arXiv (All)
open access
Thermal management optimization of Battery Electric Vehicles via hierarchical NMPC with CMO-trained Neural Models
This paper addresses the problem of energy-efficient thermal management in Battery Electric Vehicles (BEVs), with the goal of extending driving range while ensuring passenger comfort and battery thermal safety. A hierarchical Nonlinear Model Predictive Control (NMPC) framework is proposed, in which
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Adapting Neural Robot Dynamics on the Fly for Predictive Control#973116
- Model-Free Control for Residential Heating: Deployment and Simulation of Nonlinear Data-Enabled Predictive Control#1035900
- A Mobile Agent-Based Hierarchical Reinforcement Learning Framework for Energy-Balanced Data Collection and Wireless Charging in WSNs#695488
- Cloud-Based AI Framework for EV Charging Forecasting and Infrastructure Optimization#1081866
- LLM-Enhanced Multi-Agent Reinforcement Learning for Unified Electric Vehicles-Charging Station-Grid Optimization in Public Charging Systems#1083303
Record · ID 1131612
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive