Predictive Optimal Control-Based Battery Management Systems for Grid-Integrated Electrified Systems: A Multi-Objective Framework for Efficiency, Stability, and Lifecycle Optimization (Published)
A predictive optimal control-based Battery Management System (BMS) framework is designed with the focus on enhancing energy efficiency, grid stability, battery longevity and operational reliability of the grid-integrated electrified systems operating under dynamic conditions. However, as renewable energy resources and electrified transportation systems penetrate into the electric grid at unprecedented rates, new challenges related to intermittency of resource power generation & load fluctuation (large changes in demand) are introduced. Traditional BMS methods typically use fixed-rule or single-objective control strategies, which cannot effectively trade off the conflicting needs of performance, safety and lifecycle optimisation. In response to this limitation, the willing framework incorporates multi-objective optimization techniques with Model Predictive Control (MPC) in adaptable and on-demand energy management of grid-connected battery energy storage. The designed controller forecasts future battery states and grid properties within a limited prediction horizon, while jointly optimizing charging/discharging decisions against hard operational constraints pertaining to state-of-charge (SOC), temperature, voltage, and current. We propose a multi-objective cost function that captures the production of power losses, battery degradation, voltage deviations and energy imbalance, while maximizing grid support capability and overall system efficient. The system analysis is conducted using a detailed MATLAB/Simulink model that incorporates the lithium-ion battery dynamics, renewable energy intermittency, stochastic load disturbances and grid disturbances including voltage sag and power-frequency variations.The simulation results shown in this paper indicate that the proposed predictive optimal control framework outperforms traditional PI-based and rule-based BMS approaches. This method decreases around 18.7% of the total battery charging/discharging losses, reduces SOC estimation error from 4.9% to 1.2%, and raises energy conversion efficiency from 88.4% to 96.1%. It has been shown that the grid voltage is reduced during transient disturbance and frequency stabilization time are improved about 42% and compared to conventional control strategies by more than 35%. The intelligent optimization framework also realizes a reduction of battery temperature rise by around 21%, hence improving thermal stability and operation safety. Battery lifecycle analysis suggests the proposed strategy prevents most degradation-related capacity loss, with a 16–19% reduction under long-term cycling conditions from gentler charging profiles and lower high-current stress. Moreover, it improves the efficiency of renewable energy utilization by around 14%, facilitating a more effective incorporation of intermittent solar and wind resources to the grid. The predictive controller shows good robustness against parameter uncertainties and stochastic load disturbances with less than 2% steady-state voltage error keeping stable operation. In sum, the obtained result indicates that the proposed predictive optimal control based BMS is a promising and scalable solution for future grid-integrated electrified systems. The framework provides very good gains in terms of efficiency and stability along with battery health management making it well-suited for smart grids, renewable energy systems, electric vehicle charging infrastructure and sustainable energy.
Keywords: Battery Management Systems (BMS), Model Predictive Control (MPC), energy storage systems, grid integration, lifecycle optimization, multi-objective optimization