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Battery charge state estimate for a robotic forklift routing system

机译:机器人叉车路由系统的电池充电状态估计

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In the context of robotic forklift, battery management is essential and may be considered a key issue in the logistic system management for intelligent warehouses, where goods must be delivered on time according to the monitoring battery State of Charge (SOC) applied to routing system is a tendency to be considered in the planning of warehouses. Based on this scenario, this paper describes a method based on the use of Extended Kalman Filter (EKF), which uses the cell combined model to estimate the battery SOC. Tests were performed to evaluate the estimated battery consumption considering Open Voltage Circuit (OCV) and SOC EKF method applied in a mini robotic forklift. It was possible to verify the battery consumption needed to execute a determined task path and assign a route for the robotic forklift considering the actual SOC.
机译:在机器人叉车的背景下,电池管理是必不可少的,并且可能被视为智能仓库的物流系统管理中的关键问题,在智能仓库中,必须根据应用于路由系统的监视电池充电状态(SOC)准时交货。仓库规划中要考虑的一种趋势。基于这种情况,本文介绍了一种基于扩展卡尔曼滤波器(EKF)的方法,该方法使用电池单元组合模型来估计电池SOC。考虑到微型机器人叉车中采用的开路电压(OCV)和SOC EKF方法,进行了测试以评估估计的电池消耗。考虑到实际SOC,可以验证执行确定的任务路径并为机器人叉车分配路线所需的电池消耗。

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