Positive and negative convolution cross-connect neural network for predicting the remaining useful life of lithium-ion batteries

• A novel Positive and Negative Convolution Cross-Connect Neural Network (PNCCN) is proposed for accurate remaining useful life prediction of lithium-ion batteries (LIBs). • The PNCCN model achieves high prediction accuracy without requiring internal resistance data, demonstrating its practicality for real-world battery management systems. • Failure case analysis identifies potential overfitting and dataset limitations, leading to future improvements through data augmentation and regularization techniques. • The proposed model enhances generalizability across different degradation scenarios, offering a cost-effective and scalable solution for LIB health monitoring. This study introduces the positive and negative convolution cross-connect neural network (PNCCN), a novel deep learning framework designed for accurately predicting the remaining useful life (RUL) of lithium-ion batteries (LIBs). The model leverages the positive and negative convolution (PNC) and nonlinear cross-connect (NCC) architectures to effectively capture complex nonlinear interactions and degradation patterns in battery data. The PNCCN model was developed and evaluated using a comprehensive dataset comprising 118 battery cells, processed at 10 s intervals during charge-discharge cycles. The training, validation, and test datasets were divided in a 60:20:20 ratio to ensure robust performance evaluation across diverse operational conditions. By excluding internal resistance (IR) data, the model simplifies data acquisition, reduces dependency on costly sensors, and improves the practicality of battery management system (BMS) integration. The PNCCN model achieved an average root mean square errors (RMSEs) of 9.47 and 93.58 cycles for training and test datasets, respectively, with mean absolute percentage errors (MAPEs) of 1.03 % and 8.28 %. Comparative analysis demonstrates that the PNCCN model outperforms existing methods, offering a reliable and scalable solution for LIB RUL prediction. These results highlight the model's potential for real-world applications, emphasizing its effectiveness in reducing system complexity and enhancing predictive accuracy without relying on IR data.