Background
Hardware-in-the-loop simulation
Classification
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Signal HIL model as shown in Fig. 1a. In this type of model, the entire system model is simulated in real-time simulation environment while the controller is implemented in hardware. Only control and measurement signal are managed in the model. Therefore, this model is called signal HIL simulation.
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Power HIL model splits the whole system into subsystems which will be designated as tested objects or simulation ones. In this type of model, both power and signal are managed as illustrated in Fig. 1b. This model gives the most accurate test results since it uses actual subsystems as a part of experiment.
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Reduced-scaled HIL model. Its principal is similar to that of power HIL model but the tested power parts are replaced by equivalent subsystems with reduced power. And of course, the replaced subsystems must have the same characteristic with the original ones.
System configuration
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The vehicle moves on the flat surface.
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Effect of roll and pitch motion are neglected.
Vehicle dynamic and kinematic modelling
Vehicle kinematic
Force model
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Tire-road contact forces include longitudinal forces \(F_{xi}\) and lateral forces \(F_{yi}\), \(i=1\ldots4\) with respect to front-left, front-right, rear-left, rear-right wheels
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Air resistance force \(F_{air}\)
Tire model
Sideslip angle and slip ratio
Drivetrain modeling
Hardware and system design
Experimental results
Validation scenarios
Results
Discussion
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Modeling with S-function. The vehicle model is built on Simulink with S-function blocks (Fig. 8), which allow to extract more state variables of studied system for estimation, observation or control purpose. This can be done easily by adding required variables to the output field of the function callback definition.
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Role of the interface system. After re-compiling the model, the output signals presenting the variables are available digitally. In order to proceed further, they must be assigned to the DAC outputs of DS1103. A conditioning interface system is required (Fig. 2) for different EV model and peripheral hardware, as DS1103’s DAC outputs have ±10 voltage range.
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Real-time performance evaluation and other capabilities. For the sake of applications of proposed Signal HIL model, electronic control unit (ECU) design scheme, for instance, the controller is developed on basis of a hardware platform, such as DSP, FPGA or MCU. Beside processing measurement signals of state variables as mentioned above, these platforms must reserve inputs for driving system (accelerator and brake pedal positions, steering angle). The estimation or control algorithm will be implemented and applied a set of control value to the outputs of the platform, i.e. drive motor’s torque command, applied brake torque or steering angle. This control set will then be sent to the HIL model through the interface system. When running the whole control system, it can be seen that, both signal HIL model and driving system are implemented in real-time and obviously, the controller will be tested in real-time. This can evaluate the accuracy and performance of control/estimation algorithm as well as stability of hardware design since the test can be repeated and expanded as needed. Furthermore, by utilizing ability of simulating critical situations, this HIL solution can assess the controller in different modes, especially in fault operation.
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Cost-effective and fast implementation. The HIL model in this paper utilizes only regular equipments. There are 2 main parts of the model, the dSPACE-DS1103 and the driving system, in which, the DS1103 or similar simulation platform is the minimum requirement for almost all HIL model. The driving system is the low cost racing wheel that can be found easily. In addition, since all the vehicle model is built on Simulink with S-function blocks and transfer functions and the connections of the system are really simple, the researcher can develop very fast their own platform for their studies.
Coefficients | Value | Coefficients | Value |
---|---|---|---|
Pacejka’s tire model coefficients
| |||
\(a_0\)
| 1.3 |
\(b_0\)
| 1.57 |
\(a_1\)
| −49.0 |
\(b_1\)
| −48.0 |
\(a_2\)
| 1216.0 |
\(b_2\)
| 1338.0 |
\(a_3\)
| 1632.0 |
\(b_3\)
| 5.8 |
\(a_4\)
| 11 |
\(b_4\)
| 444.0 |
\(a_5\)
| 0.006 |
\(b_5\)
| 0 |
\(a_6\)
| −0.04 |
\(b_6\)
| 0.003 |
\(a_7\)
| −0.4 |
\(b_7\)
| −0.008 |
\(a_8\)
| 0.003 |
\(b_8\)
| 0.66 |
\(a_9\)
| −0.002 |
\(b_9\)
| 0 |
\(a_{10}, a_{13}, a_{14}\)
| 0 |
\(b_{10}\)
| 0 |
\(a_{11}\)
| −11 | ||
\(a_{12}\)
| 0.045 | ||
Drivetrain coefficients
| |||
\(K_s\)
| 0.15 |
\(K_m\)
| 7.84 |
\(K_b\)
| 500 |
\(T_m\)
| 0.5 |
\(K_i\)
| 6.07 |
\(J_x\)
| 100 |
Parameters | Value | Unit | |
---|---|---|---|
Vehicle’s parameters
| |||
\(l_f\)
| 1.275 | m | |
\(l_r\)
| 1.275 | m | |
\(b_f\)
| 1.475 | m | |
\(b_r\)
| 1.475 | m | |
m
| 1080 | kg | |
h
| 0.47 | m | |
\(R_{eff}\)
| 0.3 | m | |
\(J_z\)
| 900 | kg m\(^2\)
| |
Aerodynamic parameters
| |||
\(c_W\)
| 0.29 | N/A | |
A
| 2.49 | m\(^2\)
| |
\(\rho\)
| 1.2041 | kg/m\(^3\)
|