Available Capacity Computation Model Based on Long Short-Term Memory Recurrent Neural Network for Gelled-Electrolyte Batteries in Golf Carts

A deep neural network model for the investigation of the discharging voltage, temperature, cycle, and state of charge-dependent behavior of gel batteries is presented in this paper. The proposed model utilizes a long short-term memory recurrent neural network to investigate the estimation of the state of charge and the state of health. The model could serve as a management strategy for the Vehicle Service Management Centers to reduce maintenance costs by regulating the battery status, managing the driving paths, adjusting the driving areas, and replacing the batteries in batches. The management strategy utilizes the state of charge and the state of health of gel batteries as key variables in assigning driving paths and areas to golf carts. In the actual on-road experiment, the model successfully tested and verified the state of charge of the gel batteries in the golf cart with a mean absolute error of 0.376% which was better than the feedforward neural network of 2.8% and the recurrent neural network of 1.7%. These results indicate that the model can accurately estimate the state of charge and the remaining service life of batteries which provide clear indicators for the vehicle service management systems.