A reliable estimation method for mining lithium-ion battery

2021 ◽  
pp. 1-15
Author(s):  
Kaifeng Huang ◽  
Juqiang Feng ◽  
Zegong Liu ◽  
Long Wu ◽  
Xing Zhang
Keyword(s):  
Energy Management ◽  
Unscented Kalman Filter ◽  
Constant Current ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Change Rate ◽  
Soc Estimation ◽  
Maximum Change ◽  
Power Battery ◽  
Self Tuning

Power battery SOC (state of charge, SOC) is one of the important decision-making factors of energy management. Accurate estimation plays an important role in optimizing vehicle energy management and improving the utilization of power battery energy. The key to accurate estimation of SOC is to determine circuit model parameters and estimation methods. The research object of this article is lithium manganese oxide battery for mining (LiMn2O4). The experiments of multiplying power, temperature and HPPC (hybrid pulse power characteristic, HPPC) are carried out. A self-tuning calculation method of dynamic system is proposed, and the dynamic self-tuning model based on second-order RC is established. At the same time, in view of the shortcoming that the UKF (Unscented Kalman Filter, UKF) algorithm cannot estimate the noise in real time, In order to improve the accuracy of battery SOC estimation, an adaptive square root unscented Kalman filter (ASR-UKF) algorithm is proposed, which can make the noise statistical characteristics follow the estimation results for adaptive adjustment. Finally, the constant current and dynamic conditions are tested. The results show that the maximum change rate of model parameters with magnification is 76%, and the maximum change rate with temperature is 73.7%. The analysis of dynamic characteristics is a key factor to improve the accuracy of SOC estimation; ASR-UKF Compared with the UKF algorithm, the error is reduced by 78% under constant current conditions and 85.7% under dynamic conditions. The reliability and real-time performance of the algorithm can be obtained by comparing the simulation data with the actual data. The conclusions of this paper can be used as a theoretical basis, which can be used for model analysis of lithium batteries for mining and estimation of internal state variables.

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

2021 ◽  
Author(s):  
Maral Partovibakhsh
Keyword(s):  
Kalman Filter ◽  
Power Consumption ◽  
Mobile Robot ◽  
Unscented Kalman Filter ◽  
Longitudinal Velocity ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Slip Ratio ◽  
Soc Estimation ◽  
Increasing Function

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.

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

2021 ◽  
Author(s):  
Maral Partovibakhsh
Keyword(s):  
Kalman Filter ◽  
Power Consumption ◽  
Mobile Robot ◽  
Unscented Kalman Filter ◽  
Longitudinal Velocity ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Slip Ratio ◽  
Soc Estimation ◽  
Increasing Function

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.

scholarly journals Adaptive State-of-Charge Estimation for Lithium-Ion Batteries by Considering Capacity Degradation

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 122
Author(s):  
Peipei Xu ◽  
Junqiu Li ◽  
Chao Sun ◽  
Guodong Yang ◽  
Fengchun Sun
Keyword(s):  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Maximum Error ◽  
State Of Charge ◽  
Recursive Least Squares ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Soc Estimation ◽  
Capacity Degradation ◽  
Battery Capacity

The accurate estimation of a lithium-ion battery’s state of charge (SOC) plays an important role in the operational safety and driving mileage improvement of electrical vehicles (EVs). The Adaptive Extended Kalman filter (AEKF) estimator is commonly used to estimate SOC; however, this method relies on the precise estimation of the battery’s model parameters and capacity. Furthermore, the actual capacity and battery parameters change in real time with the aging of the batteries. Therefore, to eliminate the influence of above-mentioned factors on SOC estimation, the main contributions of this paper are as follows: (1) the equivalent circuit model (ECM) is presented, and the parameter identification of ECM is performed by using the forgetting-factor recursive-least-squares (FFRLS) method; (2) the sensitivity of battery SOC estimation to capacity degradation is analyzed to prove the importance of considering capacity degradation in SOC estimation; and (3) the capacity degradation model is proposed to perform the battery capacity prediction online. Furthermore, an online adaptive SOC estimator based on capacity degradation is proposed to improve the robustness of the AEKF algorithm. Experimental results show that the maximum error of SOC estimation is less than 1.3%.

scholarly journals A Novel Deep Learning-Based State-of-Charge Estimation for Renewable Energy Management System in Hybrid Electric Vehicles

