Predictive maintenance (PdM) represents a paradigm shift in how maintenance activities are performed in the manufacturing sector. Unlike traditional reactive maintenance, which addresses issues after failures occur, or preventive maintenance, which follows a fixed schedule, predictive maintenance leverages advanced technologies like Artificial Intelligence (AI) and the Internet of Things (IoT) to predict when equipment is likely to fail. The key significance is to use IoT sensors, real-time data such as temperature, vibration, pressure, and usage patterns are collected from machinery. This data is then analyzed by AI algorithms to identify anomalies, trends, and failure precursors, enabling organizations to act before breakdowns happen. This approach minimizes unplanned downtime, reduces repair costs, extends the operational life of assets, and improves overall production efficiency. Predictive maintenance has become increasingly vital in modern manufacturing due to the complexity of industrial equipment and the rising demand for efficiency and reliability. Its application has shown significant benefits across various industries, including automotive, aerospace, and heavy machinery. With the rapid evolution of AI and IoT technologies, predictive maintenance is poised to become an indispensable tool in achieving lean, cost-effective, and resilient manufacturing processes. This research explores the methodologies and future potential of predictive maintenance, particularly within electric vehicle (EV) manufacturing, where precision and uptime are critical.

This paper explores the cost analysis of vehicle recalls between 2010 and August 2024, focusing on Ford, Tesla, and Toyota. The study highlights Tesla's significantly lower recall volumes and costs compared to traditional automakers, despite rapid growth in the electric vehicle market. This research investigates the importance of integrating software-based solutions and vertical manufacturing processes to reduce recall costs. The approach involved analyzing recall data from the National Highway Traffic Safety Administration (NHTSA). The analysis is primarily based on categories such as air bags, electrical systems, and powertrain issues, considering other major concerns for each automaker and their financial implications. The results show that Tesla's reliance on software updates and fewer mechanical components results in fewer recalls, lower logistics costs, and more efficient issue resolution. In contrast, Ford and Toyota face higher recall volumes due to mechanical complexity. The study concludes that Tesla's innovative approach to recalls sets a benchmark for cost-effective recall management, offering traditional automakers a blueprint for reducing recall costs and improving operational efficiency.

The demand for lithium-ion batteries has grown rapidly, driven by the global shift toward renewable energy, electric vehicles (EVs), and energy storage systems. In 2023, the global market for LIBs exceeded $100 billion, with a projected compound annual growth rate (CAGR) of 18% through 2030[1, 2]. Despite these economic benefits, LIB manufacturing poses environmental and economic challenges. One of the most pressing issues is the generation of waste during production, which includes defective electrodes, electrolyte residues, separator materials, and packaging.

Solid-state batteries (SSBs) represent a transformative leap in energy storage, offering significant advantages in safety, energy density, and environmental impact compared to conventional lithium-ion batteries (LIBs). However, the high costs and complex manufacturing processes of SSBs hinder their large-scale adoption. This research evaluates cost-reduction strategies and efficiency enhancements in the production of SSBs, focusing on roll-to-roll (R2R) processing. Key innovations include the integration of infrared (IR) drying systems and automated start/stop control mechanisms, combined with iterative machine calibration for quality control. The results demonstrate energy savings of ~50%, processing speed improvements of 20-50%, and manufacturing cost reductions of ~20%. These findings contribute to the broader adoption of SSB technology, paving the way for a sustainable energy future.

The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) is fundamentally transforming maintenance practices in manufacturing, shifting the paradigm from reactive and preventive approaches to predictive maintenance (PdM) (Deloitte, 2017). PdM utilizes real-time data collected from IoT-enabled devices to predict potential equipment failures before they occur, allowing for timely interventions that minimize unplanned downtime and optimize maintenance schedules (Upkeep, 2023). Research by Deloitte highlights that predictive maintenance can reduce maintenance costs by up to 40%, improve equipment reliability by 30–50%, and decrease equipment downtime by 50% (2017). Additionally, IoT sensors monitor key metrics such as vibration, temperature, pressure, and operational speed, generating valuable datasets for AI algorithms to analyze. These algorithms, often leveraging machine learning, identify failure patterns and forecast future equipment performance, enabling manufacturers to make informed, proactive decisions (Upkeep, 2023).

Conventional lithium-ion batteries utilize liquid electrolyte which holds several safety issues including electrolyte leakage, internal short circuits, and explosion amongst other problems, which limits their use. Solid-state (SSEs) and gel-polymer (GPEs) electrolytes in lithium-ion batteries provide excellent safety efficiency and have been on the rise in the lithium battery industry. Poly (Ethylene Oxide) (PEO) and Poly (vinylidene fluoridehexafluoropropylene) (PVDF-HFP) are commonly used compounds in the electrolyte due to their excellent mechanical and electrochemical properties. This paper reviews the use of PEO and PVDF-HFP, along with other additives, in the electrolyte and assesses the battery performance on the metrics of crystallinity, ionic conductivity, discharge-specific capacity, capacity retention, and coloumbic efficiency. With the addition of boron nitride and polymerized dopamine in PVDF-HFP-based GPEs, the electrochemical metrics show promising results in replacing liquid electrolytes in the near future.