Central to this study is the Attention-based ReCurrent Algorithm for Neural Analysis with LSTM (ARCANA) model. This is an attention-based Seq-to-Seq architecture specifically engineered to assess early stage battery degradation and perform lifecycle monitoring. The model demonstrates superior multitasking capabilities, supported by its high modularity and dynamic adaptability. It is designed to utilize a flexible range of past battery cycle data, known as historical temporal segments, for input. In addition, the model includes predetermined parameters for future conditions, such as discharge rates and cycle numbers. These parameters are known in advance of the experiment, i.e. they are controlled by the measurement device and are referred to as encoded temporal segments. This dual capability offers multifaceted advantages, from cost and time savings to improved material selection and protocol optimization.