Mathematics ◽  
2022 ◽  
Vol 10 (2) ◽  
pp. 260
Author(s):  
Mahendiran T. Vellingiri ◽  
Ibrahim M. Mehedi ◽  
Thangam Palaniswamy
Keyword(s):  
Renewable Energy ◽  
Deep Learning ◽  
Energy Management ◽  
Electric Vehicles ◽  
Management System ◽  
Hybrid Electric Vehicles ◽  
State Of Charge ◽  
Accurate Estimation ◽  
Soc Estimation ◽  
Hybrid Electric

In recent years, alternative engine technologies are necessary to resolve the problems related to conventional vehicles. Electric vehicles (EVs) and hybrid electric vehicles (HEVs) are effective solutions to decarbonize the transportation sector. It also becomes important to shift from traditional houses to smart houses and from classical vehicles to EVs or HEVs. It is needed to combine renewable energy sources (RESs) such as solar photovoltaics, wind energy systems, and various forms of bio-energies. Among various HEV technologies, an effective battery management system (BMS) still remains a crucial issue that is majorly used for indicating the battery state of charge (SOC). Since over-charging and over-discharging result in inevitable impairment to the batteries, accurate SOC estimation desires to be presented by the BMS. Although several SOC estimation techniques exist to regulate the SOC of the battery cell, it is needed to improvise the SOC estimation performance on HEVs. In this view, this paper focuses on the design of a novel deep learning (DL) with SOC estimation model for secure renewable energy management (DLSOC-REM) technique for HEVs. The presented model employs a hybrid convolution neural network and long short-term memory (HCNN-LSTM) model for the accurate estimation of SOC. In order to improve the SOC estimation outcomes of the HCNN-LSTM model, the barnacles mating optimizer (BMO) is applied for the hyperpower tuning process. The utilization of the HCNN-LSTM model makes the modeling process easier and offers a precise depiction of the input–output relationship of the battery model. The design of BMO based HCNN-LSTM model for SOC estimation shows the novelty of the work. An extensive experimental analysis highlighted the supremacy of the proposed model over other existing methods in terms of different aspects.

scholarly journals Online Parameter Identification and State of Charge Estimation of Battery Based on Multitimescale Adaptive Double Kalman Filter Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Wenxian Duan ◽  
Chuanxue Song ◽  
Yuan Chen ◽  
Feng Xiao ◽  
Silun Peng ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Unscented Kalman Filter ◽  
Least Square Method ◽  
State Of Charge ◽  
Least Square ◽  
Accurate Estimation ◽  
Battery Life ◽  
Model Parameters ◽  
Simulation Results

An accurate state of charge (SOC) can provide effective judgment for the BMS, which is conducive for prolonging battery life and protecting the working state of the entire battery pack. In this study, the first-order RC battery model is used as the research object and two parameter identification methods based on the least square method (RLS) are analyzed and discussed in detail. The simulation results show that the model parameters identified under the Federal Urban Driving Schedule (HPPC) condition are not suitable for the Federal Urban Driving Schedule (FUDS) condition. The parameters of the model are not universal through the HPPC condition. A multitimescale prediction model is also proposed to estimate the SOC of the battery. That is, the extended Kalman filter (EKF) is adopted to update the model parameters and the adaptive unscented Kalman filter (AUKF) is used to predict the battery SOC. The experimental results at different temperatures show that the EKF-AUKF method is superior to other methods. The algorithm is simulated and verified under different initial SOC errors. In the whole FUDS operating condition, the RSME of the SOC is within 1%, and that of the voltage is within 0.01 V. It indicates that the proposed algorithm can obtain accurate estimation results and has strong robustness. Moreover, the simulation results after adding noise errors to the current and voltage values reveal that the algorithm can eliminate the sensor accuracy effect to a certain extent.

scholarly journals State of Charge Estimation for Lithium-Bismuth Liquid Metal Batteries

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 183 ◽  
Author(s):  
Xian Wang ◽  
Zhengxiang Song ◽  
Kun Yang ◽  
Xuyang Yin ◽  
Yingsan Geng ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Liquid Metal ◽  
Equivalent Circuit ◽  
Unscented Kalman Filter ◽  
State Of Charge ◽  
Convergence Time ◽  
Constant Current ◽  
Estimation Methods ◽  
Soc Estimation ◽  
Liquid Metal Batteries

Lithium-bismuth liquid metal batteries have much potential for stationary energy storage applications, with characteristics such as a large capacity, high energy density, low cost, long life-span and an ability for high current charge and discharge. However, there are no publications on battery management systems or state-of-charge (SoC) estimation methods, designed specifically for these devices. In this paper, we introduce the properties of lithium-bismuth liquid metal batteries. In analyzing the difficulties of traditional SoC estimation techniques for these devices, we establish an equivalent circuit network model of a battery and evaluate three SoC estimation algorithms (the extended Kalman filter, the unscented Kalman filter and the particle filter), using constant current discharge, pulse discharge and hybrid pulse (containing charging and discharging processes) profiles. The results of experiments performed using the equivalent circuit battery model show that the unscented Kalman filter gives the most robust and accurate performance, with the least convergence time and an acceptable computation time, especially in hybrid pulse current tests. The time spent on one estimation with the three algorithms are 0.26 ms, 0.5 ms and 1.5 ms.

