@article{35977, keywords = {Microgrid, Model predictive control (MPC), Plug-in electric vehicles, Fleet vehicles, Battery degradation, Vehicle-to-grid}, author = {Christoph Gehbauer and Douglas R Black and Peter Grant}, title = {Advanced control strategies to manage electric vehicle drivetrain battery health for Vehicle-to-X applications}, abstract = {
The demand for local power resilience is promoting the installation of renewable-based microgrids. Plug-in electric vehicles (PEVs) are rapidly increasing and can play a critical role in microgrid service provision. The capability of bi-directional charging and discharging will transition PEVs from a simple means of transportation to multi-purpose value generating assets. They can balance the inherently fluctuating generation of renewable energy resources and shape customer’s electricity demand, while still providing the core service of transportation. Value stacking through those additional services allows PEV owners to draw from a number of revenue streams to offset investment costs. However, accelerated battery degradation is often cited as concern when using PEV batteries for purposes other than driving, which presents a major barrier for broad market adoption. This study seeks to assess the economics and trade-offs of bi-directional use of PEVs when utilized as fleet vehicles. In particular, the usage patterns and benefits for PEV fleet deployment at three U.S. military bases is presented. The results show operational cost benefits for bi-directional application of up to 60.8 % annual return on investment without managing battery health, and up to 106.0 % when actively managing battery health through advanced control strategies. The one year payback period far exceeds the expected installation lifespan of at least ten years. Given the results, utilizing PEV fleets for additional services at U.S. military bases can largely offset the additional cost of bi-directional charging hardware and software compared to standard uni-directional equipment.
}, year = {2023}, journal = {Applied Energy}, volume = {345}, pages = {121296}, month = {09/2023}, issn = {03062619}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0306261923006608}, doi = {10.1016/j.apenergy.2023.121296}, language = {eng}, }