Self-learning state-of-available-power prediction for lithium-ion batteries in electrical vehicles

This paper describes an overall system for state-of-available-power (SoAP) prediction for a lithium-ion battery pack. The essential part of this method is based on an adaptive network architecture which utilizes both fuzzy model (FIS) and artificial neural network (ANN) into the framework of adaptive neuro-fuzzy inference system (ANFIS). While battery aging proceeds, the system is capable of delivering accurate power prediction for the typical temperature range. Due to design property of ANN, the network parameters are adapted on-line to the current states (state of charge (SoC), state of health (SoH), temperature). SoC is required as an input parameter to SoAP module and high accuracy is crucial for a reliable self-learning capability. Therefore, a reasonable way to determine the battery state variables is proposed applying a combination of several partly different algorithms. Among others SoC boundary estimation methods, robust extended Kalman filter (REKF) for recalibration of amp hour counters was implemented. ANFIS then achieves the SoAP estimation by means of time forward voltage prognosis (TFVP) before a power pulse. The tradeoff between computational cost of batch-learning and accuracy during on-line adaptation was optimized resulting in a real-time system. The verification was performed on a software-in-the-loop test bench setup using a 53 Ah lithium-ion cell.