Optimizing Battery Recycling Processes in Electric Vehicle Life Cycle Assessment using a Hybrid Model of LSTM and Deep Reinforcement Learning

• Main Authors: Nimma, Divya, Prakash, N., Khan, Shamim Ahmad, Manchanda, Mahesh, Poongavanam, N., Infant Raj, I
• Format: Conference Proceeding
• Language: English
• Subjects: Adaptation models; Batteries; Battery Recycling; Deep reinforcement learning; Electric Vehicles; Life cycle assessment; Long Short-Term Memory; Optimization; Predictive models; Recycling; Sustainable development; Vehicle dynamics
• ISSN: 2768-0673
• DOI: 10.1109/I-SMAC61858.2024.10714684

Battery recycling processes in the Life Cycle Assessment (LCA) of Electric Vehicles (EVs) are crucial for reducing environmental impact and enhancing resource efficiency. This paper presents a novel approach to optimizing these processes by developing a hybrid model that combines Long Short-Term Memory (LSTM) networks with Deep Reinforcement Learning (DRL). The proposed model is designed to address the complexities of predicting temporal dynamics in recycling operations and making optimal decisions to enhance overall process efficiency. The methodology begins with data collection from reputable sources. The data is preprocessed through cleaning, normalization, and feature selection to ensure its suitability for predictive modeling. The LSTM model is employed to capture the temporal dependencies within the recycling process, predicting key parameters such as material recovery rates and energy efficiency. These predictions are then fed into the DRL model, which uses a policy network to make decisions that optimize the recycling process in real-time. The integration of LSTM and DRL allows the model to adapt dynamically, improving process outcomes with each iteration. The novelty of this research is in the combination of LSTM for temporal prediction and DRL for dynamic optimization, which need to be accomplished in improving battery recycling efficiency and sustainability. The proposed hybrid model is implemented in Python, and its performance is rigorously evaluated against traditional methods. The results demonstrate that the hybrid LSTM-DRL model achieves significantly higher prediction accuracy and optimization efficiency. These findings underscore the model's potential to drive more sustainable and cost-effective recycling processes in EV battery management, offering a robust solution to the challenges of LCA in the automotive industry tasks.