Lithium-ion (Li-ion) batteries have become essential in modern industries and domestic applications due to their high energy density and efficiency. However, they experience gradual degradation over time, which presents significant challenges in maintaining optimal battery performance and increases the risk of unexpected system failures. To ensure the reliability and longevity of Li-ion batteries in applications, various methods have been proposed for battery health monitoring and remaining useful life (RUL) prediction. This paper provides a comprehensive review and analysis of the primary approaches employed for battery health monitoring and RUL estimation under the categories of model-based, data-driven, and hybrid methods. Generally speaking, model-based methods use physical or electrochemical models to simulate battery behaviour, which offers valuable insights into the principles that govern battery degradation. Data-driven techniques leverage historical data, AI, and machine learning algorithms to identify degradation trends and predict RUL, which can provide flexible and adaptive solutions. Hybrid approaches integrate multiple methods to enhance predictive accuracy by combining the physical insights of model-based methods with the statistical and analytical strengths of data-driven techniques. This paper thoroughly evaluates these methodologies, focusing on recent advancements along with their respective strengths and limitations. By consolidating current findings and highlighting potential pathways for advancement, this review paper serves as a foundational resource for researchers and practitioners working to advance battery health monitoring and RUL prediction methods across both academic and industrial fields.

The increasing demand for reliable and safe Lithium-ion (Li-ion) batteries requires more accurate estimation of state of health (SOH) and remaining useful life (RUL) prediction. However, the inherent complexity and non-linear dynamics of Li-ion batteries present specific challenges to traditional methods of SOH modeling. Although particle filter (PF) techniques can handle nonlinear dynamics, they still face challenges, including particle degeneracy and loss of diversity, that reduce their ability to effectively model the nonlinear degradation mechanisms of batteries. To tackle these limitations, this paper presents a novel artificial intelligence-driven PF (AI-PF) technology for battery health modeling and prognosis. The main contributions of the AI-PF technique are as follows: (1) A novel dynamic sample degeneracy detection method is proposed to provide real-time assessment of particle weights so as to promptly identify degeneracy and improve computational efficiency. (2) An adaptive crossover and mutation strategy is proposed to reallocate low-weight particles and maintain particle diversity to improve modeling and RUL forecasting accuracy. The effectiveness of the AI-PF framework is validated through systematic evaluations carried out using benchmark models and well-recognized battery datasets.

The evolving fuzzy technique is a recent development in the soft-computing field that has shown some promising results in applications such as control, classification, and short-term prediction. However, evolving fuzzy techniques still have challenges in terms of high-speed processing of cluster/rule generation, especially in long-term prediction applications due to the broader distribution of the input space. These factors can lead to problems such as overfitting in optimization and high computational costs, which could limit their applications in real-time monitoring. In this article, an adaptive evolving fuzzy (AEF) technique consisting of two novel aspects is developed to tackle these problems. First, an error-assessment method is suggested to monitor the trend of the cumulative training errors and to control the fuzzy cluster evolving process. Second, an adaptive particle filter algorithm is proposed to optimize the fuzzy clusters in order to enhance incremental learning and improve modeling efficiency. The effectiveness of the proposed AEF predictor is verified by simulation tests; it is also implemented for battery remaining useful life forecasting. Test results have shown that the proposed AEF technique can effectively capture the system's dynamic characteristics with fewer rules and can provide more flexibility in fuzzy modeling.

Accurate measurement and estimation of state variables in nonlinear dynamic systems are critical in various engineering and scientific applications. While particle filter (PF) techniques have become increasingly popular for modeling such dynamic systems, they are prone to sample degeneracy and impoverishment, which can considerably affect performance. Existing PF methods often come with drawbacks like high computing costs and noise sensitivity. This paper presents a novel probabilistic posterior optimization particle filter (PPO-PF) technique, inspired by the firefly algorithm, to address these PF limitations and enhance PF efficiency. In the proposed PPO-PF technique, an adaptive search method is proposed to locate the high-likelihood region within the posterior space for improved convergence. A new step size selection method is suggested to facilitate navigation within the search space. In addition, a particle position optimization approach is proposed to guide low-weight particles towards high-probability regions, optimizing the posterior probability density function and mitigating sample degeneracy. The proposed PPO-PF technique is validated by simulation under various model conditions, including predicting the remaining useful life of Lithium-ion (Li-ion) batteries. The results indicate that the proposed PPO-PF reliably captures system dynamics with improved accuracy, even under high noise conditions. These findings highlight its potential to enhance measurement science by offering a more reliable approach for state estimation in complex systems.

Accurate prediction of system degradation and remaining useful life (RUL) is essential for reliable health monitoring of Lithium-ion (Li-ion) batteries, as well as other dynamic systems. While evolving systems can offer adequate adaptability to the nonstationary and nonlinear behavior of battery degradation, existing methods often face challenges such as uncontrolled rule growth, limited adaptability, and reduced accuracy under noisy conditions. To address these limitations, this paper presents a smart evolving fuzzy predictor with customized firefly optimization (SEFP-FO) to provide a better solution for battery RUL prediction. The proposed SEFP-FO technique introduces two main contributions: (1) An activation- and distance-aware penalization strategy is proposed to govern rule evolution by evaluating the structural relevance of incoming data. This mechanism can control rule growth while maintaining model convergence. (2) A customized firefly algorithm is suggested to optimize the antecedent parameters of newly generated fuzzy rules, thereby enhancing prediction accuracy and improving the predictor’s adaptive capability to time-varying system conditions. The effectiveness of the proposed SEFP-FO technique is first validated by simulation using nonlinear benchmark datasets, which is then applied to Li-ion battery RUL predictions.