Hardware-in-the-Loop Simulation of Self-Driving Electric Vehicles by Dynamic Path Planning and Model Predictive Control

Chung, Yi Fang; Yang, Yee‐Pien

doi:10.3390/electronics10192447

Cited by 6 publications

(6 citation statements)

References 23 publications

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“…𝑃𝑃 𝑏𝑏 = 1.5𝐾𝐾 𝑐𝑐 𝑢𝑢 𝑏𝑏 − 𝜏𝜏 𝑏𝑏𝑠𝑠 𝑃𝑃 ̇𝑏𝑏 (22) where 𝑃𝑃 𝑏𝑏 is the applied brake pressure, 𝐾𝐾 𝑐𝑐 is the simple pressure gain, 𝑢𝑢 𝑏𝑏 is the brake setting and 𝜏𝜏 𝑏𝑏𝑠𝑠 is the brake lag. By considering 𝜗𝜗 is as the simple pressure gain and 𝐾𝐾 𝑏𝑏 is the cylinder pressure gain, the brake torque can be calculated as:…”

Section: Mathematical Model Of the In-wheel Motor-based Electric Vehiclementioning

confidence: 99%

“…Although the results have contributed significantly to the development of knowledge about IWM-EV, the results are still weak and questionable. This is because the tests conducted on IWM-EV capabilities are still limited to simulationbased tests [6], [8], [20] and HILS methods [14], [21][22][23] that require some simplification, justification, and assumptions during the tests. It is common knowledge that justification and assumptions may inaccurately reflect the data and likely result in inaccurate predictions [24].…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Testing of In-Wheel Motor Based Electric Vehicle in Longitudinal Direction

Razak,

Ahmad,

Che Hasan

et al. 2023

Int. J. Automot. Mech. Eng.

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This paper presents an investigation into the performance of in-wheel motor (IWM) based electric vehicles (IWM-EV) in the longitudinal direction. The design of IWM-EV is an innovation of the conventional go-kart vehicle with slightly modifications in steering, suspension, and braking system, which then makes use three-phase permanent magnet synchronous in-wheel motor (PMSM-IWM) at both of the rear axle wheels. An extension of that is a simulation of IWM-EV vehicle using a 5-degree-of-freedom vehicle longitudinal model that has been developed by incorporating PMSM-IWM as a drive wheel located at the rear axles. Using the simulation, vehicle dynamic control in the longitudinal direction-based Proportional-Integral-Derivative (PID) controller has also been strategized. As the intention to confirm the capability of the IWM-EV, experimental studies-based real IWM-EV hardware have been conducted. Three dynamic tests that generalized from SAE standard SAE J866-199908, namely acceleration performance at the level pavement (include acceleration tests and acceleration then braking tests) and road gradient tests at constant speeds of 10, 15 and 20 km/h, were used as the testing method. The performance areas evaluated were vehicle body speed, wheel speed, distance travel experienced by the vehicle, IWMs current, drive torque as well as the battery voltage capacity used by the vehicle. The finding indicate that the simulation results and experimental data are similar with less than 5 % error. The outcomes from this study will be considered in the design optimization of a torque vectoring control in the next research study.

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Section: Mathematical Model Of the In-wheel Motor-based Electric Vehiclementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic Testing of In-Wheel Motor Based Electric Vehicle in Longitudinal Direction

Razak,

Ahmad,

Che Hasan

et al. 2023

Int. J. Automot. Mech. Eng.

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“…To summarize, in the case of safety-critical applications in the industry, functional safety and HIL simulation are the most significant methods for reliable production. The following text will discuss the historical overview of different HIL simulation fields through particular examples within the automotive industry [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36], electric drive control [37][38][39][40], power electronics converters [41][42][43][44][45][46][47][48][49], electric grid applications [50][51][52][53][54][55][56][57][58][59], railway traction systems [60][61][62], and education [63][64][65][66]…”

Section: V-models For the Development Procedures And Functional Safetymentioning

confidence: 99%

Hardware-in-the-Loop Simulations: A Historical Overview of Engineering Challenges

2022

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The design of modern industrial products is further improved through the hardware-in-the-loop (HIL) simulation. Realistic simulation is enabled by the closed loop between the hardware under test (HUT) and real-time simulation. Such a system involves a field programmable gate array (FPGA) and digital signal processor (DSP). An HIL model can bypass serious damage to the real object, reduce debugging cost, and, finally, reduce the comprehensive effort during the testing. This paper provides a historical overview of HIL simulations through different engineering challenges, i.e., within automotive, power electronics systems, and different industrial drives. Various platforms, such as National Instruments, dSPACE, Typhoon HIL, or MATLAB Simulink Real-Time toolboxes and Speedgoat hardware systems, offer a powerful tool for efficient and successful investigations in different fields. Therefore, HIL simulation practice must begin already during the university’s education process to prepare the students for professional engagements in the industry, which was also verified experimentally at the end of the paper.

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“…HIL testing has many benefits, as it is faster, more cost-effective, and repeatable than a pure experimental approach while offering greater fidelity than a pure simulated approach (Fathy et al, 2006). HIL testing is used to advance path planning for autonomous vehicles (Chung and Yang, 2021), improve electric drive controller performance (Bouscayrol, 2008), and test regenerative railway braking systems (Pavlović et al, 2021), to name just a few of its applications. This paper focuses specifically on a type of HIL testing called dynamic load emulation, which can be understood as a subset of a larger class of HIL experimental-computational hybrid control approaches across a broad range of applications.…”

Section: Introductionmentioning

confidence: 99%

Hardware-in-the-loop dynamic load emulation of robotic systems actuated by fluidic artificial muscles

Mazzoleni,

Bryant

2024

Journal of Intelligent Material Systems and Structures

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Hardware-in-the-loop (HIL) testing is a popular control system testing method because it bridges the gap between modeling/simulation and experiments. Instead of designing a full hardware-based experiment to validate the results of a simulation, the plant hardware can be replaced with an emulator device that responds to exogenous inputs and effectively emulates the dynamic behavior of a system. This approach can be more cost-effective and modular, since the emulated plant system can be modeled in a simulation environment, implemented on a simplified piece of hardware and changed quickly without having to fabricate new parts. This paper develops the hardware and control scheme for a certain type of HIL device called a dynamic load emulator that consists of a 1-DOF linear hydraulic dynamometer equipped with in-line sensing to measure both its own position and the force exerted on it by a device-under-test. This measured force is passed to a real-time model of the emulated dynamic system. The model outputs the emulated system position, and a closed-loop controller is used to emulate this position. The emulator controller incorporates both model-based feedforward and standard feedback PI control. This paper characterizes the dynamometer-based dynamic load emulator and its controller, determining its hardware limitations and validating its capabilities when experiencing a force input from a linear spring with known parameters. Additionally, this paper demonstrates the ability of the emulator to represent the dynamics of a 1-DOF robotic joint when actuated by a pair of fluidic artificial muscles (FAMs). The primary contribution of this work is to allow for more comprehensive testing of FAM configurations, topologies, and controllers for a wide range of parameters, because the same hardware can be used to emulate multiple systems. As a result, this work will lead to more cost-effective, time-efficient, and energy-efficient designs of robotic systems and the FAMs used to actuate them.

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Hardware-in-the-Loop Simulation of Self-Driving Electric Vehicles by Dynamic Path Planning and Model Predictive Control

Cited by 6 publications

References 23 publications

Dynamic Testing of In-Wheel Motor Based Electric Vehicle in Longitudinal Direction

Dynamic Testing of In-Wheel Motor Based Electric Vehicle in Longitudinal Direction

Hardware-in-the-Loop Simulations: A Historical Overview of Engineering Challenges

Hardware-in-the-loop dynamic load emulation of robotic systems actuated by fluidic artificial muscles

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