An Intelligent Adaptive Neural Network Controller for a Direct Torque Controlled eCAR Propulsion System
Abstract
:1. Introduction
2. Components of an eCAR Powertrain
2.1. Battery Model
2.2. Motor Model
2.3. Glider Model
2.4. Driver Model
3. Direct Torque Controlled eCAR
4. ANN Controller
- Step 1: Firstly, torque error and the change in torque error are given as inputs to the network. The input vector matrix is given by [X] 2 × 1 with two inputs.
- Step 2: Subsequently, the targets are chosen in order to attain the desired variables of the network. The target vector matrix is given by [t] 1 × 1 with one output.
- Step 3: The weights and biases are then initialized and are updated corresponding to Levenberg–Marquardt optimization algorithm. The output vector matrix is given by [y] where [w]° and [b]° are the weights and biases row matrices, respectively.
- Step 4: The ANN is trained by using the data provided in Step 1 and Step 2, respectively, and by fixing the goal parameter to a minimum. The error data in the form of an error vector matrix (E) is generated to confirm that the desired convergence of the specified goal parameters or epochs during training has been met then training stops.
- Step 5: After the training the network, the optimized value of the steady state error as the output is yielded.
- Step 6: The optimized steady state error output is applied as the input to the PI controller in order to produce the slip speed.
5. Simulation Analysis of an eCAR
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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3Ф, AC Induction Motor | |
---|---|
Motor Power, Pm | 90 kW |
Nominal Voltage, Vn | 380 V, RMS |
Current Rating, In | 200 A |
Variable Frequency, fs | 0–400 Hz |
Constant Power | 90 kW @ 12,000 rpm |
Constant Torque | 120 N-m @ 7200 rpm |
Motor Inertia, J | 1.5 kg-m2 |
Stator Resistance, Rs | 0.021 Ohms |
Rotor Resistance, Rr | 0.016 Ohms |
Stator Reactance, Lls | 0.0164 Henry |
Rotor Reactance, Llr | 0.0167 Henry |
Mutual Inductance, Lm | 0.016 Henry |
Number of Poles, P | 4 |
Nominal Stator Flux, ψs | 0.98 Wb |
Vehicle Dynamics | |
---|---|
Mass of the vehicle, m | 1200 Kg |
Frontal area of the vehicle, Af | 0.2 sq. mt |
Wheel radius, Rw | 0.2794 m |
Coefficient of rolling resistance, Crr | 0.0015 |
Air density, | 1.225 Kg/m3 |
Air drag coefficient, Cd | 0.3 |
Gravitational constant, g | 9.81 Kg/m2 |
Transmission gear ratio, G | 6.842 |
Slope or gradient angle, α | 5° |
Constant A | 74.28 |
Constant B | 2.139 |
Constant C | 0.3922 |
Variables | Values |
---|---|
Number of neurons | 40 |
Hidden layer transfer function | Tansig |
Output layer transfer | Purlin |
Activation function | Net sum |
Gradient | 1.1045 × 10−6 |
Regression | 0.9897 |
Training sets | 100 |
Testing sets | 50 |
Training method | Levenberg–Marquardt back-propagation |
Optimization function | Mean square error (MSE) |
Learning method | Gradient descent weight and bias |
S. No | Input 1 | Input 2 | Output | Error | Target |
---|---|---|---|---|---|
1 | 0 | 0 | 282.9359 | −282.936 | 0 |
2 | 0.288448 | 9951.406 | 280.8798 | −272.321 | 8.558594 |
3 | −0.13429 | 9506.473 | 290.7358 | −275.806 | 14.9301 |
4 | −0.09254 | 9109.208 | 289.5684 | −270.595 | 18.97366 |
5 | −0.09145 | 8760.206 | 289.3706 | −267.585 | 21.78575 |
6 | 0.676865 | 8492.981 | 270.4763 | −247.518 | 22.95849 |
7 | −0.19752 | 8213.084 | 291.5531 | −266.385 | 25.16803 |
8 | −0.82494 | 7985.73 | 305.1781 | −278.464 | 26.7146 |
9 | −0.57358 | 7827.065 | 299.7575 | −272.655 | 27.10218 |
10 | 1.383714 | 7761.038 | 251.6016 | −226.022 | 25.57983 |
Torque Controller Constants | Kp | Ki |
---|---|---|
Conventional Controller | 0.2870 | 0.0167 |
ANN Controller | 0.2137 | 0.01 |
Drive Cycle | ECE (R15) | HWFET | NYCC | ||||
---|---|---|---|---|---|---|---|
Parameters | PI | ANN-PI | PI | ANN-PI | PI | ANN-PI | |
Avg torque ripple (Nm) | 28 | 4 | 12 | 2 | 10 | 2 | |
Avg flux ripple (wb) | 0.3 | 0.15 | 0.2 | 0.14 | 0.18 | 0.12 | |
Avg fuel consumption (J) | 120 | 102 | 3250 | 3133 | 250 | 176 | |
Avg output power (kW) | 50 | 48 | 120 | 90 | 145 | 115 |
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Banda, G.; Kolli, S.G. An Intelligent Adaptive Neural Network Controller for a Direct Torque Controlled eCAR Propulsion System. World Electr. Veh. J. 2021, 12, 44. https://doi.org/10.3390/wevj12010044
Banda G, Kolli SG. An Intelligent Adaptive Neural Network Controller for a Direct Torque Controlled eCAR Propulsion System. World Electric Vehicle Journal. 2021; 12(1):44. https://doi.org/10.3390/wevj12010044
Chicago/Turabian StyleBanda, Gururaj, and Sri Gowri Kolli. 2021. "An Intelligent Adaptive Neural Network Controller for a Direct Torque Controlled eCAR Propulsion System" World Electric Vehicle Journal 12, no. 1: 44. https://doi.org/10.3390/wevj12010044