Real‐time electro‐thermal modelling and predictive control design of resonant power converter in full electric vehicle applications

Dini, Pierpaolo; Ariaudo, Giorgio; Botto, Gianluca; Greca, Francesco La; Saponara, Sergio

doi:10.1049/pel2.12527

Cited by 20 publications

(11 citation statements)

References 47 publications

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“…The estimation of losses in inverter components is an essential point to obtain a realistic model in power electronic systems [69][70][71][72][73][74][75][76].…”

Section: Inverter Losses Modelingmentioning

confidence: 99%

Real‐time monitoring and ageing detection algorithm design with application on SiC‐based automotive power drive system

Dini,

Basso,

Saponara

et al. 2024

IET Power Electronics

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The article describes an innovative methodology for the design and experimental validation of monitoring and anomaly detection algorithms, with a particular focus on the aging phenomenon, linked to the anomalous modification of the , in devices switching in power electronic systems integrated into modern high‐performance electrified vehicles. The case study concerns an electric drive for fully electrified vehicles, in which a three‐phase axial flux synchronous motor integrated into a wheel motor (Elaphe) is used and in which a high‐efficiency three‐phase inverter, designed with SiC technology (silicon carbide). The article proposes the design and validation of the innovative aging monitoring and detection system, in four consecutive phases. The first phase involves the creation of a real‐time model of electric drive, validated through experimental data extrapolated directly during a WLTP (Worldwide Harmonized Light Vehicle Test Procedure) test. The second phase consists of the creation of a virtual dataset representative of the aging phenomenon, via an anomaly injection procedure, emulating this phenomenon with a scaling factor (depending on the value of the ) on the current phase of the motor, relating to the inverter branch whose SiC device is affected. The third phase concerns the design of an estimator of the , based on an ANN (Artificial Neural Network) regression model, and involves a data manipulation phase with features extraction and reduction techniques. The fourth and final phase, involves the experimental validation of the method, through PIL (Processor‐In‐the‐Loop) tests, integrating the monitoring algorithm (consisting of a real‐time model and AI‐based regression model) on the NXPs32k144 embedded platform (based on Cortex‐M4), making the algorithm interact with the electric drive model on which anomaly injection is applied.

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“…The estimation of losses in inverter components is an essential point to obtain a realistic model in power electronic systems [69][70][71][72][73][74][75][76].…”

Section: Inverter Losses Modelingmentioning

confidence: 99%

Real‐time monitoring and ageing detection algorithm design with application on SiC‐based automotive power drive system

Dini,

Basso,

Saponara

et al. 2024

IET Power Electronics

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“…Accurately modeling and controlling the temperature of power devices used in inverters can help improve their performance and reduce their susceptibility to damage. Hence, it is evident that the literature has presented real-time temperature control models [15,16].…”

Section: Hmentioning

confidence: 99%

Optimization of Proportional–Integral (PI) and Fractional-Order Proportional–Integral (FOPI) Parameters Using Particle Swarm Optimization/Genetic Algorithm (PSO/GA) in a DC/DC Converter for Improving the Performance of Proton-Exchange Membrane Fuel Cells

Benteşen Yakut

2024

Energies

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In this article, the control of a DC/DC converter was carried out using the proposed methods of conventional PI, PSO-based PI, PSO-based FOPI, GA-based PI, and GA-based FOPI controllers in order to improve the performance of PEMFCs. Simulink models of a PEMFC model with two inputs—hydrogen consumption and oxygen air flow—and with controllers were developed. Then, the outputs of a system based on conventional PI were compared with the proposed methods. IAE, ISTE, and ITAE were employed as fitness functions in optimization algorithms such as PSO and GA. Fitness function value, maximum overshoot, and rising time were utilized as metrics to compare the performance of the methods. PI and FOPI parameters were optimized with the proposed methods and the results were compared with traditional PI in which the optimum parameters were determined by an empirical approach. This research study indicates that the proposed methods perform better than the conventional PI method. However, it becomes apparent that the GA-FOPI approach outperforms the others. The simulation result also shows that the PEMFC model with conventional PI and FOPI controllers in which the controller parameters are tuned using PSO and GA has an acceptable control performance.

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“…All of this was seamlessly integrated into the ROS and Gazebo ecosystem, enabling efficient communication between the wheelchair model and other components of the robotic system. Thanks to this integration, we could simulate and evaluate the performance of the model under a wide range of environmental conditions and operational situations, ensuring that our virtual wheelchair is at the forefront in terms of realism and functionality [114,115]. Our advanced implementation of actuator dynamics and drive control within ROS/Gazebo allowed us to offer an exceptionally accurate and functional simulation of the wheelchair, furthering our commitment to creating innovative and cutting-edge solutions in the field of robotics and mobility assistance.…”

Section: System Definitionmentioning

confidence: 99%

Design of an Assisted Driving System for Obstacle Avoidance Based on Reinforcement Learning Applied to Electrified Wheelchairs

Pacini,

Dini,

Fanucci

2024

Electronics

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Driving a motorized wheelchair is not without risk and requires high cognitive effort to obtain good environmental perception. Therefore, people with severe disabilities are at risk, potentially lowering their social engagement, and thus, affecting their overall well-being. Therefore, we designed a cooperative driving system for obstacle avoidance based on a trained reinforcement learning (RL) algorithm. The system takes the desired direction and speed from the user via a joystick and the obstacle distribution from a LiDAR placed in front of the wheelchair. Considering both inputs, the system outputs a pair of forward and rotational speeds that ensure obstacle avoidance while being as close as possible to the user commands. We validated it through simulations and compared it with a vector field histogram (VFH). The preliminary results show that the RL algorithm does not disruptively alter the user intention, reduces the number of collisions, and provides better door passages than a VFH; furthermore, it can be integrated on an embedded device. However, it still suffers from higher jerkiness.

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Real‐time electro‐thermal modelling and predictive control design of resonant power converter in full electric vehicle applications

Cited by 20 publications

References 47 publications

Real‐time monitoring and ageing detection algorithm design with application on SiC‐based automotive power drive system

Real‐time monitoring and ageing detection algorithm design with application on SiC‐based automotive power drive system

Optimization of Proportional–Integral (PI) and Fractional-Order Proportional–Integral (FOPI) Parameters Using Particle Swarm Optimization/Genetic Algorithm (PSO/GA) in a DC/DC Converter for Improving the Performance of Proton-Exchange Membrane Fuel Cells

Design of an Assisted Driving System for Obstacle Avoidance Based on Reinforcement Learning Applied to Electrified Wheelchairs

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