Digital Twin-Based Intelligent Battery Management System for Predictive Diagnostics and Lifetime Optimization of Electric Vehicle Batteries

Abstract

Conventional electric-vehicle battery management systems primarily estimate present operating states and react to threshold violations. They have limited ability to forecast degradation, evaluate alternative charging and thermal strategies, or distinguish normal aging from incipient faults. This paper proposes a digital twin-based intelligent battery management system (DT-iBMS) that synchronizes an edge-resident battery observer with a cloud-assisted virtual battery. The twin combines electrical, thermal, and aging models with machine-learning residual correction, uncertainty estimation, predictive diagnostics, and constrained lifetime optimization. Its outputs include state of charge, state of health, state of power, remaining useful life, fault probability, and recommended operating limits.

A proof-of-concept capacity twin was evaluated using the NASA Ames lithium-ion battery-aging dataset. Cells B0005, B0006, and B0007 were used for training, while B0018 was kept completely unseen for testing. The adaptive nonlinear twin estimated the held-out cell's discharge capacity with a mean absolute error of 0.0426 Ah, root-mean-square error of 0.0524 Ah, mean absolute percentage error of 2.69%, and coefficient of determination of 0.885. A static cycle-based degradation baseline produced 0.1036 Ah MAE and 6.73% MAPE. Thus, the proposed twin reduced capacity MAE by 58.8% on the held-out cell. A residual-based diagnostic layer and a model-predictive supervisory controller are presented for early fault identification and lifetime-aware charging, cooling, balancing, and power limitation. The results demonstrate feasibility at cell level but do not constitute an automotive lifetime-extension claim; pack-level drive-cycle tests, uncertainty calibration, fault-injection studies, and hardware-in-the-loop validation remain necessary.