components and the control logic for an e-4WD System. And the effect of the e-4WD system is proven using a test vehicle equipped with components under various test conditions.

Comparison of power-split and parallel hybrid powertrain architectures with a single electric machine: Dynamic programming approach

Due to limited flexibility of engine operation, it is uneasy to improve the fuel economy of parallel hybrid electric vehicles (HEVs) with a single electric machine (SEM) from control perspective only. However, from design perspective, it is likely to devise multiple operating modes on a powertrain system to achieve better fuel economy. Therefore, in this paper, a velocity coupling HEV system with a SEM has been proposed, which has eleven modes, including an electric vehicle (EV) mode, three regenerative braking modes, an engine-start mode, an electrically variable transmission (EVT) mode, a parallel HEV mode, an engine-only mode, and three charging modes. Dynamic Programming (DP), as a global optimization algorithm, is leveraged to minimize the fuel consumption of this system. For comparison purposes, the P1 and P2 HEV systems are also examined by DP using the same vehicular parameters. Comparative results indicate that the P2 HEV saves about 6.68% fuel consumption over the P1 HEV, while more improvement can be observed from the proposed velocity coupling HEV, with 13.82% fuel consumption reduction over the P1 HEV.

Review on use of phase change materials in battery thermal management for electric and hybrid electric vehicles

The battery electric vehicle is evolving and has the potential to replace conventional internal combustion-based vehicles in the future. Batteries are the major power source of these vehicles. A thermal management system is required for a battery to attain effective operation and long life in all environmental conditions. Although several types of thermal management system are available, there remains a need to address various issues like high power consumption, narrow optimum temperature range and operation in varying climates. Phase change materials can assist in resolving these issues. In this paper, battery thermal management systems for electric and hybrid electric vehicles are reviewed, and challenges and opportunities for battery electric vehicles are discussed. Cooling strategies used in various thermal management systems are explained. Applications of and issues regarding the use of phase change materials in thermal management systems are also reviewed. Potential bottlenecks that need to be addressed in electric vehicle technology are explained, as are important achievement milestones and trends regarding the growth of the electric vehicle industry. It is shown that using graphite can increase thermal conductivity of PCMs by up to 70 W m- 1K- 1. Some commercially available passive thermal management systems for batteries use wax and graphite, which can increase the driving range of an electric scooter from 30 km to 55 km. Copyright © 2016 John Wiley & Sons, Ltd.

Dynamic modeling platform for series hybrid electric vehicles

This paper introduces a simulation model that can be used to develop and test designs and control systems for hybrid electric vehicles (HEVs). The work involves a novel simulating platform, developed in Simulink, where each component of a series HEV is developed using a first-principles approach in a modular fashion, validated by available experimental data and then integrated to form a coupled nonlinear dynamic model. The vehicle model is capable to act as a platform for the design of supervisory control systems (SCSs) that optimize the energy flow in the powertrain. Simulations with two distinct SCSs and two driving cycles are used to analyze the vehicle performance under varying driving and operating conditions. The results demonstrate the applicability of the model for realistic prediction of both vehicle behavior and component energy losses, design optimization and control system design.

Data-Driven Reinforcement Learning–Based Real-Time Energy Management System for Plug-In Hybrid Electric Vehicles

Plug-in hybrid electric vehicles (PHEVs) show great promise in reducing transportation-related fossil fuel consumption and greenhouse gas emissions. Designing an efficient energy management system (EMS) for PHEVs to achieve better fuel economy has been an active research topic for decades. Most of the advanced systems rely either on a priori knowledge of future driving conditions to achieve the optimal but not real-time solution (e.g., using a dynamic programming strategy) or on only current driving situations to achieve a real-time but nonoptimal solution (e.g., rule-based strategy). This paper proposes a reinforcement learning–based real-time EMS for PHEVs to address the trade-off between real-time performance and optimal energy savings. The proposed model can optimize the power-split control in real time while learning the optimal decisions from historical driving cycles. A case study on a real-world commute trip shows that about a 12% fuel saving can be achieved without considering charging opportunities; further, an 8% fuel saving can be achieved when charging opportunities are considered, compared with the standard binary mode control strategy.

Development of an Educational Small-Scale Hybrid Electric Vehicle (HEV) Setup

In this paper, the development of an educational small-scale hybrid electric vehicle (HEV) system is discussed. It consists of all the key components of a real HEV that have been scaled down to provide an ideal test platform to evaluate and study hybrid powertrains. The overall configuration is designed to allow the scaled vehicle to emulate the behavior of a series, parallel, or series-parallel HEV, as well as all-electric operation with minor modifications to the powertrain. The developed test bench not only facilitates hands-on experience but can be considered a safe and affordable solution to providing practical aspects of education in the field of electric and hybrid vehicle technology. Considering the potential of such clean transportation alternatives and their continuous growth, the learning experience obtained using the developed test bench will prove invaluable in preparing next generation professionals for further advancement of this field.

On-Line Controller for Fuel Consumption on Spilt-Type Hybrid Electric Vehicle

This paper proposes a controller used by an extremum seeking (ES) algorithm with semi-optimal controller gains for a hybrid electric vehicle (HEV) system. The semi-optimization gains connect at each output of sample controllers that are given by benchmark problem in advance. These gains are decided according to the approximate convex on fuel economy function via each gains. The target engine speed of EMI controller, ω*, is perturbed with the ES algorithm based on fuel economy and State of Charge (SOC). Furthermore, the proposed algorithm switches controller depending on SOC for restraining of the decline of SOC. The sample controllers have four units, and each unit controller controls the driving of HEV. The sample controllers can drive the HEV. For showing the validity of our proposed controller, fuel economies of the proposed controller and sample controller are compared by simulator for this benchmark. While the fuel economy achieved by the sample controller is 41.44 mpg, the final fuel economy on the proposed method is almost 59.29 mpg, and the final SOC is 64.45%. The performance of fuel economy can be improved by almost 43% compared to the sample controller.

Model predictive control of a power-split hybrid electric vehicle system with slope preview

This paper presents a model predictive control approach for the energy management problem of a power-split hybrid electric vehicle system with slope information. This paper adds three new contributions to this field. First, considering the HEV physical constraints like the speed and torque limits of the engine and motor/generators, and the battery state of charge beforehand makes the optimization and the fuel improvements trustworthy. Second, the performance index design process is nature and causal. Third, using the HEV property, the desired battery state of charge is designed according to the road slopes for better recuperation of free braking energy. Effectiveness of the proposed algorithm is validated by the significant fuel economy improvements using the proposed approach compared with the conventional approach.

An Integrated Cooperative Antilock Braking Control of Regenerative and Mechanical System for a Hybrid Electric Vehicle Based on Intelligent Tire

AbstractWith the development of the global automotive industry, environmental pollution and driving safety have been major problems. This paper focuses on a novel integrated cooperative antilock braking system (ABS) controller, which is used for antilock regenerative braking systems (ARBS) and antilock mechanical braking systems (AMBS) for hybrid electric vehicles (HEV) driving on different road surfaces. An intelligent tire system is utilized to detect varying road surfaces to obtain friction information and optimal operation slip ratio. In addition, the HEV eight‐degree‐of‐freedom dynamics model is developed for ABS control, which includes the LuGre tire model. Adaptive backstepping and finite state machine controllers are explored to maintain optimal wheel slip and regenerate energy, based on wheel slip, battery state of charge (SOC), motor capability torque, and vehicle velocity, and to develop the switching rules of regenerative and mechanical braking systems. In order to evaluate the performance of regenerative and mechanical braking systems on harvesting energy and driving safety, the HEV and LuGre dynamic tire models are developed in MATLAB SIMULINK /SimDriveline. The estimation results of different road surfaces and slip controller effects are tested via the experiments and simulations, finding good braking performances and high regenerative efficiency in different maneuvers.

