Estimating Remaining Useful Life and State of Health of EV Lithium Ion Batteries Using Sequential CNNs

Lithium-ion batteries power electric cars, portable electronics, and renewable energy systems. To ensure these batteries' optimal performance, avoid unexpected failures, and reduce maintenance costs, their usable life must be accurately predicted. This study examines data-driven, physics-based, and hybrid methods for calculating lithium-ion battery life. The proposed work also offers a unique way to estimate lithium-ion battery usable life using machine learning. This method uses current, voltage and temperature to build a prediction model that accurately predicts the battery's remaining usable life. An archive of lithium-ion battery cycles is used to evaluate the proposed strategy and compare it to other cutting-edge methods. The results support the proposed lithium-ion battery lifespan strategy. The precise estimation of battery SOH ensures uninterrupted operation and prevents unexpected battery failures, improving electric vehicle reliability and safety. It also optimizes battery use, extending battery life and lowering replacement costs. The proposed method could improve electric vehicle (EV) performance and efficacy using machine learning and electrochemical modeling, promoting sustainable transportation. Portable electric machines, electric locomotives, and renewable energy storage systems use lithium-ion batteries. For optimal performance, cost reduction, and preventing unexpected failures, accurate SOH estimation is essential. This paper examines data-driven, physics-based, and hybrid SOH estimation methods. This paper proposes a machine learning-based SOH prediction method that uses battery performance parameters like voltage, current, and temperature. The proposed work shows that this method outperforms state-of-the-art methods on a battery cycle dataset. EV reliability, safety, and battery utilization improve with accurate SOH estimation, lowering replacement costs. The proposed method can improve EV performance and promote sustainable transportation by using machine learning and electrochemical modeling.