Energy Optimization in EV Battery Thermal Management using Model Predictive Control

Abstract

As electric vehicles become more common, the efficient thermal management of their batteries has emerged as an essential aspect that must be tackled if they are to be both safe and durable. A battery works best within a rather narrow temperature range; if it deviates from this the pack's life will be shorter due to rapid degradation and It may even get you killed. Achieving this thermal stability often comes at a cost however it consumes a large amount of energy and has a negative effect on vehicle range and overall efficiency. This paper explores the application of Model Predictive Control (MPC) in electric vehicle battery thermal management systems (BTMS) to improve energy utilization.
In this paper, the thermal management problem is formulated as a constrained optimization problem. The target to be minimized is energy consumption of the cooling system, within the boundaries given by safe operational temperatures for battery. The ability of MPC to predict system behavior and account for future disturbances allows it to take proactive control decisions. A pragmatic dynamic battery-pack thermal model is presented here; it has been validated against experiment data. The MPC algorithm is then tested for real-world performance using various driving cycles and environmental conditions with this model integrated into it. The results show that by relying on proactive decision-making rather than simply responding when things go wrong, the proposed strategy can significantly reduce energy consumption. In addition, with its adaptability to different thermal loads and temperature conditions as well as driving patterns, the control system is robust and suitable for actual use.
It has been shown that MPC offers an intelligent approach to controlling the temperatures of EVs, potentially extending vehicle range and improving battery life. Future work will focus on real time implementation challenges and integration with vehicle energy management control systems.