## 1 Introduction  
Current industrial processes are crucial in pursuing high efficiency, reliability, and adaptability [1, 2]. Harnessing the power of data and optimization algorithms is imperative, as they are the key to improving the system's control, which is needed to stay competitive in the evolving industrial landscape. Artificial intelligence (AI) algorithms and data-driven optimization models for intelligent manufacturing processes have been used to improve production efficiency, reduce costs, and improve the quality of manufacturing services [3, 4]. The use of optimization algorithms to obtain profitable, productive, controllable, safe, and sustainable processes is presented in [5, 6]. In particular, Ref. [5] reviews multi-objective algorithms for energy production, supply chains, and chemical industries. Meanwhile, in Ref. [6], genetic algorithms (GAs) and artificial neural networks (ANNs) are used to define the objective function of the optimization algorithms applied in the metallurgical industry. In addition, Ref. [7] highlights algorithms for data-based industrial process prediction, namely time series and factor-based predictions. That work tests different prediction techniques in multiple steel plant processes. Energy transition has forced industries to focus on critical processes such as managing electrical energy storage systems (ESSs). Multiple works have focused on ESS characterization, including the description of their components, control strategies, applications, and technologies for high-power systems [8, 9, 10], as well as their use in multi-source power systems [11], their techno-economic assessment when integrated with renewable energy [12], and the implementation of battery management systems (BMSs) [13]. Thesesystems are fundamental in electrical vehicles (EVs) and microgrids (MGs) based on renewable energy. For example, for EVs under heavy-duty conditions, Ref. [14] evaluates different energy storage system topologies, and Ref. [15] presents a comparison study of semi-active ESS topologies for EVs. Ref. [16] provides a critical review of devices with high energy density and high power density used in electrical transportation; meanwhile, Ref. [17] reports a review of standards and challenges of ESSs for EVs. Moreover, Ref. [18] implements several control schemes tested on EVs with a real driving cycle. Similarly, Ref. [19] presents a systematic review of ESS control techniques for grid-connected and standalone systems based on artificial intelligence. In contrast, Ref. [20] discusses the importance of ESSs in MGs with renewable energy sources. Among energy storage systems, hybrid ESSs (HESSs) are relevant since the joint operation of complementary energy sources, i.e., sources with high energy density and sources with high power density, guarantees improved power quality, increased energy efficiency, and better dynamic response. Ref. [10] presents examples of HESS installations that improve grid stability and power quality with frequency regulation. A detailed review of HESS benefits is reported in Ref. [20], where the contributions of HESS to MGs are highlighted. For example, HESSs contribute to power quality, offering the MG the ability to provide clean and stable power with a constant power flow, as well as a wave-pure and noise-free sinusoidal within the acceptable frequency and voltage limits. Similarly, Ref. [21] evaluates the capabilities of HESSs to mitigate fluctuations and uncertainty of renewable energy. ESSs with high power density, like supercapacitors or flywheels [21; 22], can support loads that demand fast power peaks, while ESSs with high energy density, like batteries [21; 23], can provide sustained power for a long time (hours or days). Different topologies exist for implementing HESSs depending on the converters action; those systems are denominated as passive, semi-active, or fully active. In a passive HESS, any source is managed using a converter, while in semi-active HESS, converters manage some of the sources.