Optimal Control Design and Online Controller-Area-Network Bus Data Analysis for a Light Commercial Hybrid Electric Vehicle
Abstract
:1. Introduction
1.1. Background
1.2. Model-In-The-Loop Development
1.3. Hardware-In-The-Loop Development
- This paper presents an optimal energy development approach for the reference vehicle of the engineering faculty at the University of Debrecen by performing a time-based simulation in MATLAB/Simulink, unlike the study in [57], which is limited to studying the real transformation of the vehicle from conventional to hybrid.
- The battery charge level might not guarantee optimal performance of the HEV as proposed in [19] using the FLC technique. This paper considers almost every aspect of performance evaluation to ensure the optimal fuel economy of the proposed hybrid car.
- The proposed conventional PID controller in [13,14,15] and [58] might not guarantee optimal fuel economy. The computed fitness function in [59,60] did not achieve more stable systems; therefore, good fuel economy could not be realized. An optimal solution is realized in this paper by computing minimum values of the integral error objective functions. In addition to reducing the computational effort of the analytical approach in ref. [15], PMSM is more efficient than the proposed Brushless Direct Current (BLDC) motor as in [60] for EV application.
- The study in [61] found the power transmission efficiency to be approximately 84%. Moreover, the designed EV is 15% (75–60) more efficient in fuel economy than the traditional car with an ICE. Our research has shown that the designed HEV is 31% (91–60) more efficient than the designed conventional vehicle as in [61]. In this paper, a transmission efficiency of 94% is achieved.
2. System Description and Design
2.1. HEV Configuration
2.2. Vehicle Dynamics Description
2.3. Tire Dynamics Description
2.4. ICE Modeling
2.5. Motor Modeling
2.6. Gearbox Design for the Electric Mode
2.7. MATLAB Simulation
2.8. Experimental Set-Up
3. Simulation and Experimental Results
3.1. Simulation Results
3.2. Experimental Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Surface | B | C | D | E |
---|---|---|---|---|
Ice | 4 | 2 | 0.1 | 1 |
Snow | 5 | 2 | 0.3 | 1 |
Wet tarmac | 12 | 2.3 | 0.82 | 1 |
Dry tarmac | 10 | 1.9 | 1 | 0.97 |
Parameters | Values |
---|---|
Stator resistance (R) | 0.07 Ohm |
d-axis Inductance () | 0.0013 H |
q-axis Inductance () | 0.0039 H |
Rotor Flux (Vs) | 0.1447 Weber |
Pole Pairs | 4 |
Sample time (T) | 2 × 10−6 s |
Parameters | Specifications |
---|---|
Peak/Rated Power | 82.3/53.5 kW |
Peak/Rated Torque | 173/78.6 Nm |
Peak/Rated RPM | 8000/6500 rpm |
Time Constant | 0.02 s |
Series Resistance | 0 |
Rotor Inertia | 3.90 × 10−4 kg/m2 |
Rotor Damping | 1 × 10−5 Nm/(rad/s) |
Diameters, d (mm) | Axial Module, m (mm) | Number of Teeth, z | Gear Ratio, i |
---|---|---|---|
d1 | 2.25 | 26 | 1.54 |
d2 | 2.25 | 40 | |
d3 | 2.5 | 20 | 3.4 |
d4 | 2.5 | 68 | |
d5 | 3 | 18 | 2.33 |
d6 | 3 | 42 | |
i = 12.22 |
Vehicle Controller | Speed Ref. | Performance Indices | Energy Economy (kWh/100 km) | ||||
---|---|---|---|---|---|---|---|
IAE | ISE | ITAE | ITSE | ||||
1500 | 2000 | WLTP | 0.0887 | 0.8553 | 0.0037 | 22.07 | |
2000 | 2000 | WLTP | 0.0780 | 0.7670 | 0.0029 | 22.08 | |
2000 | 1500 | WLTP | 0.0574 | 0.5669 | 0.0016 | 22.07 | |
1000 | 600 | WLTP | 0.0029 | 0.0368 | 22.09 | ||
2000 | 700 | WLTP | 0.0918 | 0.0403 | 22.09 |
Vehicle Controller | Speed Ref. | Performance Indices | Energy Economy (kWh/100 km) | ||||
---|---|---|---|---|---|---|---|
𝐾𝑝 | 𝐾𝑖 | IAE | ISE | ITAE | ITSE | ||
700 | 800 | WLTP | 0.3753 | 0.0071 | 3.6593 | 0.0670 | 22.75 |
700 | 600 | WLTP | 0.4243 | 0.0090 | 4.1354 | 0.0856 | 22.76 |
600 | 500 | WLTP | 0.4907 | 0.0121 | 4.7829 | 0.1145 | 22.77 |
1000 | 800 | WLTP | 0.3106 | 0.0048 | 3.0381 | 0.0462 | 22.74 |
2000 | 1000 | WLTP | 0.1387 | 1.3739 | 0.0094 | 22.73 |
Drive Cycle (km/h) | L/100 km | km/L | MPG | TFU (L) | ||
---|---|---|---|---|---|---|
1500 | 2000 | WLTP | 2.769 | 36.11 | 84.95 | 0.02476 |
2000 | 2000 | WLTP | 2.769 | 36.12 | 84.95 | 0.02476 |
2000 | 1500 | WLTP | 2.769 | 36.11 | 84.93 | 0.02476 |
1000 | 600 | WLTP | 2.774 | 36.05 | 84.79 | 0.0248 |
2000 | 700 | WLTP | 2.769 | 36.11 | 84.94 | 0.02476 |
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Babangida, A.; Light Odazie, C.M.; Szemes, P.T. Optimal Control Design and Online Controller-Area-Network Bus Data Analysis for a Light Commercial Hybrid Electric Vehicle. Mathematics 2023, 11, 3436. https://doi.org/10.3390/math11153436
Babangida A, Light Odazie CM, Szemes PT. Optimal Control Design and Online Controller-Area-Network Bus Data Analysis for a Light Commercial Hybrid Electric Vehicle. Mathematics. 2023; 11(15):3436. https://doi.org/10.3390/math11153436
Chicago/Turabian StyleBabangida, Aminu, Chiedozie Maduakolam Light Odazie, and Péter Tamás Szemes. 2023. "Optimal Control Design and Online Controller-Area-Network Bus Data Analysis for a Light Commercial Hybrid Electric Vehicle" Mathematics 11, no. 15: 3436. https://doi.org/10.3390/math11153436
APA StyleBabangida, A., Light Odazie, C. M., & Szemes, P. T. (2023). Optimal Control Design and Online Controller-Area-Network Bus Data Analysis for a Light Commercial Hybrid Electric Vehicle. Mathematics, 11(15), 3436. https://doi.org/10.3390/math11153436