Development of a software platform for a plug-in hybrid electric vehicle simulator

2012 ◽  
Vol 2 (1) ◽  
Author(s):  
Athanasios Karlis ◽  
Eric Bibeau ◽  
Paul Zanetel ◽  
Zelon Lye
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Alternative Energy ◽  
Hybrid Electric Vehicle ◽  
Energy Mix ◽  
Electricity Use ◽  
Transportation Sector ◽  
Battery Capacity ◽  
Vehicle Simulator ◽  
Hybrid Electric

AbstractElectricity use for transportation has had limited applications because of battery storage range issues, although many recent successful demonstrations of electric vehicles have been achieved. Renewable biofuels such as biodiesel and bioethanol also contribute only a small percentage of the overall energy mix for mobility. Recent advances in hybrid technologies have significantly increased vehicle efficiencies. More importantly, hybridization now allows a significant reduction in battery capacity requirements compared to pure electric vehicles, allowing electricity to be used in the overall energy mix in the transportation sector. This paper presents an effort made to develop a Plug-in Hybrid Electric Vehicle (PHEV) platform that can act as a comprehensive alternative energy vehicle simulator. Its goal is to help in solving the pressing needs of the transportation sector, both in terms of contributing data to aid policy decisions for reducing fossil fuel use, and to support research in this important area. The Simulator will allow analysing different vehicle configurations, and control strategies with regards to renewable and non-renewable fuel and electricity sources. The simulation platform models the fundamental aspects of PHEV components, that is, process control, heat transfer, chemical reactions, thermodynamics and fluid properties. The outcomes of the Simulator are: (i) determining the optimal combination of fuels and grid electricity use, (ii) performing greenhouse gas calculations based on emerging protocols being developed, and (iii) optimizing the efficient and proper use of renewable energy sources in a carbon constrained world.

Multirate Integration in Hybrid Electric Vehicle Virtual Proving Grounds

1998 ◽  
Author(s):  
Dario Solis ◽  
Chris Schwarz
Keyword(s):  
Real Time ◽  
Electric Vehicle ◽  
Time Scales ◽  
Electric Vehicles ◽  
Time Scale ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Differential Algebraic Equations ◽  
Vehicle Systems ◽  
Hybrid Electric

Abstract In recent years technology development for the design of electric and hybrid-electric vehicle systems has reached a peak, due to ever increasing restrictions on fuel economy and reduced vehicle emissions. An international race among car manufacturers to bring production hybrid-electric vehicles to market has generated a great deal of interest in the scientific community. The design of these systems requires development of new simulation and optimization tools. In this paper, a description of a real-time numerical environment for Virtual Proving Grounds studies for hybrid-electric vehicles is presented. Within this environment, vehicle models are developed using a recursive multibody dynamics formulation that results in a set of Differential-Algebraic Equations (DAE), and vehicle subsystem models are created using Ordinary Differential Equations (ODE). Based on engineering knowledge of vehicle systems, two time scales are identified. The first time scale, referred to as slow time scale, contains generalized coordinates describing the mechanical vehicle system that includs the chassis, steering rack, and suspension assemblies. The second time scale, referred to as fast time scale, contains the hybrid-electric powertrain components and vehicle tires. Multirate techniques to integrate the combined set of DAE and ODE in two time scales are used to obtain computational gains that will allow solution of the system’s governing equations for state derivatives, and efficient numerical integration in real time.

Control Strategy of Hybrid Electric Vehicles Based on Driving Style Identification

2014 ◽  
Vol 945-949 ◽  
pp. 1587-1596
Author(s):  
Xian Zhi Tang ◽  
Shu Jun Yang ◽  
Huai Cheng Xia
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Entropy Theory ◽  
Driving Style ◽  
Identification Error ◽  
Identification Method ◽  
Hybrid Electric

The driving style comprehensive identification method based on the entropy theory is presented. The error and error proportion of each identification result are calculated. The entropy and the variation degree of the identification error of each identification method are calculated based on the definition of information entropy. According to the entropy and the variation degree of the identification error, the weight of each kind of identification method can be determined in the comprehensive identification method, and the driving style comprehensive identification algorithm is derived. The control strategy of hybrid electric vehicles based on the driving style identification is proposed. The economic control strategy and dynamic control strategy are established. Depending on the results of driving style identification, aiming at different kinds of drivers, the mode of control strategies can be adjusted, so the demands of different kinds of drivers can be satisfied. The hybrid electric vehicle simulation model and control strategy model are built, and the simulations have been done. Due to the simulation results, the drivers’ intention comprehensive identification method based on the entropy theory is proved to represent the driver’s driving style systematically and comprehensively, and the hybrid electric vehicle control strategy based on the driving style identification can make the vehicles satisfy different drivers’ demands.

scholarly journals Real-Time Co-optimization of Vehicle Route and Speed Using Generic Algorithm for Improved Fuel Economy

2019 ◽  
Vol 141 (03) ◽  
pp. S08-S15
Author(s):  
Guoming G. Zhu ◽  
Chengsheng Miao
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Fuel Economy ◽  
Autonomous Vehicles ◽  
Hybrid Electric Vehicle ◽  
High Efficiency ◽  
Combustion Engine ◽  
Cruise Control ◽  
Vehicle Performance ◽  
Hybrid Electric

Making future vehicles intelligent with improved fuel economy and satisfactory emissions are the main drivers for current vehicle research and development. The connected and autonomous vehicles still need years or decades to be widely used in practice. However, some advanced technologies have been developed and deployed for the conventional vehicles to improve the vehicle performance and safety, such as adaptive cruise control (ACC), automatic parking, automatic lane keeping, active safety, super cruise, and so on. On the other hand, the vehicle propulsion system technologies, such as clean and high efficiency combustion, hybrid electric vehicle (HEV), and electric vehicle, are continuously advancing to improve fuel economy with satisfactory emissions for traditional internal combustion engine powered and hybrid electric vehicles or to increase cruise range for electric vehicles.

