In-Vehicle Identification of an Induction Machine Model for Operational Torque Prediction

Forrier, Bart; Loth, Alexander; Mollet, Yves

doi:10.1109/icem49940.2020.9270731

Cited by 4 publications

(6 citation statements)

References 14 publications

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“…drives the rear axle through a reduction with a factor of 7.13. The outer diameter of the wheel was 576 mm [36].…”

Section: Installed Setup and Measurementsmentioning

confidence: 99%

“…F e (N), F l (N), r (m), and G r are the electrical thrust, load, radius of the wheel, and gearbox ratio, respectively. The source (battery), inverter, and motor are connected in the arrangement [36], as shown in Figure 18. The source (battery), inverter, and motor are connected in the arrangement [36], as shown in Figure 18.…”

Section: Installed Setup and Measurementsmentioning

confidence: 99%

“…The source (battery), inverter, and motor are connected in the arrangement [36], as shown in Figure 18. The source (battery), inverter, and motor are connected in the arrangement [36], as shown in Figure 18. The data are logged from the installed test setup [36], as shown in Figure 19, and used to validate the simulation model.…”

Section: Installed Setup and Measurementsmentioning

confidence: 99%

“…The source (battery), inverter, and motor are connected in the arrangement [36], as shown in Figure 18. The data are logged from the installed test setup [36], as shown in Figure 19, and used to validate the simulation model. The DC side of the three-phase inverter is connected to the battery, and the AC side is connected to the three-phase induction motor.…”

Section: Installed Setup and Measurementsmentioning

confidence: 99%

See 3 more Smart Citations

Accurate Electro-Thermal Computational Model Design and Validation for Inverters of Automotive Electric Drivetrain Applications

et al. 2022

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This paper proposes the fast and accurate electro-thermal model of the existing Simrod three-phase inverter for an electric vehicle (EV) application. The research focuses on analytical and dynamic electro-thermal models of inverters that can be applied for multi-applications. The optimal design approach of passive filters is presented for the DC and AC sides of the inverter. The analytical model has been established, including a mathematical representation of the inverter and induction motor (IM). The high-fidelity electro-thermal simulation model of an inverter with integrated power loss and thermal model is established. The state-space thermal model (for the IRFS4115PbF device) has been created and incorporated into the MATLAB simulation. The simulation model is then validated with the PLECS software-based thermal model to confirm the accuracy. Indirect field-oriented control (IFOC) is designed for squirrel-cage IM at a maximum power rating of 45 kW and implemented on MATLAB/Simulink. The comparative analysis between the real and simulated results is performed to validate the simulation model at a specific speed, torque, and current. Furthermore, the electro-thermal simulation model has been validated with experimental data using efficiency and temperature comparison. The developed simulation model is beneficial for designing, optimizing, and developing advanced technology-based inverters to achieve higher efficiency at a particular operating range of temperature and power quality. The new European driving cycle (NEDC) speed profile simulation results are demonstrated.

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“…drives the rear axle through a reduction with a factor of 7.13. The outer diameter of the wheel was 576 mm [36].…”

Section: Installed Setup and Measurementsmentioning

confidence: 99%

Section: Installed Setup and Measurementsmentioning

confidence: 99%

Section: Installed Setup and Measurementsmentioning

confidence: 99%

Section: Installed Setup and Measurementsmentioning

confidence: 99%

See 2 more Smart Citations

Accurate Electro-Thermal Computational Model Design and Validation for Inverters of Automotive Electric Drivetrain Applications

et al. 2022

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“…The system identification and controller design developed based on the NARX neural networks presented a remarkable ability to rapidly track the set point variations. NARX can characterize the torque of induction machine (IM) motors as part of an electric powertrain [18]. The testing data for the NARX model were generated using a model-based EV model, and the results were validated with the data from virtually performing the WLTP standard driving cycle.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Torque Demand of a Battery Electric Vehicle for Real-World Driving Maneuvers Using the NARX Technique

Alhanouti,

Gauterin

2024

WEVJ

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An identification technique is proposed to create a relation between the accelerator pedal position and the corresponding driving moment. This step is beneficial to replace the complex physical model of the vehicle control unit, especially when the sufficient information needed to model certain functionalities of the vehicle control unit are unavailable. We utilized the nonlinear autoregressive exogenous model to regenerate the electric motor torque demand, given the accelerator pedal position, the motor’s angular speed, and the vehicle’s speed. This model proved to be extremely efficient in representing this highly complex relationship. The data employed for the identification process were chosen from an actual three-dimensional route with sudden changes of a dynamic nature in the driving mode, different speed limits, and elevations, as an attempt to thoroughly cover the driving moment scope based on the alternation of the given inputs. Analyzing the selected route data points showed the widespread coverage of the motor’s operational scope compared to a standard driving cycle. The training outcome revealed that linear modeling is inadequate for identifying the targeted system, and has a substantial estimation error. Adding the nonlinearity feature to the model led to an exceptionally high accuracy for the estimation and validation datasets. The main finding of this work is that the combined model from the nonlinear autoregressive exogenous and the sigmoid network enables the accurate modeling of highly nonlinear dynamic systems. Accordingly, the maximum absolute estimation error for the motor’s moment was less than 10 Nm during the real-world driving maneuver. The highest errors are found around the maximum motor’s moment. Finally, the model is validated with measurements from an actual field test maneuver. The identified model predicted the driving moment with a correlation of 0.994.

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Privacy-Preserving Active Learning on the Internet of 5G Connected Artificial Intelligence of Things

Ahmed

Lin

Srivastava

2022

IEEE Internet Things M.

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In-Vehicle Identification of an Induction Machine Model for Operational Torque Prediction

Cited by 4 publications

References 14 publications

Accurate Electro-Thermal Computational Model Design and Validation for Inverters of Automotive Electric Drivetrain Applications

Accurate Electro-Thermal Computational Model Design and Validation for Inverters of Automotive Electric Drivetrain Applications

Predicting the Torque Demand of a Battery Electric Vehicle for Real-World Driving Maneuvers Using the NARX Technique

Privacy-Preserving Active Learning on the Internet of 5G Connected Artificial Intelligence of Things

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