Model Predictive Control-Based Optimal Energy Management of Autonomous Electric Vehicles Under Cold Temperatures

TL;DR

This paper presents a real-time Model Predictive Control approach to optimize energy management in autonomous electric vehicles, balancing propulsion, HVAC, and preconditioning to maximize range under cold conditions.

Contribution

It introduces a novel MPC-based method for dynamic energy allocation in AEVs, improving range and thermal management during cold weather operation.

Findings

Enhanced energy efficiency in cold conditions

Improved thermal management and battery health

Extended driving range under low-temperature scenarios

Abstract

In autonomous electric vehicles (AEVs), battery energy must be judiciously allocated to satisfy primary propulsion demands and secondary auxiliary demands, particularly the Heating, Ventilation, and Air Conditioning (HVAC) system. This becomes especially critical when the battery is in a low state of charge under cold ambient conditions, and cabin heating and battery preconditioning (prior to actual charging) can consume a significant percentage of available energy, directly impacting the driving range. In such cases, one usually prioritizes propulsion or applies heuristic rules for thermal management, often resulting in suboptimal energy utilization. There is a pressing need for a principled approach that can dynamically allocate battery power in a way that balances thermal comfort, battery health and preconditioning, along with range preservation. This paper attempts to address this issue using real-time Model Predictive Control to optimize the power consumption between the propulsion, HVAC, and battery temperature preparation so that it can be charged immediately once the destination is reached.