Design and Analysis of a Series Hybrid Electric Vehicle for Indian Conditions

2012 ◽  
Author(s):  
C. S. Nanda Kumar ◽  
Shankar C. Subramanian
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Electric Motor ◽  
Hybrid Electric Vehicle ◽  
Traction Force ◽  
Drive System ◽  
Maximum Speed ◽  
Performance Requirements ◽  
Series Hybrid Electric Vehicle ◽  
Hybrid Electric

Electric and hybrid vehicles are emerging rapidly in the automotive market as alternatives to the traditional Internal Combustion Engine (ICE) driven vehicles to meet stringent emission standards, environmental and energy concerns. Recently, Electric Vehicles (EVs) and Hybrid Electric Vehicles (HEVs) have been introduced in many countries including India. One configuration of a HEV is the Series Hybrid Electric Vehicle (SHEV). The design and analysis of the drive system of a SHEV under Indian conditions is the focus of this paper. In conventional vehicles, the ICE is the power source that drives the vehicle. The energy from the ICE is distributed to the wheels through the transmission, which is then used to generate the traction force at the tyre-road interface. In a HEV, both the engine and the electric motor provide the energy to drive the vehicle. In a SHEV, the energy generated by the electric motor is transmitted through the transmission to meet the torque demand at the wheels. Based on the driver’s acceleration demand and the state of charge of the battery, the controller manages the ICE, the generator and the battery to supply the required energy to the motor. The motor finally develops the required drive torque to generate the traction force at the wheels to meet the vehicle drive performance requirements like gradeability, acceleration and maximum speed. The objective of this paper is to discuss the design of the drive system of a SHEV. This involves the calculation of the power specifications of the electric motor based on the vehicle drive performance requirements. The equations for performing these calculations are presented. The procedure is then demonstrated by considering a typical Indian commercial vehicle along with its typical vehicle parameter values. A simulation study has also been performed by considering the Indian drive cycle to demonstrate the energy savings obtained by the use of a SHEV.

Development of Fuel Cell Hybrid Electric Vehicle Simulator and HCU

2010 ◽  
Vol 26-28 ◽  
pp. 1110-1114
Author(s):  
Dong Ji Xuan ◽  
Qian Ning ◽  
Zhen Zhe Li ◽  
Tai Hong Cheng ◽  
Yun De Shen
Keyword(s):  
Fuel Cell ◽  
Electric Vehicle ◽  
Control Strategy ◽  
Cell Hybrid ◽  
Hybrid Electric Vehicle ◽  
Driver Model ◽  
Vehicle Speed ◽  
Battery Capacity ◽  
Hybrid Electric ◽  
Fuel Cell Hybrid

Based on the Matlab/Simulink module modeling for Fuel Cell Hybrid Electric Vehicle was carried out, which is comprised of the fuel cell stack model, a DC/DC converter model, a battery model, a motor model, avehiclemodel and a driver model, and Hybrid Control Unit(HCU) was developed. The HCU control strategy also incorporates regenerative braking and recharge for battery capacity recovery. Vehicle speed effect is evaluated in New Europe Driving Cycle. The simulation result that the control strategy implemented by HCU is achievable, and which proves that the mode of Start, Accele_FCBat, Cruise, RE_Brake, Power_FC and Pause operate sequently as well as reliably.

Hybrid Electric Vehicle Energy Management With Battery Thermal Considerations Using Multi-Rate Dynamic Programming

2013 ◽  
Author(s):  
Rajit Johri ◽  
Wei Liang ◽  
Ryan McGee
Keyword(s):  
Dynamic Programming ◽  
Electric Vehicle ◽  
Thermal Management ◽  
Hybrid Electric Vehicle ◽  
Computational Time ◽  
Bulk Temperature ◽  
Thermal Dynamics ◽  
Battery Thermal Management ◽  
Battery Capacity ◽  
Hybrid Electric

Battery capacity and battery thermal management control have a significant impact on the Hybrid Electric Vehicle (HEV) fuel economy. Additionally, battery temperature has a key influence on the battery health in an HEV. In the past, battery temperature and cooling capacity has not been included while performing optimization studies for power management or optimal battery sizing. This paper presents an application of Dynamic Programming (DP) to HEV optimization with battery thermal constraints. The optimization problem is formulated with 3 state variables, namely, the battery State Of Charge (SOC), the engine speed and the battery bulk temperature. This optimization is critical for determining appropriate battery size and battery thermal management design. The proposed problem has a major challenge in computation time due to the large state space. The paper describes a novel multi-rate DP algorithm to reduce the computational challenges associated with the particular class of large-scale problem where states evolve at very different rates. In HEV applications, the battery thermal dynamics is orders of magnitude slower than powertrain dynamics. The proposed DP algorithm provides a novel way of tackling this problem with multiple time rates for DP with each time rate associated with the fast and slow states separately. Additionally, the paper gives possible numerical techniques to reduce the DP computational time and the time reduction for each technique is shown.

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