The optimization of electric vehicle (EV) batteries is a critical component in advancing sustainable transportation solutions, with significant implications for performance, cost-efficiency, and longevity. This chapter explores the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing battery management systems (BMS), focusing on key areas such as battery degradation prediction, real-time health monitoring, energy management, and fast-charging protocols. By leveraging AI-driven models, significant advancements have been made in improving the accuracy of State of Charge (SOC) and State of Health (SOH) estimations, thus enabling proactive maintenance and performance optimization. Reinforcement learning techniques have shown particular promise in adapting charging strategies based on real-time battery conditions, ensuring both rapid charging and battery longevity. Additionally, the integration of machine learning with sensor data has facilitated more precise predictive modeling, allowing for better management of battery cycles and degradation factors influenced by environmental conditions. As the electric vehicle industry faces growing demand for faster and more efficient battery technologies, AI and ML offer robust solutions to address these challenges, enabling smarter, more sustainable EV systems. This chapter delves into the latest trends, challenges, and future directions in AI-powered battery optimization, providing a comprehensive overview of how these technologies can drive the next generation of electric mobility.
The rapid advancement of electric vehicles (EVs) is transforming the global automotive industry, offering promising alternatives to traditional internal combustion engine vehicles [1]. Central to the success of electric mobility is the performance and longevity of EV batteries, which directly influence the vehicle’s efficiency, driving range, and overall operational costs [2]. As the adoption of electric vehicles continues to grow, there is a pressing need to address the challenges associated with battery optimization, particularly with regard to improving efficiency, managing degradation, and ensuring sustainability [3]. The integration of Artificial Intelligence (AI) and Machine Learning (ML) into battery management systems (BMS) represents a cutting-edge solution that can significantly enhance these critical aspects of battery performance [4]. By leveraging AI and ML, it is possible to optimize battery operation in real-time, providing more accurate predictions of battery health and improving the overall driving experience for consumers [5].
Battery performance is influenced by several factors, including temperature, charge/discharge cycles, and driving conditions [6]. Traditionally, battery management systems have relied on predefined algorithms to estimate parameters such as the State of Charge (SOC) and State of Health (SOH) [7]. These models, while effective to a degree, often fail to adapt to the dynamic nature of real-world driving conditions [8]. The integration of AI and ML enables BMS to move beyond static models, allowing for continuous learning and adaptation based on real-time data from the vehicle’s sensors [9]. Machine learning algorithms, for example, can process large datasets derived from the battery’s performance over time, recognizing patterns and trends that would otherwise go unnoticed. This results in more accurate predictions and optimized battery usage, helping to extend battery life and enhance overall efficiency [10].
AI-powered predictive modeling plays a crucial role in understanding and mitigating battery degradation, a key challenge in EV technology [11]. Over time, batteries naturally degrade due to factors like temperature fluctuations, frequent charge/discharge cycles, and high power demands [12]. Without accurate predictive models, manufacturers are limited in their ability to forecast when and how battery degradation will occur, potentially leading to suboptimal vehicle performance and costly replacements [13]. AI-driven models can predict degradation rates with much greater precision by incorporating a variety of factors, including environmental conditions and usage patterns [14]. These models can also recommend adjustments to the charging and discharging processes, reducing stress on the battery and prolonging its effective lifespan. By forecasting potential failures before they occur, AI systems can enable proactive maintenance, thereby reducing unexpected downtime and improving the reliability of electric vehicles [15].
