Eee projects on autonomous electric vehicle obstacle avoidance — Simulation & Power Modeling

Abstract — Eee projects on autonomous electric vehicle obstacle avoidance

Eee Projects On Autonomous Electric Vehicle Obstacle Avoidance: Power Electronics, MATLAB/Simulink & Circuit Modeling Integrated Analysis: MATLAB/Simulink · MOSFET/IGBT Gate Drivers · SPWM/SVPWM · THD Spectrum · ~6602 Words Chapter 1: Power Electronics Overview, System Specifications & Topology Modeling With the continuous advancements in science and technology, there is an increasing focus on environmental sustainability, leading to heightened interest in autonomous electric vehicles (AEVs) . AEVs hold significant potential for enhancing electric mobility, energy efficiency, environmental preservation, and driving capabilities. They offer numerous advantages that could revolutionize transportation and urban lifestyles, notably improving road safety by reducing human error—a leading cause of accidents. This paper presents a comprehensive simulation of an AEV by developing and integrating models for the driving system, battery, motor, transmission, and vehicle body within the MATLAB/Simulink environment. Each component is configured and interconnected to create a pure vehicle model. The simulation is performed under UDDS cycle conditions to evaluate the vehicle’s performance in speed maintenance, driving distance, acceleration capabilities, and battery state-of-charge (SoC) dynamics . The results demonstrate that the car exhibits strong output characteristics. Furthermore, the driver model incorporates an energy brake recovery function, which, when set to a 50% recovery efficiency, increases the driving distance by 25% compared to a vehicle without this function.

Analytical & State-Space Control Methodology

While traditional internal combustion engine (ICE) vehicle technology has advanced considerably, the rapid increase in vehicle numbers has led to heightened fossil fuel consumption and significant environmental concerns. This has accelerated the need for alternative automotive technologies that do not rely on conventional energy sources ( 1 ) . Autonomous electric vehicles (AEVs) , known for their energy efficiency, sustainability, and superior driving capabilities, have emerged as a key focus for the 21st-century automotive industry ( 2 ) . Research into AEVs has expanded across various dimensions, particularly in enhancing their autonomous capabilities and integrating advanced electric powertrains ( 3 , 4 ) . For instance, to address the impact of drivetrain flexibility in AEVs, researchers have developed electric drive systems that incorporate a double-inertia model augmented with notch filters to improve transmission accuracy ( 5 ) . Additionally, efforts to optimize the powertrain parameters of AEVs under performance constraints have led to driving range improvements and power consumption reductions ( 6 ) . The power battery, often considered the “core” of an AEV, has also been a major research focus ( 7 ) . Predictive models for battery state-of-charge (SoC) that use advanced algorithms like decision tree regression and light gradient boosting have shown to be very accurate, enhancing battery management for self-driving operations ( 8 ) .

Simulink Waveforms & Power Quality Findings

Moreover, improved modeling techniques have addressed SoC estimation challenges influenced by factors like charge and discharge rates and temperature ( 1 ) . Enhancements to the refined Kalman filter parameters have made SoC estimation more accurate, which is crucial for AEV dependability. As the demand for AEVs grows, research into their charging infrastructure has also intensified. Studies have developed optimization models that balance power grid needs and autonomous operation requirements, using sophisticated algorithms to manage peak loads and ensure continuous operation ( 6 ) . Additionally, researchers have integrated models for autonomous charging and battery swapping systems to create dual-layer energy exchange frameworks, effectively supporting the unique needs of AEVs ( 9 ) .

Scope & Practical Applications

• High-efficiency DC-DC converters and isolated resonant topologies (LLC / DAB)
• Sinusoidal and Space Vector PWM inverters for grid integration & industrial motor drives
• MOSFET / IGBT gate driver optimization with active Miller-clamp shoot-through protection
• Power quality conditioning with Total Harmonic Distortion (THD) strictly compliant with IEEE 519
• Complete Simscape .SLX model files, parameter calculation scripts, and IEEE dissertation reports