Controller Design for ISG Hybrid Electric Vehicle Based on SAE J1939 Protocol

The Graduate Automotive Technology Education (GATE) Center at the University of Tennessee, Knoxville (UTK) offers courses addressing the simulation, modeling, and control system design of hybrid electric vehicles (HEV). In the Spring of 1999 such a course was conducted to support the UTK FutureCar Challenge entry for 1999. The vehicle modeled is a Dual-configuration Hybrid Electric Vehicle (DHEV) which uses a planetary power-split device similar to the Toyota Hybrid System used in the Toyota “Prius”. The goals of the course included the development of a real-time simulator that could incorporate actual vehicle control hardware in the simulator loop. This “control-hardware-in-the-loop” (CHIL) configuration was used for simulation, control system design, and troubleshooting. This approach allows the simulation of normal vehicle operating conditions as well as emergency fault handling situations in which it may not be desirable to subject the actual prototype vehicle to a given test condition. Additionally, it is possible to do a great deal of control system testing and development without an operating vehicle.

This paper designs a penetration testing platform on the CAN bus. The platform is characterized by the combined functions, such as, CAN bus data acquisition and transmission, data preprocessing and analysis, CAN news automatic detection and construction, CAN communication database establishment, various penetration and attack methods and recoding CAN bus arbitration ID. Aiming at the information security verification of the CAN bus protocol of the embedded system, the penetration testing platform can flexibly and quickly build a testing environment to successfully realize the connection with the system under test and the hidden connection of forged communication nodes, and then carry out fuzz testing verification experiments efficiently and conveniently.

The rear drive hybrid electric vehicle system and control model are established based on the dynamics equation and redevelopment of ADVISOR. After importing the Chinese city bus typical condition in ADVISOR, the power performance and fuel economy of the vehicle are simulated and analyzed. The simulation results show that the hybrid electric vehicle compared with the prototype vehicle has a great improvement on fuel economy, and its power performance can meet the design requirements. The hybrid electric vehicle simulation system has very good feasibility that can provide technical support for the development of experimental prototype, and contribute to shorten the development period and reduce costs. Keywords-ADVISOR redevelopment; hybrid electric vehicle; modeling; simulation.

Current vehicles include electronic features that provide ease and convenience to drivers. These electronic features or nodes rely on in-vehicle communication protocols to ensure functionality. One of the most-widely adopted in-vehicle protocols on the market today is the Controller Area Network, popularly referred to as the CAN bus. The CAN bus is utilized in various modern, sophisticated vehicles. However, as the sophistication levels of vehicles continue to increase, we now see a high rise in attacks against them. These attacks range from simple to more-complex variants, which could have detrimental effects when carried out successfully. Therefore, there is a need to carry out an assessment of the security vulnerabilities that could be exploited within the CAN bus. In this research, we conducted a security vulnerability analysis on the CAN bus protocol by proposing an attack scenario on a CAN bus simulation that exploits the arbitration feature extensively. This feature determines which message is sent via the bus in the event that two or more nodes attempt to send a message at the same time. It achieves this by prioritizing messages with lower identifiers. Our analysis revealed that an attacker can spoof a message ID to gain high priority, continuously injecting messages with the spoofed ID. As a result, this prevents the transmission of legitimate messages, impacting the vehicle's operations. We identified significant risks in the CAN protocol, including spoofing, injection, and Denial of Service. Furthermore, we examined the latency of the CAN-enabled system under attack, finding that the compromised node (the attacker's device) consistently achieved the lowest latency due to message arbitration. This demonstrates the potential for an attacker to take control of the bus, injecting messages without contention, thereby disrupting the normal operations of the vehicle, which could potentially compromise safety.

Through the prevalence of sustainable ideas, automobiles are increasingly pursuing environmental protection strategies for green design, the non-traditional hybrid electric vehicles (HEV) are promoted continuously. If the company can add emotional value to the modeling of HEV, it will be helpful to its sustainable design and sales promotion of it. Therefore, an innovative model combining fuzzy linguistic preference relations (FLPR) and fuzzy quality function deployment (QFD) is proposed here to explore the connection between customer sentiment and the front view of the HEV. Compared with the previous methods, FLPR has the advantages of fewer comparison times and high consistency. First, find out the customer’s emotional expectations and attribute weight ranking for HEV through FLPR, and import customer requirements (CRs) on the left side of fuzzy QFD. Second, the grey prediction model was used to screen out the key engineering features (ECs) and the initial weight of HEV. Finally, based on human subjective imprecise natural semantics, fuzzy QFD established a matrix association between CRs and key ECs, then finally obtained the optimal combination of ECs' final weight and morphological design. The results can assist designers to shorten product development cycles and improve customers' emotional satisfaction, which provides a theoretical reference for the sustainable design and marketing of environmentally friendly cars in the future.

