Adaptive primal–dual Q-learning for electric vehicle route optimization on real-world charging networks
Sarvesh Ram Kumar, Rayappa David Amar Raj, Archana Pallakonda, K Sreelakshmi, K Krishna Prakasha · 2026
Read Adaptive primal–dual Q-learning for electric vehicle route optimization on real-world charging networks by Sarvesh Ram Kumar, Rayappa David Amar Raj, Archana Pallakonda, K Sreelakshmi, K Krishna Prakasha (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/PMC13392393.txt.
Description
Sarvesh Ram Kumar and colleagues present, in 2026, a learning-based route planner for electric vehicles on real charging networks. Range limits, sparse chargers, and variable energy use make electric trip planning harder than shortest-path routing, and classic algorithms ignore charger availability and connector fit.
Their framework blends reinforcement learning with graph methods, centred on a dual model that weighs gain against cost as it learns, tuning its own weights by a primal-dual scheme. Transport software engineers gain an optimiser designed around charging reality, aiming to cut both travel time and stranding risk as charging networks evolve.
Catalog record
| CREATOR | Sarvesh Ram Kumar, Rayappa David Amar Raj, Archana Pallakonda, K Sreelakshmi, K Krishna Prakasha |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 64760 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |