Energy storage has become a fundamental component in renewable energy systems, especially those including batteries. However, in charging and discharging processes, some of the parameters are not controlled by the battery’s user. That uncontrolled working leads to aging of the batteries and a reduction of their life cycle. Therefore, it causes an early replacement. Development of control methods seeks battery protection and a longer life expectancy, thus the constant-current–constant-voltage method is mostly used. However, several studies show that charging time can be reduced by using fuzzy logic control or model predictive control. Another benefit is temperature control. This paper reviews the existing control methods used to control charging and discharging processes, focusing on their impacts on battery life. Classical and modern methods are studied together in order to find the best approach to real systems.

The development of a robust and efficient electric vehicle (EV) charging infrastructure is essential for accelerating the transition to sustainable transportation. This systematic review analyzes recent research on EV charging network planning, with a particular focus on optimization techniques, machine learning applications, and sustainability integration. Using bibliometric methods and Principal Component Analysis (PCA), we identify key thematic clusters, including smart grid integration, strategic station placement, renewable energy integration, and public policy impacts. This study reveals a growing trend toward hybrid models that combine artificial intelligence and optimization methods to address challenges such as grid constraints, range anxiety, and economic feasibility. We provide a taxonomy of computational approaches—ranging from classical optimization to deep reinforcement learning—and synthesize practical insights for researchers, policymakers, and urban planners. The findings highlight the critical role of coordinated strategies and data-driven tools in designing scalable and resilient EV charging infrastructures, and point to future research directions involving intelligent, adaptive, and sustainable charging solutions.

As the cost of solar photovoltaic (PV) falls, their potential for transforming modern electricity generation increases. Solar PV provides a simpler way of producing clean and affordable energy, which makes it an attractive investment. Great investments in solar PV have occurred in industrialized countries, but government efforts to promote this technology have not been effective in nonindustrialized countries. Despite this, some of these countries may have a high solar PV potential, such as Colombia, where policies to encourage solar PV are only just starting to take place. Therefore, this paper proposes a simulation model to assess different policies—feed-in tariff, net metering, and capital subsidy—to promote solar PV investments in the Colombian residential sector. Policies are assessed considering the criteria of efficiency and effectiveness. Simulation results suggest that (i) net metering is the most efficient policy with a cost indicator of 20,298 USD/MW; (ii) feed-in tariff is the most effective policy as it reaches the highest level of avoided CO₂ emissions—4,792,823 million tons of CO₂—and a meaningful PV installed capacity of 7522 MW; (iii) capital subsidy is the least efficient policy as it has the highest cost indicator of 509,616 USD/MW.