# A Unified Machine Learning Framework for Li-Ion Battery State Estimation and Prediction +
###### Abstract  
The accurate estimation and prediction of internal states in lithium-ion (Li-Ion) batteries, such as State of Charge (SoC) and Remaining Useful Life (RUL), are vital for optimizing battery performance, safety, and longevity in electric vehicles and other applications. This paper presents a unified, modular, and extensible machine learning (ML) framework designed to address the heterogeneity and complexity of battery state prediction tasks. The proposed framework supports flexible configurations across multiple dimensions, including feature engineering, model selection, and training/testing strategies. It integrates standardized data processing pipelines with a diverse set of ML models, such as a long short-term memory neural network (LSTM), a convolutional neural network (CNN), a feedforward neural network (FFNN), automated machine learning (AutoML), and classical regressors, while accommodating heterogeneous datasets. The framework's applicability is demonstrated through five distinct use cases involving SoC estimation and RUL prediction using real-world and benchmark datasets. Experimental results highlight the framework's adaptability, methodological transparency, and robust predictive performance across various battery chemistries, usage profiles, and degradation conditions. This work contributes to a standardized approach that facilitates the reproducibility, comparability, and practical deployment of ML-based battery analytics. Article  
17 July 202518 July 20251  
the State of Charge (SoC), State of Health (SoH), and State of Power (SoP). It would also accommodate different preprocessing workflows and handle heterogeneous datasets, thus promoting reproducibility, scalability, and interoperability in predictive battery analytics. Although several efforts in the literature attempt to address portions of this challenge, they are generally focused on specific aspects of battery management. For instance, the framework introduced in [2] is centered on battery reliability, emphasizing failure probability estimation and the development of early warning and fault detection strategies. In another example, Ref. [3] proposes a battery digital twin--a virtual representation of the physical battery--integrated with machine learning techniques to predict the SoC, SoH, and SoC. While both efforts contribute valuable insights, they address only selected facets of the broader state estimation problem. In response to this gap, this paper proposes a unified framework that integrates three essential components aimed at performing battery state prediction. Battery state refers to a set of quantifiable indicators that describe the internal condition and performance characteristics of a battery at a given time. First, it introduces a battery data model that can flexibly accommodate multiple data types and sources. Second, it defines standardized data processing pipelines that ensure the consistent transformation and preparation of data for analysis. Third, it incorporates a diverse set of predictive modeling techniques, which can be selectively applied to different battery states based on data characteristics and application needs. This framework is designed for methodological flexibility and sustainability, enabling users to tailor combinations of models and processing steps to the unique demands of each predictive task. Overall, this paper presents a unified framework that emphasizes flexibility, adaptability, and sustainability, enabling users to tailor both modeling techniques and data workflows to the unique requirements of each prediction task. Its novelty relies on its capability of handling different datasets from different data sources in a structured way, different processes, and several ML models with configurable parameters aimed at predicting different battery states. All of these dimensions can be handled and monitored through this framework, providing a flexible tool to the analyst for predictive processes involving different battery states.