Model predictive control for hybrid electric vehicle platooning using route information

This paper presents a new model predictive control system for hybrid electric vehicle platooning using route information to improve fuel economy. The new features of this study are as follows. First, a system for hybrid electric vehicle platooning has been developed considering varying the desired vehicle speed. Second, a general model of road slope in a vehicle platoon is developed. Third, the effect of prediction horizon and sampling interval on battery efficiency is analyzed. The model predictive control problem is solved using a discrete numerical computation method, the continuation and generalized minimum residual method. Computer simulation results reveal improvements in fuel economy using the proposed control method.

Subcooled Boiling Heat Transfer for Cooling of Power Electronics in Hybrid Electric Vehicles

At present, single-phase liquid, forced convection cooled heat sinks with fins are used to cool power electronics in hybrid electric vehicles (HEVs). Although use of fins in the cooling channels increases heat transfer rates considerably, a second low-temperature radiator and associated pumping system are still required in HEVs. This additional cooling system adds weight and cost while decreasing the efficiency of HEVs. With the objective of eliminating this additional low-temperature radiator and pumping system in HEVs, an alternative cooling technology, subcooled boiling in the cooling channels, was investigated in the present study. Numerical heat transfer simulations were performed using subcooled boiling in the power electronics cooling channels with the coolant supplied from the existing main engine cooling system. Results show that this subcooled boiling system is capable of removing 25% more heat from the power electronics than the conventional forced convection cooling technology, or it can reduce the junction temperature of the power electronics at the current heat removal rate. With the 25% increased heat transfer option, high heat fluxes up to 250 W/cm2 (typical for wideband-gap semiconductor applications) are possible by using the subcooled boiling system.

Conversor CC-CC Multifásico Bidirecional em Corrente não Isolado Aplicado a Sistemas Elétricos de Tração de Veículos Elétricos e Híbridos

Este artigo revisa as principais características de veículos elétricos (EV) e veículos elétricos híbridos (HEV), as arquiteturas do sistema elétrico de tração que os compõem, e algumas topologias de conversores CC-CC bidirecionais aplicados aos sistemas de tração. Entre as contribuições deste artigo, destaca-se o estudo das diversas possibilidades tecnológicas de armazenamento de energia e das principais características dos sistemas de tração de EVs e HEVs atualmente comercializados, objetivando servir de referencial conceitual aos interessados. Bem como, estuda, projeta e apresenta os testes em malha aberta de um protótipo de um conversor multifásico bidirecional em corrente de três braços com potência de 6 kW. O conversor opera como elevador e abaixador de tensão, apresenta como principais características: reduzida ondulação de corrente e reduzido volume dos capacitores de filtro. Os resultados experimentais obtidos comprovam as especificações, limitações e funcionamento teóricos previstos.

Intelligent Hybrid Electric Vehicle ACC With Coordinated Control of Tracking Ability, Fuel Economy, and Ride Comfort

Adaptive cruise control (ACC) of hybrid electric vehicles (HEVs) has been traditionally developed without an efficient integration with active safety and energy management systems of hybrid powertrains, mainly for facilitating its implementation. This, however, leads to a compromise in the fuel economy of HEVs, since the predictive driving information provided by ACC is not exploited by the energy management system. In order to enhance the energy efficiency and control system integration, a novel ACC system for intelligent HEVs (i-HEV ACC) is developed in this study. The controller is proposed within the framework of nonlinear model predictive control, and a position-based nonlinear longitudinal intervehicle dynamics model is developed. A coordinated optimal control problem for both the tracking safety and the fuel consumption is formulated subject to the constraints on stable tracking. A multistep offline dynamic programming optimization and an online lookup table are used to implement the real-time control algorithm. Experiments are further conducted, which demonstrate that the proposed i-HEV ACC achieves enhanced performance and cooperation in traffic safety, fuel efficiency, and ride comfort.