The interaction of multiple domains, the evolving technologies and the relative lack of experience with respect to conventional vehicle development, compounds the complexities within the hybrid electric vehicle (HEV) design space. In order to meet the needs of efficient and flexible HEV powertrain modelling within this design space, a parallel is drawn with the development of complex software systems. This paper proposes a modelling method that makes use of object-oriented modelling principles for the design and development of HEV powertrain models. A case study shows how the method is used in developing a library of reusable, replaceable and maintainable models for HEV powertrain modelling.

Embry Riddle Aeronautical University is part of EcoCar: The NeXt Challenge, an advanced vehicle competition run by Argonne National Labs and sponsored by General Motors and the Department of Energy. The competition tasks 16 schools around the country with designing and implementing the most efficient vehicle architecture. As part of the EcoCar Challenge, Embry Riddle Aeronautical University is working on developing a controls strategy for a Plug in Hybrid Electric Vehicle. The control system is designed to optimize efficiency and consumer acceptability by allowing the EcoEagles to control each of the cars sub-systems. Control is done using CAN bus communication that utilizes National Instruments (NI) single board reconfigurable input / output (sbRIO) real time hardware. The EcoEagles powertrain architecture includes GM’s two-mode hybrid electric transmission which contains two 55kW electric motors, a 1.3 liter turbo diesel engine running on B20 biodiesel, and a 12.8 kWh lithium-ion battery pack produced by A123 Systems. Each component has a control module that interfaces directly with the subsystems and hardware on the vehicle. These controllers are: the Traction Power Inverter Module (TPIM), the Engine Control Module (ECM), and the Battery Pack Control Module (BPCM). Vehicle control and communication between these modules is managed by the EcoEagles, two controllers called the Supervisory Control Unit (SCU) and the Gateway (GW). The purpose of the gateway is to control the flow of CAN communication between modules and to isolate the ECM and BPCM from the vehicle to avoid data interference. Communication is done on two separate CAN buses, the Power Train Expansion Bus (PTEB), and the High Speed Bus (HS). The controls diagram can be seen in Figure 1. The paper will go into detail on shift strategy and engine operation where optimization was used to maintain efficient operation of the engine. The paper will also describe the control strategy that was developed using coupled LabVIEW Statecharts [1] with CAN messaging inputs from all of the control modules in order to maintain safe efficient operation.

The paper proposes a Hybrid Electric Vehicle (HEV) design based on the installation of a fuel cell (FC) module in the existing Daewoo Tico electric vehicle to increase its range in urban areas. Installing an FC module supplied by a 2 kg hydrogen tank would not significantly increase the mass of the electric vehicle, and the charging time of the hydrogen tank is lower than the battery charging time. For design analysis, a model was created in the MATLAB/Simulink software package. The model simulates vehicle range at different HEV speeds for Absorbent Glass Mat (AGM) and Proton Exchange Membrane Fuel Cell (PEMFC) power sources. The greatest anticipated benefit derived from the model analysis relates to velocities ranging from 20 km/h to 30 km/h, although the optimal HEV velocity in an urban area is in the range of 30 km/h to 40 km/h. The results indicate that this conversion of Electric Vehicle (EV) to HEV would bring a benefit of 87.4% in terms of vehicle range in urban areas. Therefore, the result of the conversion in this case is a vehicle with sub-optimal characteristics, which are nevertheless very close to optimal.

The work presented in this paper is a progression to previous research which developed an overcurrent-tolerant prediction model. This paper presents some of the modelling and development techniques used for the previous research, but more emphasis is placed on the requirements of the case study; whereby an aeroplane pushback tug is converted into a series Hybrid Electric Vehicle (HEV). An iterative design process enabled the traction motor, transmission, generator and battery pack parameters to be tailored for this vehicle’s unique duty cycle. A MATLAB/Simulink model was developed to simulate the existing internal combustion engine powertrain as well as the series HEV equivalent for comparative analysis and validation purposes. The HEV design was validated by comparing the simulation results to recorded real-world data collected from the existing vehicle (torque, speeds etc.). The HEV simulations provided greater fuel savings and reduced emissions over the daily duty cycle in comparison to the existing vehicle.

