This research presents an advanced control approach for battery management in battery electric utility vehicles (BEUV) operating in indoor logistics environments. The proposed approach utilizes a combination of proportional-integral (PI), fuzzy PI, and interval type 2 fuzzy PI (IT2fuzzyPI) control structures to augment the state space model for battery management. The state space model incorporates the voltage and current of each battery cell as state variables and considers the current demand from the electric motor as an input. By integrating fuzzy logic with PI control and considering uncertainty, the IT2fuzzyPI structure offers improved control recital and system robustness in the occurrence of nonlinearities, uncertainties, and turbulences. The outcomes of the simulation validate the effectiveness of the proposed scheme in managing the battery pack system’s state of charge and controlling the rates of charging and discharging. The IT2fuzzyPI control significantly improves the overall proficiency and longevity of the battery system, making it suitable for battery electric utility vehicles in logistics environments. This research contributes to the field of battery management systems, providing a valuable tool for designing and evaluating high-performance electric vehicles with enhanced control capabilities.

Due to their lower productivity, lower reliability, and lower economic stability, older power plants are leading to higher carbon emissions. Rather than simply focusing on the retirement and recuperation of power plants, this study focuses on generation expansion planning (GEP). Considering recuperation is economically and environmentally beneficial to power the power generating company. These criteria have made the GEP problem more complex. Hence, the applications of optimization algorithms are required to solve these complex, constrained, and large-scale problems. In this study, an effective hybrid spotted hyena-particle swarm optimization (HSHPSO) algorithm is proposed to handle the GEP problem for the Tamil Nadu power system. This case study addresses the GEP problem for a 7-year planning horizon (2020–2027), as well as a 14-year planning horizon (2020–2034). A significant reduction in total cost and pollution occurs by including retirement and recuperation in GEP. To prove the effectiveness of the proposed HSHPSO technique, it is compared with the existing technologies such as particle swarm optimization (PSO) and differential evolution (DE). Compared to GEP with no recuperation or retirement, the total cost and CO2 emissions of the GEP have been reduced by 11.07% and 9.48%, respectively. Also, the results demonstrate that the HSHPSO algorithm outperformed other algorithms.