*3.2. Vehicle Analysis*

To help better understand the performance of the models, the results for each individual vehicle were analysed. Prediction errors for each of the 48 vehicles were determined by calculating the proportion of the test dataset for which the predicted availability was incorrect for that vehicle. Given that the results from the two averaging approaches were not significantly different, the results were only reported for one of the two models (CMAh) for purposes of clarity.

Figure 6 reveals a high degree of correlation between the two models and a clear outlier with a much higher error rate than other vehicles. The analysis of the datasets revealed that was due to a substantially different pattern of behaviour in the 8 test weeks to the 34 training weeks. The vehicle was available for 50.1% of the training period in contrast to only 12.7% of the test period. A similar, but smaller, disparity between training data and test data was also apparent for the two next worse performers. However, this was not always the case for vehicles with a relatively high prediction error. There was a close correlation between the proportion of available periods in the training and test data for the vehicle with the 4th highest error rate despite a prediction error in excess of 20%. In this case, the error was more strongly influenced by the specific times the vehicle was available rather than the total time it was available.

**Figure 6.** Prediction errors (Perr) for each of the 48 vehicles for the AutoML and CMAh models.

At the other extreme, the figure showed 11 vehicles with error rates of less than 2%. However, the analysis revealed that this excellent performance was enabled by the fact they were almost always unavailable. As a result, both models predicted that these vehicles were never available, and the error rate was due to the small number of periods where this wasn't the case. Such vehicles would not be appropriate for V2G as they must be both relatively predictable and available for substantial amounts of time. A simple metric was thus developed to calculate the viability of a vehicle, given these variables, as shown in Equation (5), where *Perr* is the prediction error, and *Pav* the percentage of time the vehicle was available, both expressed as a number between 0 and 1.

$$V2G\upsilon = (1 - Perr) \ast Pa\_{\upsilon} \tag{5}$$

Thus, a stationary vehicle that was entirely predictable and always available would score 1. A vehicle that was either entirely unpredictable and/or never available would score 0, and potentially viable vehicles would score somewhere in between. Figure 7 shows this metric calculated for all vehicles using the test dataset and prediction errors from the AutoML model.

**Figure 7.** V2G (vehicle-to-grid) viability scores, V2Gv, for the 48 fleet vehicles using the AutoML model on the test dataset.

The figure suggested which vehicles would be candidates for V2G. For example, 30 of the 48 vehicles had a V2Gv score in excess of 0.6 as a result of a combination of relatively low prediction errors and relatively high availability. The same set of 30 vehicles was produced using all three models and consisted of vehicles from every department. Of particular interest for a V2G service, however, is the ability to deliver grid services when most required, i.e., at time of peak demand. To determine whether this was the case, the analysis was repeated, considering only periods within a typical peak demand period of 16:00 to 19:00. Figure 8 shows that 30 vehicles again achieved a V2Gv score over 0.6, with only 1 vehicle differing from the original set. However, 15 vehicles now scored over 0.85, making them excellent candidates for participation in V2G during peak hours.

**Figure 8.** V2Gv scores for the peak 4 pm to 7 pm period using the AutoML model on the test dataset.

Such a score is not in itself sufficient to demonstrate the viability of a vehicle for V2G however. Another key consideration is the ability of the vehicle to deliver the required power or energy when called upon, i.e., it must have sufficient charge to satisfy journey requirements while delivering energy for the V2G service. To assess this requirement, vehicle trip journey over the 34 weeks of training data

was analysed to determine the mean daily mileage for each vehicle on a workday and non-workday. This gave an indication of how much battery capacity would be required to satisfy typical journey requirements and hence how much would be available for V2G. The mean workday daily mileage for vehicles with a peak period V2Gv score over 0.6 was found to be only 26 km (s = 21.8 km), and they were rarely used on other days. It would, therefore, be possible to satisfy these journey requirements while enabling V2G with relatively modest battery capacity. In addition, the vehicles were available on average 96.9% (s = 3.9%) of the time, during the hours of 7 pm and 7 am, thus providing the opportunity for them to start the working day fully charged.
