## 1 Introduction  
The increasing demand for sustainable transportation options has fostered the expansion of electric vehicles (EVs). Furthermore, in many urban areas, low-emission zones are delimited to make cities more sustainable. Fully or hybrid EVs will have free access to them, which will push the adoption of EVs for delivery [1, 2] or for rent or car sharing [3, 4]. Therefore, for optimizing delivery routes, we have to consider the distance or time needed, as well as the energy consumption and recovery. Furthermore, with the adoption of automatic driving, the energy cost will be even more relevant in the operational costs of transport and delivery systems. In addition, this is an important step toward reducing carbon emissions and moving toward more sustainable transportation systems. Key factors for optimizing EV routing are the city topography and traffic conditions [5]. To improve the accuracy of energy estimation, it is crucial to use high-quality digital elevation model (DEM) data as inaccurate DEM data can lead to significant errors in energy estimation [6, 7]. Apart from basic electric features inherent to each vehicle, to estimate EV energy consumption, we must consider factors such as road gradient, rolling friction coefficient, air drag, and kinetic energy [7]. Therefore, low speeds will result in a lower energy consumption than higher speeds. When speed is low, as in traffic jams, we may incorporate the representation of losses generated by the drive train and the supply of auxiliary systems by providing a more accurate representation of the energy consumption [8]. In the logistics domain, strategies for solving the TSP have been approached through diverse methods. Solutions in the field of air logistics involve the utilization of a mixed descending optimization technique, which combined 2-Opt, Or-Opt, and Single-Node Insertion to address the TSP [9]. As a different choice for resolving the TSP, the nodes could be partitioned into clusters, addressing the problem by visiting only one node within each cluster [10]. We can find several examples of EV routing optimization, including the Electric Vehicle Traveling Salesman Problem (EV-TSP) and the electric vehicle routing problem (E-VRP) based on the Traveling Salesman Problem (TSP) [11; 12]. The time windows for EV-TSP are where batteries are fully/partially recharged at each stop [13]. Alternatively, the TSP can be considered for full or hybrid electric vehicles [14; 15] or even with stochastic battery depletion [16]. As time execution is a concern [17; 18], preprocessing traffic and energy consumption information [19] is needed to solve the E-VRP. Nevertheless, there is a lack of open datasets that can be used to easily analyze these problems with elevation data integrated into them. This would facilitate researchers to concentrate in the algorithmic part and not in the data preparation. In this work, we present an efficient solution for the Steiner Traveling Salesman Problem for EVs (EV-STSP) that considers traffic information and city topography data, incorporating good-quality elevation data. On average, the algorithm took 113.93 s to solve instances of size 80 for the STSP and 22.14 s to solve instances of size 40 within the city of Madrid. Our proposed solution combines (1) an artificial neural network (ANN) that estimates the electric consumption when traveling between two nodes in the city graph under different traffic states, which allows transforming the problem from an STSP to a normal TSP; (2) the 2-OPT algorithm with a Nearest Neighbor (NN) initialization to solve the TSP; and (3) the Bellman-Ford algorithm [20; 21; 22] to reconstruct the entire route, since the energy recovery of EVs leads to negative weights on the city graph, and the shortest-path algorithms like Dijkstra do not support them [23]. Our methodology is evaluated on real-world instances of the city of Madrid. We show its effectiveness in reducing electric consumption compared to traditional routing algorithms based solely on distance.