Enhancing Electric Wheelchair Safety via Battery State of Charge Estimation With PCC–NSSR–LSTM Method

Abstract

This study explores a novel algorithm created to predict the state of charge (SOC) of batteries in electric wheelchairs (EWs) to improve EW safety by adjusting SOC thresholds and reducing consumer range anxiety. It involves collecting experimental data from lithium iron phosphate (LFP) battery cells over 1500 cycles at 25°C, encompassing various parameters. With the Pearson correlation coefficient (PCC), a select set of key parameters including voltage, temperature, dQ/dV (capacity increase to voltage increase ratio) are chosen as inputs for non-linear state space reconstruction long short-term memory (NSSR-LSTM) neural networks, facilitating precise SOC predictions. The study showcases the precision of SOC predictions by revealing outcomes for different cycles, such as 900, 1000 and 1100. In addition to EWs, the proposed PCC–NSSR–LSTM method is also applicable to other mobility devices, including electric bicycles, golf carts and similar vehicles.