Maximum power point tracking (MPPT) is an indispensable component of the Photovoltaic (PV) systems to maximize efficiency. Perturb and observe (P&O) is one of the prevalent MPPT methods owing to its easier structure for implementation but it suffers from problems of slow tracking and oscillations around the MPP. In this paper, a modified variable step size MPPT algorithm based on P&O method is proposed to obtain maximum power output from the PV system coupled with a boost DC-DC converter. The proposed method employs the scaled power difference as a control variable to enable variation of step size for each cycle. Results obtained by simulation and experimental work verified that the proposed algorithm is potent mitigating the problems related with classical P&O method.

Energy storage systems (ESS) are indispensable in daily life and have two types that can offer high energy and high power density. Hybrid energy storage systems (HESS) are obtained by combining two or more energy storage units to benefit both types. Energy management systems (EMS) are essential in ensuring the reliability, high performance, and efficiency of HESS. One of the most critical parameters for EMS is the battery state of health (SoH). Continuous monitoring of the SoH provides essential information regarding the system status, detects unusual performance degradations and enables planned maintenance, prevents system failures, helps keep efficiency at a consistently high level, and helps ensure energy security by reducing downtime. The SoH parameter depends on parameters such as depth of discharge (DoD), charge and discharge rate (C-rate), and temperature. Optimal values of these parameters directly affect the lifetime and operating performance of the battery. The proposed adaptive energy management system (AEMS) uses the SoH parameter of the battery as the control input. It provides optimal control by dynamically updating the C-rate and DoD parameters. In addition, the supercapacitor integrated into the system with filter-based power separation prevents deep discharge of the batteries. Under the proposed AEMS control, HESS has been observed to generate 6.31% more energy than a system relying solely on batteries. This beneficial relationship between supercapacitors and batteries efficiently managed by AEMS opens new possibilities for advanced energy management in applications ranging from electric vehicles to renewable energy storage systems.

Lithium-based battery systems (LBS) are used in various applications, from the smallest electronic devices to power generation plants. LBS energy storage technology, which can offer high power and high energy density simultaneously, can respond to continuous energy needs and meet sudden power demands. The lifetime of LBSs, which are seen as a high-cost storage technology, depends on many parameters such as usage habits, temperature and charge rate. Since LBSs store energy electrochemically, they are seriously affected by temperature. High-temperature environments increase the thermal stress exerted on LBS and cause its chemical structure to deteriorate much faster. In addition, the fast charging feature of LBSs, which is generally presented as an advantage, increases the internal temperature of the cell and negatively affects the battery life. The proposed energy management approach ensures that the ambient temperature affects the charging speed of the battery and that the charging speed is adaptively updated continuously. So, the two parameters that harm battery health absorb each other, and the battery has a longer life. A new differential approach has been created for the proposed energy management system. The total amount of energy that can be withdrawn from LBS is increased by 14.18% as compared to the LBS controlled with the standard energy management system using the genetic algorithm optimized parameters. Thus the LBS replacement period is extended, providing both cost benefits and environmentally friendly management by LBSs turning into chemical waste distinctly later.

This paper proposes a delay-affected two-degree-of-freedom proportional-integral-derivative (DE-2DOFPID) controller for accurate speed regulation of brushless direct-current (BLDC) motors operating under time-delay conditions. In the proposed architecture, the delay is explicitly embedded into the control law through a weighted delayed-error feedback term. At the same time, two reference-weighting coefficients reshape the proportional and derivative paths. This structure extends the classical 2DOFPID controller by enriching the instantaneous error response with memory-based information and tunable reference coupling, which strengthens both transient behavior and robustness in delay-dominated regimes. The controller gains are tuned using the physics-inspired Rime-inspired metaheuristic (RIME) algorithm, which is well-suited to the highly multimodal, nonconvex search landscape induced by the composite performance index. The objective function combines multiple criteria, including integral absolute error, integral time-weighted absolute error, integral squared error, steady-state error, rise time, settling time and overshoot. For a BLDC speed-control benchmark, comparative optimization studies are carried out against recent metaheuristics, including the educational competition optimizer, Fata morgana algorithm, Slime mould algorithm, Moss growth optimizer, and Ruppell’s Fox optimizer, as well as RIME-based tuning of PI-FF, PID, PID with anti-windup, and conventional 2DOFPID controllers. The proposed RIME-tuned DE-2DOFPID reduces the settling time by 8.03% and the lower cost by 6.99% relative to PID-based methods, while maintaining overshoot below 3%. Real-time experiments on a laboratory BLDC drive confirm that the delay-affected 2DOFPID architecture preserves its advantages under measurement noise, parameter uncertainty and external disturbances, demonstrating its practical relevance for robust motion-control applications.