TY - GEN
T1 - Hierarchical Climate Control Strategy for Electric Vehicles with Door-Opening Consideration
AU - Nam, Sanghyeon
AU - Lee, Hyejin
AU - Kim, Youngki
AU - Kwak, Kyoung Hyun
AU - Han, Kyoungseok
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - This study proposes a novel climate control strategy for electric vehicles (EVs) by addressing door-opening interruptions, an overlooked aspect in EV thermal management. We create and validate an EV simulation model that incorporates door-opening scenarios. Three controllers are compared using the simulation model: (i) a hierarchical non-linear model predictive control (NMPC) with a unique coolant dividing layer and a component for cabin air inflow regulation based on door-opening signals; (ii) a single MPC controller; and (iii) a rule-based controller. The hierarchical controller outperforms, reducing door-opening temperature drops by 46.96% and 51.33% compared to single layer MPC and rule-based methods in the relevant section. Additionally, our strategy minimizes the maximum temperature gaps between the sections during recovery by 86.4% and 78.7%, surpassing single layer MPC and rule-based approaches, respectively. We believe that this result opens up future possibilities for incorporating the thermal comfort of passengers across all sections within the vehicle.
AB - This study proposes a novel climate control strategy for electric vehicles (EVs) by addressing door-opening interruptions, an overlooked aspect in EV thermal management. We create and validate an EV simulation model that incorporates door-opening scenarios. Three controllers are compared using the simulation model: (i) a hierarchical non-linear model predictive control (NMPC) with a unique coolant dividing layer and a component for cabin air inflow regulation based on door-opening signals; (ii) a single MPC controller; and (iii) a rule-based controller. The hierarchical controller outperforms, reducing door-opening temperature drops by 46.96% and 51.33% compared to single layer MPC and rule-based methods in the relevant section. Additionally, our strategy minimizes the maximum temperature gaps between the sections during recovery by 86.4% and 78.7%, surpassing single layer MPC and rule-based approaches, respectively. We believe that this result opens up future possibilities for incorporating the thermal comfort of passengers across all sections within the vehicle.
UR - http://www.scopus.com/inward/record.url?scp=85199751471&partnerID=8YFLogxK
U2 - 10.1109/IV55156.2024.10588432
DO - 10.1109/IV55156.2024.10588432
M3 - Conference contribution
AN - SCOPUS:85199751471
T3 - IEEE Intelligent Vehicles Symposium, Proceedings
SP - 2634
EP - 2639
BT - 35th IEEE Intelligent Vehicles Symposium, IV 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 35th IEEE Intelligent Vehicles Symposium, IV 2024
Y2 - 2 June 2024 through 5 June 2024
ER -