## 2 Related Works
### Gaps in Current Research, Research Opportunities, and Challenges  
Despite its transformative potential, web-based predictive analytics faces challenges related to data security, model interpretability, and integration complexity. Hosting sensitive data on cloud platforms raises concerns about compliance with regulations like GDPR [33]. Additionally, the complexity of machine learning models can make predictions difficult to interpret, especially for non-technical users [34]. Ensuring seamless integration with diverse enterprise systems requires significant expertise and effort [35]. Ongoing research addressing these issues by developing secure cloud architectures, interpretable AI models, and standardized integration frameworks to advance the field further. Despite significant progress in predictive analytics for forecasting the RUL of Li-ion batteries, several challenges remain, highlighting opportunities for further research and innovation. A major limitation is the generalizability of predictive models, as many are trained on controlled laboratory datasets and struggle to account for real-world variability, such as temperature changes, inconsistent charging cycles, and aging effects [36; 37; 38]. Another critical gap lies in the integration of predictive analytics with practical systems like Battery Management Systems (BMS) or Enterprise Resource Planning (ERP) tools, as many existing solutions function as standalone tools, requiring manual interpretation and limiting real-time decision-making [39; 40]. Progress in this field also depends on the availability of comprehensive datasets, which are currently scarce and often limited in scope. Broader, diverse, and publicly accessible datasets encompassing various battery chemistries and operational conditions are urgently needed to advance model robustness and applicability [41; 42]. Moreover, the lack of user-friendly tools makes RUL prediction inaccessible to non-technical users. Simplified dashboards with visual explanations and actionable insights could promote adoption among fleet managers and maintenance teams [43]. Another challenge lies in the interpretability of complex models like deep learning, which are often viewed as \"black boxes\". Developing interpretable AI techniques that provide transparency in predictions is critical for safety-critical applications like aerospace and healthcare [44; 45]. Additionally, data privacy and security concerns arise from using IoT devices and cloud-based platforms, emphasizing the need for robust encryption, secure storage, and regulatory compliance [46; 47]. Upcoming battery chemistries, such as solid-stateand lithium-sulfur batteries, also remain under-researched compared to lithium-ion counterparts, necessitating new models to address their unique degradation behaviors [48, 49]. Scalability is another hurdle, especially in large-scale deployments like innovative grid systems and remote sensing or object detection, where models must process vast sensor data streams and deliver real-time insights without bottlenecks [50, 51]. Furthermore, hybrid modeling approaches that combine data-driven and model-based methods hold significant potential but remain underutilized due to computational complexity and data integration challenges [52]. Lastly, the environmental impacts of battery usage are often overlooked, despite the importance of aligning RUL prediction with sustainability goals to reduce waste and promote recycling.