Physics-informed neural network approach for heat generation rate estimation of lithium-ion battery under various driving conditions

Abstract

Accurate insight into the heat generation rate (HGR) of lithium-ion batteries (LIBs) is one of key issues for battery management systems to formulate thermal safety warning strategies in advance. For this reason, this paper proposes a novel physics-informed neural network (PINN) approach for HGR estimation of LIBs under various driving conditions. Specifically, a single particle model with thermodynamics (SPMT) is first constructed for extracting the critical physical knowledge related with battery HGR. Subsequently, the surface concentrations of positive and negative electrodes in battery SPMT model are integrated into the bidirectional long short-term memory (BiLSTM) networks as physical information. And combined with other feature variables, a novel PINN approach to achieve HGR estimation of LIBs with higher accuracy is constituted. Additionally, some critical hyperparameters of BiLSTM used in PINN approach are determined through Bayesian optimization algorithm (BOA) and the results of BOA-based BiLSTM are compared with other traditional BiLSTM/LSTM networks. Eventually, combined with the HGR data generated from the validated virtual battery, it is proved that the proposed approach can well predict the battery HGR under the dynamic stress test (DST) and worldwide light vehicles test procedure (WLTP), the mean absolute error under DST is 0.542 kW/m3, and the root mean square error under WLTP is 1.428 kW/m3 at 25 ℃. Lastly, the investigation results of this paper also show a new perspective in the application of the PINN approach in battery HGR estimation.