Batteries are gaining entry into every home and office for they are widely used because of their variant benefits. However, these batteries are prone to failure caused by charge imbalance in the batteries connected in either series or parallel, which can sometimes be catastrophic and hence they require to be properly monitored in a real-time manner. There exist many battery balancing schemes which are broadly grouped into either passive or active schemes. All these schemes have their own advantages and disadvantages, and hence it is upon the user to decide on which scheme will best work for them. However, research has proven that the hybrid scheme will be the best as it couples the benefits of all schemes. This study will review the various battery cell balancing methodologies and evaluate their relationship with battery performance. At present there are a few studies tackling the mechanical vibration of battery balancing performance. This study shows that battery balancing performance during long-term should be evaluated from various temperature and vibration frequencies.INDEX TERMS Battery cell balancing, electric vehicles, hybrid schemes, and performance optimization.

The battery is the most ideal power source of the twenty-first century, and has a bright future in many applications, such as portable consumer electronics, electric vehicles (EVs), military and aerospace systems, and power storage for renewable energy sources, because of its many advantages that make it the most promising technology. EVs are viewed as one of the novel solutions to land transport systems, as they reduce overdependence on fossil energy. With the current growth of EVs, it calls for innovative ways of supplementing EVs power, as overdependence on electric power may add to expensive loads on the power grid. However lithium-ion batteries (LIBs) for EVs have high capacity, and large serial/parallel numbers, when coupled with problems like safety, durability, uniformity, and cost imposes limitations on the wide application of lithium-ion batteries in EVs. These LIBs face a major challenge of battery life, which research has shown can be extended by cell balancing. The common areas under which these batteries operate with safety and reliability require the effective control and management of battery health systems. A great deal of research is being carried out to see that this technology does not lead to failure in the applications, as its failure may lead to catastrophes or lessen performance. This paper, through an analytical review of the literature, gives a brief introduction to battery management system (BMS), opportunities, and challenges, and provides a future research agenda on battery health management. With issues raised in this review paper, further exploration is essential.

When wind turbine blades are icing, the output power of a wind turbine tends to reduce, thus informing the selection of two basic variables of wind speed and power. Then other features, such as the degree of power deviation from the power curve fitted by normal sample data, are extracted to build the model based on the random forest classifier with the confusion matrix for result assessment. The model indicates that it has high accuracy and good generalization ability verified with the data from the China Industrial Big Data Innovation Competition. This study looks at ice detection on wind turbine blades using supervisory control and data acquisition (SCADA) data and thereafter a model based on the random forest classifier is proposed. Compared with other classification models, the model based on the random forest classifier is more accurate and more efficient in terms of computing capabilities, making it more suitable for the practical application on ice detection.