Impact of Environmental Factors on Battery Degradation and Control Strategies in EVs
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V6I2P105Keywords:
Electric Vehicles (EVs), Battery Degradation, Environmental Factors, Thermal Management, Battery Management System (BMS), Lithium-Ion Batteries, Adaptive Charging, Predictive MaintenanceAbstract
Electric Vehicles (EVs) are now the best choice to bridge the gap between energy sustainability and environmental pollution. EVs' long-term reliability and sustainability are predominantly dependent on battery functionality, specifically the aging of lithium-ion batteries over time. The present paper presents a comprehensive study on how environmental temperature, humidity, altitude, and air pressure contribute to battery aging. The research also aims to introduce sophisticated control methods to be used to address such undesirable consequences and ensure battery life and performance valid. Temperature fluctuations, for instance, offer electrochemical activity in the battery, which encourages degradation at high temperatures and charge acceptance deficit at low temperatures. Humidity is the water penetration susceptibility, causing corrosion and loss of insulation resistance. Altitude fluctuations affect atmospheric pressure, which can affect cooling system efficiency as well as pressure balance in the battery cells. Degradation mechanisms under such environmental conditions are SEI layer growth, lithium plating, and electrolyte decomposition. They cause capacity loss, increase in internal resistance, and also can be safety hazards. Countermeasures to the above are thermal management system, battery management system (BMS), and adaptive charging algorithm. Thermal management is comprised of passive and active cooling systems, which are designed to maximize operating temperatures. Smart BMS involve sensors and real-time processing to sample operating and ambient conditions and modulate operating conditions to lower the level of battery stress. Adaptive charging regimes, such as temperature-compensated charge profiles and predictive maintenance based on machine learning, also maximize battery life. The work is founded on sets of experimental findings, simulation models, and empirical case studies in trying to contrast the performance of these control mechanisms. The authors have a highlight of a comparative study of battery ageing rates in various conditions through accelerated life testing approaches. The article stresses that EV should be developed with climatic adaptation, particularly for use in various climatic zones. The structure thus laid out is a blueprint for automobile engineers, researchers, and policymakers to further improve the sustainability of EV technology
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