Extended Kalman Filter-Based State of Charge and State of Power Estimation Algorithm for Unmanned Aerial Vehicle Li-Po Battery Packs

The popular and widely reported lithium-ion battery model is the equivalent circuit model (ECM). The suitable ECM structure and matched model parameters are equally important for the state-of-charge (SOC) estimation algorithm. This paper focuses on high-accuracy models and the estimation algorithm with high robustness and accuracy in practical application. Firstly, five ECMs and five parameter identification approaches are compared under the New European Driving Cycle (NEDC) working condition in the whole SOC area, and the most appropriate model structure and its parameters are determined to improve model accuracy. Based on this, a multi-model and multi-algorithm (MM-MA) method, considering the SOC distribution area, is proposed. The experimental results show that this method can effectively improve the model accuracy. Secondly, a fuzzy fusion SOC estimation algorithm, based on the extended Kalman filter (EKF) and ampere-hour counting (AH) method, is proposed. The fuzzy fusion algorithm takes advantage of the advantages of EKF, and AH avoids the weaknesses. Six case studies show that the SOC estimation result can hold the satisfactory accuracy even when large sensor and model errors exist.

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Aerodynamic parameter estimation of an Unmanned Aerial Vehicle based on extended kalman filter and its higher order approach

2010 2nd International Conference on Advanced Computer Control ◽

10.1109/icacc.2010.5487116 ◽

2010 ◽

Cited By ~ 7

Author(s):

Li Meng ◽

Liu Li ◽

S.M. Veres

Keyword(s):

Parameter Estimation ◽

Kalman Filter ◽

Unmanned Aerial Vehicle ◽

Extended Kalman Filter ◽

Higher Order ◽

Aerodynamic Parameter ◽

Aerial Vehicle

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Ultra-wideband based cooperative relative localization algorithm and experiments for multiple unmanned aerial vehicles in GPS denied environments

International Journal of Micro Air Vehicles ◽

10.1177/1756829317695564 ◽

2017 ◽

Vol 9 (3) ◽

pp. 169-186 ◽

Cited By ~ 33

Author(s):

Kexin Guo ◽

Zhirong Qiu ◽

Wei Meng ◽

Lihua Xie ◽

Rodney Teo

Keyword(s):

Kalman Filter ◽

Unmanned Aerial Vehicles ◽

Unmanned Aerial Vehicle ◽

Extended Kalman Filter ◽

Ultra Wideband ◽

Cooperative Localization ◽

Relative Localization ◽

Aerial Vehicles ◽

Aerial Vehicle ◽

Displacement Measurements

This article puts forward an indirect cooperative relative localization method to estimate the position of unmanned aerial vehicles (UAVs) relative to their neighbors based solely on distance and self-displacement measurements in GPS denied environments. Our method consists of two stages. Initially, assuming no knowledge about its own and neighbors’ states and limited by the environment or task constraints, each unmanned aerial vehicle (UAV) solves an active 2D relative localization problem to obtain an estimate of its initial position relative to a static hovering quadcopter (a.k.a. beacon), which is subsequently refined by the extended Kalman filter to account for the noise in distance and displacement measurements. Starting with the refined initial relative localization guess, the second stage generalizes the extended Kalman filter strategy to the case where all unmanned aerial vehicles (UAV) move simultaneously. In this stage, each unmanned aerial vehicle (UAV) carries out cooperative localization through the inter-unmanned aerial vehicle distance given by ultra-wideband and exchanging the self-displacements of neighboring unmanned aerial vehicles (UAV). Extensive simulations and flight experiments are presented to corroborate the effectiveness of our proposed relative localization initialization strategy and algorithm.

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State of Charge Estimation Algorithm for Unmanned Aerial Vehicle Power-Type Lithium Battery Packs Based on the Extended Kalman Filter

Energies ◽

10.3390/en12203960 ◽

2019 ◽

Vol 12 (20) ◽

pp. 3960 ◽

Cited By ~ 2

Author(s):

Haitao Zhang ◽

Ming Zhou ◽

Xudong Lan

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

Control Method ◽

Equivalent Circuit Model ◽

State Of Charge ◽

Energy Utilization ◽

Battery Life ◽

Power Type ◽

Advantages And Disadvantages ◽

Li Po

The lack of endurance is an important reason restricting further development of unmanned aerial vehicles (UAVs). Accurately estimating the state of charge (SOC) of the Li-Po battery can maximize the battery energy utilization and improve the endurance of UAVs. In this paper, the main current methods for estimating the SOC of vehicles were explored and discussed to unveil their advantages and disadvantages. In addition, the extended Kalman filter algorithm based on an equivalent circuit model was used to estimate SOC of power-type Li-Po batteries at different temperatures. The result showed that the closed-loop control method can effectively improve the battery life of small-sized electric UAVs.

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