Optimization of the component sizing for a plug-in hybrid electric vehicle using a genetic algorithm

This paper describes a methodology to optimize the component sizes of a plug-in hybrid electric vehicle in a blended control strategy using a genetic algorithm. For this purpose, first, a fuzzy logic controller with two operating modes is developed for the energy management system of a parallel plug-in hybrid electric vehicle. Then, the battery requirements to drive the vehicle during the all-electric range are determined. In order to optimize the transmission components, multi-objective constrained optimization based on a genetic algorithm is developed, where the objective function consists of the weighted fuel consumption and the weighted exhaust emissions. Also, the driving performance requirements of the vehicle are considered as the constraint functions from which, when they exceed their allowable bounds, the penalty functions are evaluated and added to the objective function. Finally, the results of optimization of the component sizing in a blended control strategy are compared for the optimized data and their default component sizes. The simulation results demonstrate the effectiveness of the approach and noticeable reductions in the equivalent fuel consumption and in the exhaust emissions while ensuring a good longitudinal vehicle performance in various driving cycles.

Evaluation and development of improved braking model for a motor-assisted vehicle using MATLAB/simulink

In recent years, R&D trends in the automobile industry have involved developing vehicles that are eco-friendly and have good fuel economy in order to meet increasingly stringent vehicle emission and fuel economy regulations. Among various environmentallyfriendly vehicles, recently-developed hybrid electric vehicles have been considered a successful technology. To improve the hybrid electric vehicle’s efficiency, a regenerative braking system is applied that can save waste braking energy and fuel consumption. To date, much research has been conducted that is related to regenerative brake systems for motor-assisted vehicles. In this study, a vehicle brake model and DC motor model have been developed to predict the braking and electric characteristics of vehicle and motor by using MATLAB/Simulink code and compared with brake performance reference data. Since generating current is a key factor for regenerative braking in a hybrid electric vehicle (HEV) system, electric characteristics are the focus of this study. Therefore, in order to investigate the electric characteristics, analysis is also performed using the DC motor model. The main results obtained by this study are that the vehicle braking dynamics calculated by MATLAB/Simulink simulation were in reasonable agreement with reference values. Using the vehicle brake model, analysis is carried out for investigating the numerical model’s characteristics and performances at initial velocity, slip ratio and road conditions. Additionally, experimentation is carried out using a chassis dynamometer and a hybrid electric vehicle, and the data are analyzed.

Performance of an Eco‐Driving Model Predictive Control System for HEVs during Car Following

AbstractThis paper presents a model predictive control approach for the energy management problem of hybrid electric vehicles (HEVs) with variable time headway during car following. The new features of this study are as follows. First, a new method using model predictive control with fixed time headway is proposed to improve fuel economy outside the HEV. Second, the battery state of charge profile is scheduled systematically to improve fuel economy inside the HEV compared with the vehicle spacing influence outside the HEV. Computer simulation results are shown using the approaches with variable time headway and fixed time headway. The conclusion is that the model predictive control approach is effective for the energy management problem of the HEV system during car following.

Indirect Matrix Converter for Hybrid Electric Vehicle Application with Three-Phase and Single-Phase Outputs

This paper presents an indirect matrix converter (IMC) topology for hybrid electric vehicle (HEV) application with three-phase and single-phase outputs. The HEV includes mechanical, electrical, control, and electrochemical systems among others. In the mechanical system, a traction motor and a compressor motor are used to drive the HEV. The traction motor and the compressor motor are usually operated as three-phase and single-phase motors, respectively. In this respect, a dual AC-drive system can operate the traction and the compressor motor simultaneously. Furthermore, compared to a conventional dual matrix converter system, the proposed topology can reduce the number of switches that the dual outputs share with a DC-link. The application of this system for HEV has advantages, like long lifetime and reduced volume due to the lack of a DC-link. The proposed control strategy and modulation schemes ensure the sinusoidal input and output waveforms and bidirectional power transmission. The proposed system for the HEV application is verified by simulation and experiments.