This paper presents an application of dynamically driven recurrent networks (DDRNs) in online electric vehicle (EV) battery analysis. In this paper, a nonlinear autoregressive with exogenous inputs (NARX) architecture of the DDRN is designed for both state of charge (SOC) and state of health (SOH) estimation. Unlike other techniques, this estimation strategy is subject to the global feedback theorem (GFT) which increases both computational intelligence and robustness while maintaining reasonable simplicity. The proposed technique requires no model or knowledge of battery's internal parameters, but rather uses the battery's voltage, charge/discharge currents, and ambient temperature variations to accurately estimate battery's SOC and SOH simultaneously. The presented method is evaluated experimentally using two different batteries namely lithium iron phosphate ( $\text{LiFePO}_4$) and lithium titanate ( $\text{LTO}$) both subject to dynamic charge and discharge current profiles and change in ambient temperature. Results highlight the robustness of this method to battery's nonlinear dynamic nature, hysteresis, aging, dynamic current profile, and parametric uncertainties. The simplicity and robustness of this method make it suitable and effective for EVs’ battery management system (BMS).

The prediction of a Lithium-ion battery's lifetime is very important for ensuring safety and reliability. In addition, it is utilized as an early warning system to prevent the battery's failure. Recent advance in Machine Learning (ML) is an enabler for new data-driven estimation approaches. In this paper, we suggest a hybrid method, named the CNN-LSTM-DNN, which is a combination of Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), and Deep Neural Networks (DNN), for the estimation of the battery's remaining useful life (RUL) and improving prediction accuracy with acceptable execution time. A comparison against various ML estimation algorithms is carried out to show the superiority of the proposed hybrid estimation approach. For that, three statistical indicators, i.e., the MAE, R ² , and RMSE, are selected to assess numerically the prediction results. Experimental validation is performed using two datasets of different lithium-ion batteries from NASA and CALCE. Thus, results reveal that hybrid methods perform better than the single ones, also the effectiveness of the suggested method in reducing the prediction error and in achieving better RUL prediction performance compared to the other methods.

Lithium-ion batteries experience degradation with each cycle, and while aging-related deterioration cannot be entirely prevented, understanding its underlying mechanisms is crucial to slowing it down. The aging processes in these batteries are complex and influenced by factors such as battery chemistry, electrochemical reactions, and operational conditions. Key stressors including depth of discharge, charge/discharge rates, cycle count, and temperature fluctuations or extreme temperature conditions play a significant role in accelerating degradation, making them central to aging analysis. Battery aging directly impacts power, energy density, and reliability, presenting a substantial challenge to extending battery lifespan across diverse applications. This paper provides a comprehensive review of methods for modeling and analyzing battery aging, focusing on essential indicators for assessing the health status of lithium-ion batteries. It examines the principles of battery lifespan modeling, which are vital for applications such as portable electronics, electric vehicles, and grid energy storage systems. This work aims to advance battery technology and promote sustainable resource use by understanding the variables influencing battery durability. Synthesizing a wide array of studies on battery aging, the review identifies gaps in current methodologies and highlights innovative approaches for accurate remaining useful life (RUL) estimation. It introduces emerging strategies that leverage advanced algorithms to improve predictive model precision, ultimately driving enhancements in battery performance and supporting their integration into various systems, from electric vehicles to renewable energy infrastructures.

The prognosis and health management of lithium-ion batteries are extremely important issues for operating performance as well as the cost of energy storage systems in vehicular applications. This is achieved through the estimation of the state-of-health and the prediction of remaining useful life (RUL). This paper presents a lithium battery prognosis model considering the battery-aging conditions. The proposed model is developed based on the Rao-Blackwellization particle filter, which is able to estimate the posterior values of the aging indicators, i.e., capacity and resistance, and to predict the RUL. The particularity of the proposed model is that it considers the aging conditions of batteries as inputs of the prognosis model. In order to validate the proposed method, experiments have been carried out under different aging conditions for three types of lithium-ion batteries. The proposed model performances have been evaluated. A comparison against the particle filter prognosis model is presented. Results highlight the effectiveness of the proposed technique to predict the RUL for different cases: initial conditions, types of lithium-ion batteries, and aging conditions. The RUL prediction using the proposed prognosis model presents a maximum relative error of 6.64%, which is low compared to 14.3% when a simple particle filter prognosis model is used.

The rapid growth of electric vehicles (EVs) and new energy systems has put lithium-ion batteries at the center of the clean energy change. Nevertheless, to achieve the best battery performance, safety, and sustainability in many changing circumstances, major innovations are needed in Battery Management Systems (BMS). This review paper explores how artificial intelligence (AI) and digital twin (DT) technologies can be integrated to enable the intelligent BMS of the future. It investigates how powerful data approaches such as deep learning, ensembles, and models that rely on physics improve the accuracy of predicting state of charge (SOC), state of health (SOH), and remaining useful life (RUL). Additionally, the paper reviews progress in AI features for cooling, fast charging, fault detection, and intelligible AI models. Working together, cloud and edge computing technology with DTs means better diagnostics, predictive support, and improved management for any use of EVs, stored energy, and recycling. The review underlines recent successes in AI-driven material research, renewable battery production, and plans for used systems, along with new problems in cybersecurity, combining data and mass rollout. We spotlight important research themes, existing problems, and future drawbacks following careful analysis of different up-to-date approaches and systems. Uniting physical modeling with AI-based analytics on cloud-edge-DT platforms supports the development of tough, intelligent, and ecologically responsible batteries that line up with future mobility and wider use of renewable energy.