Översikt
18 dec 2026, 09:0018 dec 2026, 12:00
Plats: EA (Room 4233), EDIT Building, Chalmers
Opponent:Professor Davide Martino Raimondo, Department of Engineering and Architecture, University of Trieste, Italy
Avhandling:Läs avhandlingen (Öppnas i ny flik)
Lateral surface cooling can limit temperature rise in lithium-ion batteries through its large heat-transfer area, whereas tab cooling can reduce internal thermal gradients by exploiting the high thermal conductivity of the current collectors. This thesis investigates how these complementary characteristics can be integrated through modelling and predictive control for battery thermal management.
A control-oriented thermal modelling framework is developed using the Chebyshev spectral-Galerkin method and model component decomposition. The resulting reduced-order two-dimensional models enable independent manipulation of thermal boundary conditions at the battery tabs and lateral surface while reproducing the spatial temperature field at low computational cost. Building on this framework, tab and surface cooling are integrated via a constrained optimal coolant allocation problem in which coolant is dynamically distributed among the cooling interfaces. A first-principles coolant model and valve-actuation model are coupled with the electrothermal dynamics. The resulting nonlinear problem is solved using real-time iteration model predictive control (MPC), which closely reproduces nonlinear MPC while reducing mean computation time from several seconds to approximately 19 ms. The framework is further extended to degradation-aware control by reformulating a semi-empirical degradation model as dynamic states and incorporating spatial temperature non-uniformity through the volume-averaged temperature and temperature variance, reducing degradation model computation by more than 20×.
Across the control studies, integrated tab-surface cooling provides the best overall trade-off between temperature tracking, thermal uniformity, accumulated capacity loss, and coolant utilisation compared with conventional cooling configurations. Together, these contributions establish a basis for hybrid battery cooling combining spatially resolved modelling with real-time predictive control in battery systems.
A control-oriented thermal modelling framework is developed using the Chebyshev spectral-Galerkin method and model component decomposition. The resulting reduced-order two-dimensional models enable independent manipulation of thermal boundary conditions at the battery tabs and lateral surface while reproducing the spatial temperature field at low computational cost. Building on this framework, tab and surface cooling are integrated via a constrained optimal coolant allocation problem in which coolant is dynamically distributed among the cooling interfaces. A first-principles coolant model and valve-actuation model are coupled with the electrothermal dynamics. The resulting nonlinear problem is solved using real-time iteration model predictive control (MPC), which closely reproduces nonlinear MPC while reducing mean computation time from several seconds to approximately 19 ms. The framework is further extended to degradation-aware control by reformulating a semi-empirical degradation model as dynamic states and incorporating spatial temperature non-uniformity through the volume-averaged temperature and temperature variance, reducing degradation model computation by more than 20×.
Across the control studies, integrated tab-surface cooling provides the best overall trade-off between temperature tracking, thermal uniformity, accumulated capacity loss, and coolant utilisation compared with conventional cooling configurations. Together, these contributions establish a basis for hybrid battery cooling combining spatially resolved modelling with real-time predictive control in battery systems.
Godwin Peprah
- Doktorand, System- och reglerteknik, Elektroteknik
Godwin Peprah
- Doktorand, System- och reglerteknik, Elektroteknik