Reducing EV range anxiety: How a simple AI model predicts port availability

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Reducing EV range anxiety: How a simple AI model predicts port availability

Researchers evaluated a model for predicting charging port availability over 30-minute and 60-minute time horizons using data from 100 randomly selected stations. The stations were sampled 48 times daily, every 30 minutes, for a full week.

The model was benchmarked against a “Keep Current State” baseline, which assumes the number of available ports H minutes in the future will be the same as it is now. The source notes that this is a strong baseline, especially over short horizons, because on the US East Coast, never more than 10% of ports change their availability state within a 30-minute block.

The evaluation focused on two metrics for predicting the exact number of free ports: mean squared error (MSE) and mean absolute error (MAE). The source also says a ratio of MSE/MAE ≥ 1 free port measures the accuracy of the binary question: “Will I find at least one free port (Yes/No)?”

For users, the practical issue is whether a charging port will still be free when they arrive. The results framework shows why that is difficult to predict: when most ports do not change state in a short period, even a simple “no change” assumption can be hard to beat.

Source: research.google.

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