Application of Unscented Kalman Filter in the SOC Estimation of Li-ion Battery for Autonomous Mobile Robot

Pu, Shi; Zhao, Yiwen

doi:10.1109/icia.2006.305934

Cited by 20 publications

(10 citation statements)

References 11 publications

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“…Additionally, some improved algorithms based on these methods have been proposed, such as the adaptive Kalman filter [11], the unscented Kalman filter (UKF) [12][13][14][15], the strong tracking cubature Kalman filter (STCKF) [16], and the unscented PF [17]. The Coulomb counting method is widely used for SOC estimation of electric vehicles because it is a simple algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Cubature Particle Filter Algorithm to Estimate the State of the Charge of Lithium-Ion Batteries Based on a Second-Order Equivalent Circuit Model

et al. 2017

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Abstract:The state of charge (SOC) is the residual capacity of a battery. The SOC value indicates the mileage endurance, and an accurate SOC value is required to ensure the safe use of the battery to prevent over-and over-discharging. However, unlike size and weight, battery power is not easily determined. As a consequence, we can only estimate the SOC value based on the external characteristics of the battery. In this paper, a cubature particle filter (CPF) based on the cubature Kalman filter (CKF) and the particle filter (PF) is presented for accurate and reliable SOC estimation. The CPF algorithm combines the CKF and PF algorithms to generate a suggested density function for the PF algorithm based on the CKF. The second-order resistor-capacitor (RC) equivalent circuit model was used to approximate the dynamic performance of the battery, and the model parameters were identified by fitting. A dynamic stress test (DST) was used to separately estimate the accuracy and robustness of the CKF and the CPF algorithms. The experimental results show that the CPF algorithm exhibited better accuracy and robustness than the CKF algorithm.

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Section: Introductionmentioning

confidence: 99%

A Cubature Particle Filter Algorithm to Estimate the State of the Charge of Lithium-Ion Batteries Based on a Second-Order Equivalent Circuit Model

et al. 2017

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“…Piller et al provide an overview in [1]. Later on, there have been more methods reported in [2]- [6]. The method of Coulomb counting is presently the most widely used one, which is an open-loop algorithm requiring dynamic measurement of the cell/battery current [7], but accumulation of measurement errors happens due to reliance on integration and also precise initial SoC is required in this method, which can be difficult to determine accurately.…”

Section: Battery Soc Estimationmentioning

confidence: 99%

Battery state of charge estimation and online wheel slip ratio control for autonomous wheeled mobile robot

Partovibakhsh¹

2021

Preprint

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For autonomous mobile robots moving in unknown environment, accurate estimation of available power along with the robot power demand for each mission is paramount to successful completion of that mission. Regarding the power consumption, the control unit deals with two tasks simultaneously: 1) it has to monitor the power supply (batteries) state of charge (SoC) constantly. This leads to estimation of robot current available power. Besides, batteries are sensitive to deep discharge or overcharge. The battery SoC is an essential factor in power management of a mobile robot. Accurate estimation of the battery SoC can improve power management, optimize the performance, extend the lifetime, and prevent permanent damage to the batteries. 2) The dynamic characteristics of the terrain the robot traverse requires rapid online modifications in its behaviour. The power required for driving a wheel is an increasing function of its slip ratio. For a wheeled robot moving for driving a wheel is an increasing function of its slip ratio. For a wheeled robot moving on different terrains, slip of the wheels should be checked and compensated for to keep the robot moving with less power consumption. To reduce the power consumption, the target robot moving with less power consumption. To reduce the power consumption, the target of the control system is to keep the slip ratio of the driving wheels around the desired value of the control system is to keep the slip ratio of the driving wheels around the desired value. To fulfill the above mentioned tasks, in this thesis, to increase model validity of lithium-ion battery in various charge/discharge scenarios during the mobile robot operation, the battery capacity fade and internal resistance change are modeled by adding them as state variables to a state space model. Using the output measured data, adaptive unscented Kalman Filter (AUKF) is employed for online model parameters identification of the equivalent circuit model at each sampling time. Subsequently, based on the updated model parameters, SoC estimation is conducted using AUKF. The effectiveness of the proposed method is verified through experiments under different power duties in the lab environment through experiments under different power duties in the lab environment. Better results are obtained both in battery model parameters estimation and the battery SoC estimation in comparison with other Kalman filter extensions. Furthermore, for effective control of the slip ratio, a model-based approach to estimating the longitudinal velocity of the mobile robot is presented. The AUKF is developed to estimate the vehicle longitudinal velocity and the wheel angular velocity using measurements from wheel encoders. Based on the estimated slip ratio, a sliding mode controller is designed for slip control of the uncertain nonlinear dynamical system in the presence of model uncertainties, parameter variations, and disturbances. Experiments are carried out in real time on a four-wheel mobile robot to verify the effectiveness of the estimation algorithm and the controller. It is shown that the controller is able to control the slip ratio of the mobile robot on different terrains while adaptive concept of AUKF leads to better results than the unscented Kalman filter in estimating the vehicle velocity which is difficult to measure in actual practice.

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“…In fact, instead of linearization required by EKF, in UT a set of weighted sigma points are chosen deterministically in a way that the sample mean and the sample covariance of these points match those of the priori distribution. Each point is then propagated through a nonlinear function, and the predicted mean and covariance are calculated from the transformed samples [6], [24].…”

Section: B Parameter Identification Using Ukfmentioning

confidence: 99%

“…The majority of these techniques have been discussed thoroughly by Piller et al in [1] and developed later as in [2]- [6]. Coulomb counting method is one of the most commonly used technique which is an open-loop algorithm requiring dynamic measurement of the cell/battery current [7]- [8], but accumulation of measurement errors happens due to reliance on integration and it is also difficult to determine This the initial value of SoC accurately.…”

Section: Introductionmentioning

confidence: 98%

Online estimation of model parameters and state-of-charge of Lithium-Ion battery using Unscented Kalman Filter

Partovibakhsh

Liu

2012

2012 American Control Conference (ACC)

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For the operation of Autonomous Mobile Robot (AMR) in unknown environments, accurate estimation of internal parameters and consequently precise prediction of the battery state of charge (SoC) are critical issues for power management. Battery performance can be affected by factors such as temperature deviation, discharge/charge current, Coulombic efficiency losses, and aging. Thus, in order to increase the model accuracy, it is important to update the model parameters online. In this paper, the Unscented Kalman Filter (UKF) is employed for the online estimation of the Lithium-Ion battery model parameters and the battery SoC based on the updated model. The proposed method is evaluated experimentally, and the results are compared with that of the Extended Kalman Filter (EKF). The comparison with the EKF shows that UKF provides better accuracy both in battery parameters estimation and the battery SoC estimation.

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Application of Unscented Kalman Filter in the SOC Estimation of Li-ion Battery for Autonomous Mobile Robot

Cited by 20 publications

References 11 publications

A Cubature Particle Filter Algorithm to Estimate the State of the Charge of Lithium-Ion Batteries Based on a Second-Order Equivalent Circuit Model

A Cubature Particle Filter Algorithm to Estimate the State of the Charge of Lithium-Ion Batteries Based on a Second-Order Equivalent Circuit Model

Battery state of charge estimation and online wheel slip ratio control for autonomous wheeled mobile robot

Online estimation of model parameters and state-of-charge of Lithium-Ion battery using Unscented Kalman Filter

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