Design And Implementation Of A Power Management System Based On Exoskeletal Exoskeletal Robotics

The exoskeleton is a new wearable device, it can be worn on the outside of the body to provide protection and additional power, and improve wearer’s exercise ability, so that the wearer can complete many complex work easily. Therefore, the exoskeleton have a very broad prospect in rehabilitation, disaster relief, military war, emergency relief and other aspects.In this paper, we mainly study the application of the power management system for lower limb exoskeleton. The power management system provides the energy and dynamic management function for each module of the exoskeleton. Its reliability will directly determine whether the exoskeleton can run normally and stably. The system is capable of detecting battery parameters and estimating the state of charge (SOC) in real time, and provide the remaining power, battery life time of Exoskeleton and other information,but also to prevent the occurrence of battery overvoltage, overcurrent, overtemperature and other abnormal conditions, and ensure the lower limb Exoskeleton wearer personal safety and prolong battery life.In this paper, I design the hardware circuit and software code of each module of the power management system. The hardware design is divided into: microprocessor hardware design, power supply hardware design, parameters detection hardware design,communication interface hardware design, human-computer interaction and other hardware design; Program code written mainly to complete the underlying driver code,SOC estimation procedures, etc..According to the SOC estimation of lithium battery, we present a Kalman filterting correction algorithm, namely the SOC of the battery group are estimated online by the open circuit voltage, ampere hour integral method, and Kalman filtertinging method combined estimation. The method uses EKF (extended Kalman filterting) has the characteristics of strong correction ability, so the estimation results of the open circuit voltage and ampere hour integration method can be revised and improved.Finally, I made the lithium battery SOC estimation precision experiment, and through the analysis of experimental results, the Kalman filterting correction algorithm in this paper has good estimation accuracy, It can reach the expected requirements of Exoskeleton lithium battery SOC estimation.