### Related Work  
Techniques based on machine learning have proven to be effective approaches for modelling and controlling ICEs, due to their robustness, which allows compensating variations in the electrical load and whose approach guarantees stability [3]. Nevertheless, artificial-intelligence techniques require significant amounts of data to achieve a good model; in addition, they are merely black boxes, in the sense of interpreting the system's parameters and its mathematical structure. This contrasts with fuzzy models and controllers, such as Mamdani linguistic systems, where an expert in the physical system dictates the basis of operation rules, which are not optimal, but rather based on human experience. On the other hand, the fuzzy modelling and control technique proposed by [PERSON] (T-S) can be complemented with optimal parameter identification algorithms, as well as with different control techniques. In the Mamdani fuzzy model, both the antecedent and the consequent are fuzzy propositions, which are chosen by experts on the system to be modelled or by an expert in fuzzy modelling, while in the T-S fuzzy modelling, the antecedent is a fuzzy proposition and the consequent is a mathematical function, which can have a polynomial state-space architecture [13]. This represents a strength for the modelling of complex systems and control based on submodels represented in state space. For instance, the authors in [14] presented a fuzzy logic-based control and power management strategy for unmanned aerial vehicles, in which they developed a set of fuzzy rules for controlling and effectively dividing the power between the two power sources: electric generator and ICE. Similarly, the authors in [15] used a fuzzy logic controller to improve the performance of the traditional perturb and observe algorithm for power generation using a photovoltaic system. The proposed fuzzy logic controller with a rule base collection of 25 rules outperformed the controller using the traditional perturb and observe algorithm due to its adaptive step size, enabling the fuzzy logic controller to adapt the photovoltaic system faster to changing environment conditions. However, the selection of the fuzzy parameter \(m\) of the fuzzy c-means algorithm is a crucial aspect in the implementation of this type of techniques. The authors in [16] found that the value of \(m\) must be greater than \(n/(n-2)\) (where \(n\) is the total number of data objects in the data set) viewed from the perspective of the algorithm convergence. More recently, the authors in [17] proposed, based in their experience, a range of \(1.1\leq m\leq 5\), whereas [18] proposed a new guideline for \(m\) selection based on robust fuzzy c-means analysis. They suggested to define \(m\) in the range [1.5-4]. The authors in [19] proposed a method for the selection of the optimal \(m\) value using four cluster validity indices, obtaining an optimal \(m\) value in the interval [2.5-3]. The authors in [20] analyzed the use of soft computing techniques based on the c-means fuzzy classification algorithm and the T-S type fuzzy rule structure in research developed in the field of ICE for the generation of electric power. In [21], the authors proposed a novel semi-supervised fuzzy c-means clustering algorithm using multiple fuzzification coefficients instead of an auxiliary matrix to adjust the membership grade of the data elements, aiming to make the proposed fuzzy c-means algorithm a semi-supervised machine-learning method. The authors demonstrated the convergence of the algorithm and validated the efficiency of the method through a numerical example and derived a supervised approach to determine the parameter \(m\) of the c-means algorithm. In our previous work [22], we used the fuzzy T-S technique to estimate the remaining battery energy in an autonomous vehicle.