## 4 Related Work
### Regression Model-Based Energy Consumption  
Regression models have been widely applied to the energy consumption and performance characteristics of BEVs to improve prediction accuracy and optimize performance. Studies have shown that linear regression models are effective in estimating energy consumption by taking into account variables such as vehicle speed, acceleration and environmental factors [30]. [PERSON] et al. developed a regression model to estimate the energy consumption of battery electric vehicles (BEV) [31]. Their study aimed to develop a methodology to assess the impact of traffic control elements on BEVs on a route. In accordance with this, the focus of their paper was to develop a predictive model of the expected energy consumption and indicate the most relevant explanatory variables. The theoretical approach applied to real routes allowed them to verify the applicability of the proposed method, which can serve as a first step for further studies, but also enable identification of the most significant factors that influence BEV consumption in urban environments. Based on the results, the following conclusions were drawn by the authors:  
* A predictive linear regression model can be applied to estimate vehicle consumption as a function of traffic control elements. * The probability of stopping due to traffic control elements significantly affects the predictive model. Thus, the randomness of the traffic control elements has a significant impact on the energy consumption of the BEV. The model does not define a roadmap for sustainable mobility but is a first step towards this goal. Based on the investigation of the reviewed models, we find that the former simulation-based approach was mainly designed to provide a solution for estimating fuel consumption in an urban environment. It may reasonably be anticipated that a simulation model containing a more detailed set of parameters and information about the vehicle in question would yield more precise results. [PERSON] et al. analyzed the impact of measuring actual energy consumption. Using data collected from 50 BEVs operating in Shanghai, the travel patterns of a total of 11,052 trips were first summarized [32]. The 11,052 trips were characterized by short distances and average travel times, with an average distance of 17.2 km and an average travel time of 40 min. After analyzing the battery performance, three routes were selected to build the energy consumption model. The distance traveled, speed, temperature and the initial battery State of Charge (SoC) affect the energy consumption to varying degrees [33; 34; 35]. The change in travel distance has a significant effect on the change in energy consumption. Generally, as the distance increases, the rate of change in energy consumption decreases. This is also the case for temperature. The effect of speed minimizes energy consumption at 40 km/h and the variance first decreases and then increases. This publication has been selected as a benchmark due to its utilization of a large sample size within a comparable context, and the parameters identified within the model align with the most prevalent characteristics associated with electric passenger car consumption calculations. More sophisticated models, including multiple linear regression and polynomial regression, have been used to capture the non-linear relationships between these variables and energy consumption [36]. In addition, machine learning-based regression models, such as support vector regression and neural networks, have shown excellent performance in predicting BEV energy consumption by exploiting interactions between large datasets and complex features [37]. These models are key to improving range estimation, optimizing powertrain efficiency and reducing range anxiety for BEV users. In the last examined publication, [PERSON] et al. considered all potential factors influencing the energy consumption of EVs at different stages [38]. Based on the literature, a number of factors were proposed and grouped into six categories.