# Analysis of Instantaneous Energy Consumption and Recuperation Based on Measurements from SORT Runs
## 1 Introduction
Additionally, the literature emphasises the importance of precise measurements of instantaneous energy consumption by electric buses, which depends on driving dynamics, energy management strategy and external factors such as route profile and vehicle load. This is extremely important for optimising drive systems and designing effective operating strategies [32; 33]. Data from SORTs enable the development of detailed energy profiles of the vehicle in various driving scenarios. They allow for the identification of the most energy-intensive cycle stages and the optimisation of energy management strategies. In addition to the SORTs discussed above, other tests are conducted worldwide. One is the Altoona (USA) bus testing program supported by the Federal Transit Administration (FTA). Such tests include checking structural integrity, fuel or energy consumption, exhaust emissions, brake performance and noise level [34; 35; 36]. Using a more theoretical approach, one can also use the vehicle energy consumption calculation tool (VECTO) simulator [37; 38], developed by the European Commission to study CO\({}_{2}\) emissions and fuel consumption of vehicles [39]. In addition to standardised and simulation methods, more unconventional and experimental solutions are used. Examples of this include [23; 40; 41; 42], in which the authors studied the energy consumption of electric buses during real trips around Athens. In this way, extensive data from several days of research were obtained. Another example of extensive energy consumption tests is the study reported in [24], which lasted 14 months and was conducted in various weather conditions, including days with extremely low and high temperatures, and which took into account several other factors, as well as the use of systems implemented in electric buses, during the operation of vehicles in real conditions in Jilin Province. Based on the examples presented above, one can see many options regarding the methodology implemented in testing electric buses. However, it is worth noting that experimental and long-term methods can lead to errors or distortions in the obtained results, resulting from external factors that were not included in the testing process, or which were not adequately considered. Assuming this, the authors stated that the best test method would be the E-SORT, which was developed by UITP and which allows one to obtain reliable energy consumption results of electric buses by implementing standardised procedures, significantly reducing the probability of obtaining unreliable results. This research dives into the search for smarter ways to make electric buses more energy efficient. It focuses on how much energy these buses use and recover in real time, using three modelling methods: linear regression, KNN, and neural networks. The results show that KNN and neural networks do a better job of capturing the twists and turns in the data. This suggests that adding more details, like extra variables and time delays, could make these models even better. The study also looked at how well buses recover energy when braking in three typical driving situations--heavy city traffic, mixed routes, and suburban roads. This helped reveal how different driving conditions impact energy recovery. By using real-time data, the research ensures that the results are reliable and can work with different bus models and in various settings. Road gradients, passenger loads, and weather fluctuations are just a few examples of the real-world operating factors that introduce significant unpredictability and make it difficult to estimate electric bus energy efficiency. According to [PERSON] et al. [11] and [PERSON] et al. [23], these factors significantly affect the efficiency of regenerative braking and energy consumption, often departing from controlled test conditions like the SORT approach. Another important problem is that existing approaches do not investigate the underexplored area of long-term data on battery degradation and its impact on energy performance [22; 33].