Bayesian Hyperparameter Optimization of stacked Bidirectional Long Short-Term Memory neural network for the State of Charge estimation

The battery industry has recently grown as a result of electric power adoption in many applications showing less reliance on fossil fuels. Accurate estimation of the State of Charge (SOC) is vital for optimal Battery Management System (BMS) operation to maintain the use of the battery within the safety limits. Data-driven methods require a careful hyperparameter selection as their results strongly depend on their choice. Heuristic methods involve manual tuning or exhaustive ways, which can be computationally demanding. Here a Bayesian Hyperparameter Optimization (BHO) with a Gaussian process is proposed, exploiting the benefits of the probabilistic approach to hyperparameter tuning to reduce the computational effort. For the accurate prediction of SOC, the BHO technique is implemented on a stacked Bidirectional Long Short-Term Memory (BiLSTM) neural network, incorporating a novel set of hyperparameters. The presented method is validated using two well-documented public datasets. Results are compared using datasets with different time granularity, revealing accuracy and computational differences while exhibiting superior yield capabilities compared to other state-of-the-art methods. To assess the computational effort associated with data size and model creation, a time granularity analysis was performed. The analysis demonstrated that decreasing the timestep resulted in reduced network creation time while maintaining similar error levels. Moreover, in order to ensure the comparability of the method, the metric of Floating-point Operations (FLOPs) was integrated to quantify the network’s computational volume. Finally, a sensitivity analysis performed on the time-series window span showed accuracy and computational load differences highlighting the need for caution and careful consideration.