Advances in Control Strategies and Battery Management for Stand-Alone Photovoltaic Systems: A Comprehensive Review

2026-02-09T05:48:03+00:00

With rapid changes in the move toward renewable energy, stand-alone PV systems are considered a critical solution for rural electrification, energy independence, and sustainable development. These systems face several challenges, such as variations in solar irradiance, battery aging, transient load variations, and lack of adequate reactive power support. Recent developments have focused on improving the system reliability and efficiency through methods such as advanced control strategies, battery energy storage systems (BESS), and intelligent power management. The review brings together contributions from 2019 to 2025, drawing attention to improvements in MPPT algorithms, hybrid storage integration, predictive and adaptive controllers, and AI-based solutions. Special focus is placed on transient load handling, with methods such as fractional-order controllers, model predictive control, reinforcement learning, and hybrid optimization techniques attaining superior performance in terms of stability and ride-through capability. Battery management is exposed as a fundamental factor in improving state-of-charge estimation, mitigating cycling stress, and integrating supercapacitors for dynamic response enhancement. The consideration of reactive power is also treated in the review, emphasizing inverter design and battery conversion control for voltage stability support. Further development is shown in experimental validation, modeling of long-term degradation, and the question of scalability for near-market deployment. Keywords identify key gaps and outline future directions for highly reliable, efficient, and sage stand-alone PV systems fitting into the global renewable energy agenda.

Model Predictive Control and Deep Reinforcement Learning for Energy Optimization in Battery Electric Vehicles: A Review

2026-02-09T06:49:08+00:00

The rising deployment of Battery Electric Vehicles (BEVs) has accentuated the need for energy-efficient driving strategies to ensure maximum vehicle range and battery longevity. Eco-driving, which intends to optimise energy consumption without compromising vehicle performance, has become an important research focus. This review investigates the use of Model Predictive Control (MPC) and DRL techniques for energy maintenance-orientated eco-driving in BEVs. MPC provides a structured approach to predicting and controlling the dynamic behaviour of the vehicle, considering constraints on battery parameters, regenerative braking, and energy recovery. DRL, conversely, employs a data-driven learning approach to create adaptive driving policies that optimise energy usage in real scenarios. The work investigates alternatives for forecasting maximum energy consumption variations using AI techniques and improvement of vehicle and battery operation. Also, it discusses performance comparison based on predicted results against measured operational data to verify how accurate and feasible these algorithms are. Their latest developments in integrating predictive control with learning-based methods for energy efficiency optimisation are reviewed, and some challenges related to computational burden, real-time implementation, and generalisation across different driving conditions are outlined. In line with this discussion, future research would be orientated toward hybrid methods integrating MPC and DRL for adaptive energy management, as well as the exploitation of promising AI techniques for enhanced prediction and control. This overall study will give good insight into designing intelligent, energy-efficient BEV systems and provide a research direction of eco-driving optimisation.

Journal of Advanced Research in Electrical Engineering and Technology

Advances in Control Strategies and Battery Management for Stand-Alone Photovoltaic Systems: A Comprehensive Review

Model Predictive Control and Deep Reinforcement Learning for Energy Optimization in Battery Electric Vehicles: A Review