Virtual experiments for battery state of health estimation based on neural networks and in-vehicle data

To ensure the safety, performance, and warranty of electric vehicles, it is crucial to monitor the evolution of the state of health of lithium-ion batteries. Estimators for the state of health are often based on costly, timeconsuming, and predefined testing procedures under laboratory full cycling conditions. In contrast, automotive operating conditions are highly volatile and thus cannot be interpreted by laboratory feature extraction methods. Given a rapidly growing fleet of electric vehicles and a limited number of battery test facilities, the need for alternative and scalable methods to determine state of health is essential for future developments. In this paper, we present a novel data-driven approach for battery state of health estimation based on the virtual execution of battery experiments. Therefore, an LSTM-based neural network learns the electrical behavior of an automotive battery cell based on in-vehicle driving data. This LSTM model is then used to simulate the electric response during capacity testing, incremental capacity analysis, and peak-power testing, which are explicitly designed for automotive lithium-ion batteries and adapted to real-world customer usage. Results show state-of-the-art accuracy for state of health estimation in terms of internal resistance (1.77% MAE) and remaining capacity estimation (0.60% MAE). This virtual execution of battery experiments is scalable, saves laboratory effort and test facilities, and in return requires only operational driving data.

Automated summary

This paper presents a novel data-driven approach for estimating the state of health of batteries based on virtual execution of battery experiments. The method learns the electrical behavior of batteries based on driving data and simulates the electric response during battery experiments to determine the state of health. The results show state-of-the-art accuracy in terms of internal resistance and remaining capacity estimation.