Multi-step ahead voltage prediction and voltage fault diagnosis based on gated recurrent unit neural network and incremental training

Accurate and early detection of voltage faults facilitates the driver and battery management system to take protective measures and reduce property damage and passenger injury. To identify the battery operation fault in a timely manner, this study develops an accurate multi-step voltage prediction and voltage fault diagnosis method based on gated recurrent unit neural network and incremental training. First, considering the impacts of drivers' behaviors and vehicle states on battery performance under practical operations, a long-term operation dataset of electric scooters is acquired and established, and the Pearson correlation coefficient is applied to quantify these correlations. Then, the gated recurrent unit neural network, together with the multi-step ahead prediction scheme, is advanced to construct the voltage prediction model. Next, to effectively capture the per-formance variation of battery under complex dynamic operating environment, the incremental learning approach is developed to adaptively update the prediction model. Finally, the fault diagnosis strategy is pro-posed, with the combination of the voltage prediction model, to accurately detect battery faults of over-voltage, under-voltage, over-voltage change rate and poor consistency. The experimental validations highlight that the proposed method can predict the battery voltage 1 min in advance, and detect battery faults in real time with high accuracy.

Automated summary

Accurate and early detection of voltage faults is crucial for protecting property and passengers. This study develops a precise voltage prediction and fault diagnosis method using a gated recurrent unit neural network and incremental training. The method can predict battery voltage in advance and detect faults with high accuracy.