Eee projects on ai based optimal ev route planner with charging stops — Simulation & Power Modeling

Abstract — Eee projects on ai based optimal ev route planner with charging stops

experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. The rapid adoption of electric vehicles (EVs) presents significant challenges regarding the stability of the power grid, involving increasing the peak demand as well as voltage deviation, power quality (PQ) degradation, harmonic distortion, and reactive power mismatch. This paper proposes an integrated artificial intelligence solution to optimize EV charging, utilizing predictive forecasting and adaptive control, to ensure compliance with the power quality standards of grid power. In the artificial intelligence solution, we employ a Temporal Fusion Transformer (TFT) to forecast multi-horizon charging demand, with a Proximal Policy Optimization (PPO)-based deep reinforcement learning agent to coordinate smart charging. The proposed artificial intelligence solution comprises a multi-objective power quality optimizer with Distribution Static Compensator (D-STATCOM) capabilities, allowing for real-time harmonic filtering and reactive power compensation. The proposed artificial intelligence solution was validated using the MATLAB platform, utilizing simulations comprising a 10 MVA distribution feeder with 20 EV chargers, with power ratings ranging from 7 to 150 kW. The simulation results confirmed substantial improvements in the grid’s performance, including a reduction in energy losses by 59.7%, a decrease in instances of load-shedding by 75.4%, and an improved power factor from 0.910 to 0.969. The performance measures used to indicate power quality observed substantial improvements, with the Total Harmonic Distortion (THD) reduced from 6.8% to 4.6%, limited to a AI-based strategy optimally balances charging efficiencies, cost minimization, and power quality improvements. The proposed AI-driven approach successfully balances charging efficiency, cost optimization, and power quality enhancement, providing a scalable solution for large-scale EV integration in smart grid environments.

Analytical & State-Space Control Methodology

International Energy Agency anticipated that EV sales would reach 200 million units worldwide by 2030 1 . The rapid increase of electric vehicles promises significant environmental sustainability benefits, yet simultaneously poses significant challenges for the electric power grid infrastructure. The challenge is rooted in the intersection of electricity grid reliability and quality issues with large-scale electric vehicle adoption, as uncoordinated EV charging creates tremendous PQ issues such as voltage sags, voltage swells, and harmonic distortions that can degrade electricity grid reliability, break down sensitive swells (greater than 1.1 p.u.) can happen at times of rapid charging commencement and termination, while harmonics can arise from the operational switching of the power electronic converters. Total Harmonic Distortion (THD) 2 , of which 5% is the when uncontrolled. During peak charging times and with millions of vehicles charging simultaneously, load demand from EV charging can create aggregated demand beyond normal electricity design parameters.

Simulink Waveforms & Power Quality Findings

Figure 1: Power Converter Circuit Schematic & Electrical Interfacing for Eee Projects On Ai Based Optimal Ev Route Planner With Charging Stops Traditional charging approaches, which tend to be uncontrolled charging and basic time-of-use (ToU) scheduling, have been limited in synergistic efforts to mitigate power quality constraints 3 . In 4 , heuristic EV charging approaches were assessed for a stable grid, and it was pointed out that, while rule-based methods were effective for load leveling, they lacked any real flexibility to account for changing grid conditions. Model predictive control (MPC) techniques were discovered to be useful for balancing load, but they had issues with computational restrictions and scalability for large numbers of EVs 5 . Moreover, traditional methods usually only consider single objectives, either from the point of view of minimizing costs or balancing load, artificial intelligence may provide a disruptive path forward in these areas via intelligent forecasting and adaptive control methods 7 . In particular, deep learning architectures have demonstrated superior performance in both model accuracy and efficiency for power system applications. Long Short-Term Memory (LSTM) networks provide improved load forecasting performance over traditional ARIMA models by 15–20% 8 . However, LSTM-centric approaches have had challenges regarding their limited attention mechanisms and their difficulties dealing with long-term dependencies 9 . In short, the acknowledgement of attention-based models in the form of Temporal Fusion Transformers (TFT) improves the status quo in terms of time series forecasting technique development 10 .

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