The realization of the first prototype of a vehicle requires several tests of the algorithms implemented on the electronic control unit (ECU). This represents an important step for conventional vehicles, which becomes fundamental when dealing with unmanned vehicles. Since there is no human supervision, most critical tasks are handled by the control unit, which results in higher complexity for the control algorithms. In this work, a software-in-the-loop (SiL) test bench is used to validate the control algorithms of a vehicle control unit (VCU) for a driverless railway vehicle (DLRV). The VCU manages the control of the traction motors, pneumatic braking systems, and range extender, as well as control of the hybrid powertrain configuration to guarantee a high level of availability via the use of redundant systems. The SiL test bench has been developed in a Simulink real-time environment, where the vehicle model is simulated along with its fundamental subsystems. The model communicates with the VCU through a CAN bus protocol in the same way that it will operate with a real vehicle. The proposed method can be used to simulate many mission profiles for the DLRV, which may last several hours each. Moreover, this kind of test bench ensures a high time resolution, which allows one to find solutions for problems which occur with a time scale that is much smaller than the simulation time scale.

In the bench experiment of electric drive vehicle, the experimental data are mostly presented in the form of tables and curves, which cannot predict the actual motion of the vehicle well. With the development of computer technology, virtual reality can be used in automobile bench experiment. The existing virtual simulation platforms simulate vehicle motion in off-line form, which cannot be combined with real-time experimental data, and there are shortcomings in vehicle kinematics performance analysis. In order to solve this problem, this paper proposes a vehicle real-time virtual simulation platform based on virtual reality fusion. The whole vehicle and its internal coupling mechanism were modeled with CAD modeling technology, and the motion scene of the model was designed with 3D rendering technology. CAN bus protocol is used to obtain external data in real time. Based on the Virtools virtual simulation software, the internal algorithm is constructed to control the real-time simulation model and display the vehicle movement. Finally, an example is carried out to verify the feasibility and practicability of the simulation platform.

Bolometric experiments searching for rare events usually require an extremely low radioactive background to prevent spurious signals from mimicking those of interest, spoiling the sensitivity of the apparatus. In such contexts, radioactive sources cannot be used to produce a known signal to calibrate the measured energy spectrum during data taking. In this paper we present an instrument designed to generate ultra-stable and very precise calibrating pulses, which can be used to stabilize the response of bolometers during data taking. The instrument is characterized by the presence of multi-outputs, a completely programmable pulse width and amplitude and a dedicated daisy-chained optical trigger line. It can be fully controlled and monitored remotely via CAN bus protocol. An energy resolution of the order of 20 eV FWHM at 1 MeV (2 eV FWHM at 10 keV) and a thermal stability of the order of 0.1 ppm/̂C have been achieved. The device can also provide an adjustable power to compensate the low frequency thermal fluctuations that typically occur in cryogenic experiments.

<div class="htmlview paragraph">This work presents the analytic and experimental results relating engine performance and operating schedule with fuel cost and tail-pipe emissions for the series hybrid electric vehicle (HEV) design. Results are based on vehicle data from the electric G-van produced by Conceptor Industries. A computer simulation developed using basic principles predicts the battery demand over a specified vehicle operation profile. Engine tests on the effects of engine power level and number of starts show that while fuel consumption did not vary, carbon monoxide (CO) emissions did show an increase. The engine used was a 2-cylinder, spark ignited, 4-stroke, carbureted engine and did not utilize exhaust after-treatment. The results of the engine tests and simulation data are combined to predict the CO emissions and fuel cost for the series HEV design. The estimated CO output was 18.7 g/km, and the fuel cost was estimated between 4.3 and 4.5 cents/km. The estimated CO emissions are almost 9 times higher than the 1994 federal standards.</div>

Based on the introduction of the mechanical structure and working principle of the new compound power-split hybrid electric vehicle, this paper studies the minimum equivalent fuel consumption energy management control strategy based on the minimum principle and builds the model and simulation environment through Simulink.Compared with the traditional internal combustion engine prototype vehicle, the fuel consumption per 100 kilometers under the NEDC cycle condition has significantly improved the fuel economy of the whole vehicle.The results show that the optimal control strategy formulated in this paper can realize the working mode of hybrid electric vehicles and achieve better fuel saving and control effects.

This paper presents a detailed derivation of a permanent magnet synchronous motor, which may be used as the electric power train for the simulation of a hybrid electric vehicle. A torque tracking control of the permanent magnet synchronous motor is developed by using an adaptive proportional-integral-derivative controller. Several tests such as step function, saw tooth function, sine wave function and square wave function were used in order to examine the performance of the proposed control structure. The effectiveness of the proposed controller was verified and compared with the same system under a PID controller and the desired control. The result of the observations shows that the proposed control structure proves to be effective in tracking the desired torque with a good response. The findings of this study will be considered in the design, optimisation and experimentation of series hybrid electric vehicle.