# Analysis of Instantaneous Energy Consumption and Recuperation Based on Measurements from SORT Runs
## 2 Materials and Methods
### Problem Formulation
Making sure the data gathered represents real-world performance, accurate voltage measurement is crucial for evaluating instantaneous energy consumption and recovery. Specific places for measuring voltage are marked with yellow indicators and circled in red, which showcases the voltage measurement setup. The electric circuit's voltage levels can be captured at these carefully placed places. The electric bus's performance may be precisely monitored under various operating scenarios, including acceleration, braking, steady driving, and stopping, with this system. Electric current primarily flows along the blue lines in the diagram, which represent the main pathways of high voltage. In order to guarantee that voltage measurements encompass the essential portions of the bus's electrical system, these wires link various circuit components. Subsystems like energy control units and battery management are able to coordinate voltage measurements thanks to the orange lines that represent signal and control pathways. With this dual-path system, data gathering is simple, and voltage behaviour can be accurately monitored under all kinds of driving conditions. In order to regulate the measuring process and guarantee data capture under certain conditions, the circuit incorporates components such as relay switches labelled \"K712\", \"K713\", \"K714\", and \"K715\". The \"Charge POU\" labelled voltage measurement sites are also linked to the bus's charging system. Thanks to this connection, voltage may be monitored both when the bus is running and when it is charging. A thorough comprehension of the energy behaviour of the bus, including the battery's handling of charging and discharging processes, can be gained by capturing voltage data during charging. The evaluation of battery performance, identification of inefficiencies, and optimisation of energy management strategies all rely on these data. In order to evaluate the bus's total energy efficiency, voltage measurements are crucial during the SORT, which mimics real-world operating circumstances. First, SORT 1, which simulates heavy urban traffic with frequent stops; second, SORT 2, which simulates mixed driving conditions with moderate stops; and finally, SORT 3, which simulates suburban routes with few pauses. By recording voltage data under various scenarios, drivers can assess the bus's energy consumption and braking efficiency. This validates the promises made by manufacturers about battery performance and energy usage and assists in determining the most energy-efficient operational solutions. Evaluating the recovery process also benefits from precise voltage monitoring. When the bus brakes, the voltage data help determine how much energy is being regenerated and stored back into the battery. Improving the recuperation system's efficiency is vital for lowering overall energy usage, which is a necessity. Additionally, voltage data allow fleet operators to verify battery health and efficiency, ensuring that the bus maintains reliable performance over time. Adaptive energy management systems, which modify energy use in response to actual driving circumstances, can be better built with the help of these data. When current and voltage readings are added together, a clear picture is painted of the bus's power usage. Reduced running expenses and increased energy efficiency are the results of optimising the bus's energy management plan. In the context of SORT testing, such data ensure a standardised comparison of different electric bus models, eliminating variables related to local traffic and infrastructure. Ten measurements were taken in the K1 direction and eight measurements in the K2 direction. Energy consumption was determined from each direction in accordance with the methodology specified in the SORT standard. For the purposes of mathematical analysis, the following were used: linear model, K-nearest neighbour regression (KNN), neural networks, assessment of model fit and prediction. The tools used are described below.