Managing Charging Induced Grid Stress and Battery Degradation in Electric Taxi Fleets

TL;DR

This paper presents a simulation framework to optimize electric taxi fleet charging policies, balancing battery health, grid stability, and profitability using real-world data and reinforcement learning.

Contribution

It introduces a novel EV fleet simulator that evaluates the effects of charging strategies on battery degradation, grid stress, and profitability over time.

Findings

Reinforcement learning policy reduces grid stress compared to baseline.

Optimized policy prolongs fleet service life.

Simulation demonstrates trade-offs between profitability and battery health.

Abstract

Operating fleets of electric vehicles (EVs) introduces several challenges, some of which are borne by the fleet operator, and some of which are borne by the power grid. To maximize short-term profit a fleet operator could always charge EVs at the maximum rate to ensure vehicles are ready to service ride demand. However, due to the stochastic nature of electricity demand, charging EVs at their maximum rate may potentially increase the grid stress and lead to overall instability. Furthermore, high-rate charging of EVs can accelerate battery degradation, thereby reducing the service lifespan of the fleet. This study aims to reconcile the conflicting incentives of fleet longevity, short-term profitability, and grid stability by simulating a taxi fleet throughout its lifespan in relation to its charging policies and service conditions. We develop an EV fleet simulator to evaluate the battery degradation due to unpredictable charging and ride demand. Consequently, the impact on the power grid through the charging infrastructure is assessed due to these activities. This simulation utilizes publicly accessible real-world travel data from the NYC taxi dataset. We compare a baseline 80-20 fleet charging policy with a reinforcement learning-based policy designed to prolong the fleet's service life and alleviate grid stress. We monitor grid stress, battery degradation, and profitability over five years and find that our learned policy outperforms the baseline. This simulator enables fleet operators to assess the impact of different charging policies on these indicators to make informed decisions in the future.