School of Accounting, Xijing University, Xi'an, China.
PLoS One. 2024 Jun 7;19(6):e0303933. doi: 10.1371/journal.pone.0303933. eCollection 2024.
Lithium batteries, as an important energy storage device, are widely used in the fields of renewable vehicles and renewable energy. The related lithium battery recycling industry has also ushered in a golden period of development. However, the high cost of lithium battery recycling makes it difficult to accurately evaluate its recycling value, which seriously restricts the development of the industry. To address the above issues, machine learning will be applied in the field of economic benefit analysis for lithium battery recycling, and backpropagation neural networks will be combined with stepwise regression. On the basis of considering social and commercial values, a lithium battery recycling and utilization economic benefit analysis model based on stepwise regression backpropagation neural network was designed. The experimental results show that the mean square error of the model converges between 10-6 and 10-7, and the convergence speed is improved by 33%. In addition, in practical experiments, the model predicted the actual economic benefits of recycling a batch of lithium batteries. The results show that the predictions are basically in line with the true values. Therefore, the economic benefit analysis and prediction model for lithium battery recycling proposed in the study has the advantages of high accuracy and fast operation speed, providing new ideas and tools for promoting innovation in the field of economic benefit analysis. It has certain application potential in the evaluation of the benefits of lithium battery recycling.
锂电池作为一种重要的储能装置，广泛应用于新能源汽车和可再生能源领域。相关的锂电池回收产业也迎来了发展的黄金时期。然而，锂电池回收成本高，难以准确评估其回收价值，严重制约了产业的发展。针对上述问题，将机器学习应用于锂电池回收的经济效益分析领域，结合反向传播神经网络和逐步回归，在考虑社会和商业价值的基础上，设计了一种基于逐步回归反向传播神经网络的锂电池回收利用经济效益分析模型。实验结果表明，模型的均方误差收敛在 10-6 到 10-7 之间，收敛速度提高了 33%。此外，在实际实验中，该模型预测了回收一批锂电池的实际经济效益。结果表明，预测结果基本符合真实值。因此，本研究提出的锂电池回收经济效益分析和预测模型具有精度高、运算速度快的优点，为推动经济效益分析领域的创新提供了新的思路和工具。在锂电池回收效益评估方面具有一定的应用潜力。
