Extreme learning machine based spatiotemporal modeling of lithium-ion battery thermal dynamics

• A data based thermal model is developed for lithium ion batteries
• The model is efficient enough for online control oriented applications.
• The temperature distribution of the whole battery can be estimated in real-time with the developed model.
• The training of the nonlinear model only contains a linear process.

Due to the overwhelming complexity of the electrochemical related behaviors and internal structure of lithium ion batteries, it is difficult to obtain an accurate mathematical expression of their thermal dynamics based on the physical principal. In this paper, a data based thermal model which is suitable for online temperature distribution estimation is proposed for lithium-ion batteries. Based on the physics based model, a simple but effective low order model is obtained using the Karhunen–Loeve decomposition method. The corresponding uncertain chemical related heat generation term in the low order model is approximated using extreme learning machine. All uncertain parameters in the low order model can be determined analytically in a linear way. Finally, the temperature distribution of the whole battery can be estimated in real time based on the identified low order model. Simulation results demonstrate the effectiveness of the proposed model. The simple training process of the model makes it superior for onboard application.