State-of-charge (SOC) is a crucial battery quantity that needs constant monitoring to ensure cell longevity and safe operation. However, SOC is not an observable quantity and cannot be practically measured outside of laboratory environments. Hence, machine learning (ML) has been employed to map correlated observable signals such as voltage, current and temperature to SOC values. In recent studies, deep learning (DL) has been a prominent ML approach outperforming many existing methods for SOC estimation. However, yielding optimal performance from DL models relies heavily on appropriate selection of hyperparameters. At present, researchers relied on established heuristics to select hyperparameters through manual tuning or exhaustive search methods such as grid search (GS) and random search (RS). This results in lengthy development time in addition to less accurate and inefficient models. This study proposes a systematic and automated approach to hyperparameter selection with a Bayesian o
The microgrid connected with the battery energy storage system is a promising solution to address carbon emission problems and achieve the global decarbonization goal by 2050. Proper integration of the battery energy storage system in the microgrid is essential to optimize the overall efficiency as well as manage the power efficiently and securely. However, battery energy storage system integrated microgrid exhibits several concerns, including intermittencies, poor power quality, high capital cost, and energy imbalance between supply and demand. To address these shortcomings, an improved scheduling controller and optimization of the battery energy storage system are required to ensure the resilient, sustainable, and economic operation of the microgrid. Several approaches have been employed to improve the performance of microgrids; however, the review studies on controllers and optimizations based scheduling operations in microgrid have not been explored yet. In light of this research g
Abstract-This paper presents an improved machine learning approach for the accurate and robust state of charge (SOC) in electric vehicle (EV) batteries using differential search optimized random forest regression (RFR) algorithm. The precise SOC estimation confirms the safety and reliability of EV. Nevertheless, SOC is influenced by numerous factors which cannot be measured directly. RFR is suitable for real-time SOC estimation due to its robustness to noise, overfitting issues and capacity to work with huge datasets. However, proper selection of RFR architecture and hyper-parameters combination remains a key issue to be explored. Hence, a differential search algorithm (DSA) is employed to search for the optimal values of trees and leaves in the RFR algorithm. DSA optimized RFR eliminates the utilization of the filter in data pre-processing steps and does not require a detailed understanding and knowledge about battery chemistry, rather only needs sensors to monitor battery voltage and
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