AI-Driven Predictive Energy Optimization for Software-Defined Electric Vehicles

Abhishek Devgan

doi:10.5281/zenodo.20348035

AI-Driven Predictive Energy Optimization for Software-Defined Electric Vehicles

Author(s)	Abhishek Devgan
Country	India
Abstract	The recent surge in electric vehicles (EVs) and the introduction of software-defined vehicle (SDV) architectures have offered an unprecedented opportunity to real-time, predictive, energy optimization of all subsystems of the vehicle, using artificial intelligence (AI) and machine learning (ML) techniques. The research framework presented is based on the AI-based predictive energy optimization in software-defined electric vehicles (SDEVs) where the power can be allocated dynamically between the powertrain and battery thermal management system (BTMS), heating ventilation and air conditioning (HVAC), regenerative braking, infotainment, and auxiliary electronics by the ML-based models. The methodology that is proposed combines deep reinforcement learning (DRL), long short-term memory (LSTM) networks, transformer-based models, and model predictive control (MPC) to have a synergistic, closed-loop energy management approach. The system predicts demand spikes based on real-world driving cycle information, connectivity-enabled traffic predictions, and state-of-health (SOH) indicators and reallocates energy reserves to maximize driving range, battery life and comfort to the passengers. The results of the experimental investigations based on several benchmark investigations prove the efficiency improvement of 5-22 % as compared to traditional rule-based controllers, and a considerable decrease in peak battery stress and thermal degradation. The paper also generalizes 20 practical deployment case studies and 20 of modern applications, which illustrate the translational preparedness of the suggested solution. The research has concluded by listing the issues that are still to be solved including the computational latency, edge-hardware constraints, data privacy, and cross-platform transferability and has laid an easy research roadmap of the next-generation AI-enabled SDEVs.
Keywords	Software-Defined Electric Vehicles (SDEV); AI-Driven Energy Management; Deep Reinforcement Learning; LSTM-Transformer; Battery Thermal Management; Model Predictive Control; Dynamic Energy Allocation; Predictive Energy Optimization; Control of Vehicle Subsystems; ML in EVs.
Field	Engineering
Published In	Volume 5, Issue 12, December 2024
Published On	2024-12-06
DOI	https://doi.org/10.5281/zenodo.20348035
Short DOI	https://doi.org/hb48m2

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AI-Driven Predictive Energy Optimization for Software-Defined Electric Vehicles

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