Novel deep learning model developed for battery lifespan prediction

Accurate prediction of lithium battery lifespan is crucial for the proper functioning of electrical equipment. However, predicting battery lifespan accurately is challenging due to the nonlinearity of capacity degradation and the uncertainty of operating conditions.

Recently, Prof. Chen Zhongwei and Assoc. Prof. Mao Zhiyu from the Dalian Institute of Chemical Physics of the Chinese Academy of Sciences, in collaboration with Prof. Feng Jiangtao from Xi’an Jiaotong University, designed a novel deep learning model, the dual stream-vision transformer with the efficient self-attention mechanism (DS-ViT-ESA), to predict the current cycle life (CCL) and remaining useful life (RUL) of the target battery. The study was published in IEEE Transactions on Transportation Electrification.

The researchers developed the deep learning model using a small amount of charging cycle data. This model employed a vision transformer structure with a dual-stream framework and an efficient self-attention mechanism to capture and integrate hidden features across multiple time scales.

The model was able to accurately predict the battery’s CCL and RUL. With just 15 charging cycle data points, it achieved RUL and CLL prediction errors of only 5.40% and 4.64%, respectively. Moreover, the model maintained low prediction errors even when tested on charging strategies not included in the training dataset, demonstrating its zero-shot generalization capability.

This battery lifespan prediction model was also a crucial component of the first-generation Battery Digital Brain, called PBSRD Digit. The system integrated with this algorithm has significantly improved accuracy. Currently, the Battery Digital Brain system serves as the core energy management system for large-scale commercial storage and electric vehicles, with the capability to be deployed on both cloud servers and client-side embedded devices.

“The battery lifespan prediction model effectively balances prediction accuracy with computational cost, thereby increasing the application value of the Battery Digital Brain for lifespan estimation,” said Prof. Chen. “We plan to further optimize the model using techniques such as model distillation and pruning, aiming to enhance robustness and resource utilization of the system.”

Accurate prediction of lithium battery lifespan is crucial for the proper functioning of electrical equipment. However, predicting battery lifespan accurately is challenging due to the nonlinearity of capacity degradation and the uncertainty of operating conditions.

Recently, Prof. Chen Zhongwei and Assoc. Prof. Mao Zhiyu from the Dalian Institute of Chemical Physics of the Chinese Academy of Sciences, in collaboration with Prof. Feng Jiangtao from Xi’an Jiaotong University, designed a novel deep learning model, the dual stream-vision transformer with the efficient self-attention mechanism (DS-ViT-ESA), to predict the current cycle life (CCL) and remaining useful life (RUL) of the target battery. The study was published in IEEE Transactions on Transportation Electrification.

The researchers developed the deep learning model using a small amount of charging cycle data. This model employed a vision transformer structure with a dual-stream framework and an efficient self-attention mechanism to capture and integrate hidden features across multiple time scales.

The model was able to accurately predict the battery’s CCL and RUL. With just 15 charging cycle data points, it achieved RUL and CLL prediction errors of only 5.40% and 4.64%, respectively. Moreover, the model maintained low prediction errors even when tested on charging strategies not included in the training dataset, demonstrating its zero-shot generalization capability.

This battery lifespan prediction model was also a crucial component of the first-generation Battery Digital Brain, called PBSRD Digit. The system integrated with this algorithm has significantly improved accuracy. Currently, the Battery Digital Brain system serves as the core energy management system for large-scale commercial storage and electric vehicles, with the capability to be deployed on both cloud servers and client-side embedded devices.

“The battery lifespan prediction model effectively balances prediction accuracy with computational cost, thereby increasing the application value of the Battery Digital Brain for lifespan estimation,” said Prof. Chen. “We plan to further optimize the model using techniques such as model distillation and pruning, aiming to enhance robustness and resource utilization of the system.”