An Intelligent BMS With Probabilistic MO-GSA Based CDMAS Integrating Edge Controller Analytics

One of the most challenging facets of any Battery Management System is scheduling the charging and discharging cycles of each battery without compromising the uninterruptible power supplies to meet demand. A battery-powered system may include several batteries of varying sorts, models, makes, sizes, and lifespans, etc which employs a diversity of charging techniques. Subsequently, each battery would have its own charging-discharging path. Even when a battery of the same capacity and make is used with the same load profile, the charging curves differ. This is because batteries have differential electrochemical characteristics and deteriorate to some extent with each use. Accordingly, the Battery Management System should schedule the batteries so that degradation and usage are kept to a minimum. This paper developed an adaptive Intelligent Battery Management System that can schedule batteries with minimal power loss, increased battery life, and higher financial benefit, even when batteries of various sizes, capacities, production types, lifespans, charge cycle models etc are incorporated. Multi-zonal approaches are used, combining the benefits of edge analytics and Centralized Data Management and Analytics System. Every millisecond, the battery parameters will be monitored using an energy monitoring circuit integrated with an edge controller. However, the edge controller alone will not be able to process such a vast volume of data on its own, the data will be divided into two categories and analyzed in two phases. All of the big data is delivered to a Centralized Data Management and Analytics System using Low-power wide-area network, and this data is labeled as primary data. A second set of data is extracted from big data within the edge controller via metadata processing before it is transmitted to Centralized Data Management and Analytics System. This secondary data is processed against safety standards stored in read only memory and rapid judgments are performed using edge analytics if necessary. The Centralized Data Management and Analytics System employs a number of analytics techniques. An Auto-Regressive Integrated Moving Average method will be used to forecast the State of Charge of batteries. With the help of this forecasted data, the Multi-Objective Gravitational Search Algorithm is then used to schedule the best battery allocation based on a number of objectives such as battery temperature runaway, unit cost of consumption including span of service, last used time period, State of Charge (%), State of Charge (WH), and so on. Between Auto-Regressive Integrated Moving Average and Gravitational Search Algorithm, a Naive Bayes probabilistic estimator is encased to identify the best general population for Gravitational Search Algorithm, avoiding repeated battery swapping and improving power efficiency. The whole device is evaluated in Hardware in a Loop model. When comparing the performance of the developed model to that of other optimization models, it is evident that Gravitational Search Algorithm outperforms other methods when population is constrained.