Thermal Management Optimization in EV Battery Pack Assembly: A Data-Driven Approach Using AI-Based Feedback Loops
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I2P109Keywords:
Electric Vehicles (EVs), Battery Thermal Management, Artificial Intelligence, Feedback Loops, Machine Learning, Deep Learning, Energy EfficiencyAbstract
The fast development of Electric Vehicles (EVs) has posed one of the burning challenges: proper thermal management of battery pack assemblies. Thermal variation in EV batteries may result in poor performance, early battery failure and safety hazards. This paper provides an in-depth data-driven proposal for maximizing thermal management of EV battery pack assembly through Artificial Intelligence (AI)-powered feedback loops. The system can dynamically respond to variations in its operations through real-time data acquisition, predictive models, and intelligent control algorithms, thereby improving performance and safety. The paper begins by summarizing the thermal issues that are faced in modern EV battery solutions, discussing heat sources, including electrochemical reactions, exposure to the environment, and charge/discharge state. In the research, the hybrid AI framework (a mixture of Machine Learning (ML) and Deep Learning (DL) is used to create intelligent feedback loops that suggest thermal anomalies and automatically control cooling processes. Experiments were performed through simulators and reality with high-fidelity thermal sensors, cloud-based telemetry, and machine learning devices such as Long Short-Term Memory (LSTM) neural networks, Random Forest regressors, and Reinforcement Learning (RL) agents. When measured against conventional passive and rule-based solutions, the proposed system can show up to a 37 percent increase in thermal consistency and a 22 percent decrease in energy consumption in cooling subsystems. Some of the main contributions are a modular AI feedback design, an adaptive control method to thermally control battery thermal systems and a very rich dataset gathered on different environmental conditions and with diverse operations of the battery. The results provide a firm recommendation for the use of AI-driven dynamic systems as a game changer in the EV thermal management area, with an eye on further improvements in terms of EV reliability and sustainability
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