scholarly journals SOC Estimation with an Adaptive Unscented Kalman Filter Based on Model Parameter Optimization

2019 ◽  
Vol 9 (19) ◽  
pp. 4177
Author(s):  
Xiangwei Guo ◽  
Xiaozhuo Xu ◽  
Jiahao Geng ◽  
Xian Hua ◽  
Yan Gao ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Equivalent Circuit ◽  
Parameter Optimization ◽  
Unscented Kalman Filter ◽  
Equivalent Circuit Model ◽  
Circuit Model ◽  
Second Order ◽  
Model Parameters ◽  
Soc Estimation ◽  
Adaptive Unscented Kalman Filter

State of charge (SOC) estimation is generally acknowledged to be one of the most important functions of the battery management system (BMS) and is thus widely studied in academia and industry. Based on an accurate SOC estimation, the BMS can optimize energy efficiency and protect the battery from being over-charged or over-discharged. The accurate online estimation of the SOC is studied in this paper. First, it is proved that the second-order resistance capacitance (RC) model is the most suitable equivalent circuit model compared with the Thevenin and multi-order models. The second-order RC equivalent circuit model is established, and the model parameters are identified. Second, the reasonable optimization of model parameters is studied, and a reasonable optimization method is proposed to improve the accuracy of SOC estimation. Finally, the SOC is estimated online based on the adaptive unscented Kalman filter (AUKF) with optimized model parameters, and the results are compared with the results of an estimation based on pre-optimization model parameters. Simulation experiments show that, without affecting the convergence of the initial error of the AUKF, the model after parameter optimization has a higher online SOC estimation accuracy.

scholarly journals An Accurate State of Charge Estimation Method for Lithium Iron Phosphate Battery Using a Combination of an Unscented Kalman Filter and a Particle Filter

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4536 ◽  
Author(s):  
Thanh-Tung Nguyen ◽  
Abdul Basit Khan ◽  
Younghwi Ko ◽  
Woojin Choi
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Lithium Iron Phosphate ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Iron Phosphate ◽  
State Of Charge ◽  
Superior Performance ◽  
Accurate Estimation ◽  
Soc Estimation

An accurate state of charge (SOC) estimation of the battery is one of the most important techniques in battery-based power systems, such as electric vehicles (EVs) and energy storage systems (ESSs). The Kalman filter is a preferred algorithm in estimating the SOC of the battery due to the capability of including the time-varying coefficients in the model and its superior performance in the SOC estimation. However, since its performance highly depends on the measurement noise (MN) and process noise (PN) values, it is difficult to obtain highly accurate estimation results with the battery having a flat plateau OCV (open-circuit voltage) area in the SOC-OCV curve, such as the Lithium iron phosphate battery. In this paper, a new integrated estimation method is proposed by combining an unscented Kalman filter and a particle filter (UKF-PF) to estimate the SOC of the Lithium iron phosphate battery. The equivalent circuit of the battery used is composed of a series resistor and two R-C parallel circuits. Then, it is modeled by a second-order autoregressive exogenous (ARX) model, and the parameters are identified by using the recursive least square (RLS) identification method. The validity of the proposed algorithm is verified by comparing the experimental results obtained with the proposed method and the conventional methods.

The Research of Power Battery SOC Estimation Based on Adaptive Kalman Filter Algorithm

2013 ◽  
Vol 433-435 ◽  
pp. 754-759
Author(s):  
Wei Bo Yu ◽  
Ting Ting Yang ◽  
Cui Yuan Feng ◽  
Hong Jun Li
Keyword(s):  
Kalman Filter ◽  
Lithium Iron Phosphate ◽  
Pulse Discharge ◽  
Iron Phosphate ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Adaptive Kalman Filter ◽  
Soc Estimation ◽  
Power Battery ◽  
Kalman Filter Algorithm

Taking the lithium iron phosphate power battery as the research object, through analysis on characteristics of the battery, this paper chooses the improved second-order RC model as the model of battery whose complexity is moderate and it can better reflect the battery dynamic and static characteristics. Then by pulse discharge experiments and with improved recursive least squares algorithm to identify model parameters online, and puts forward up the adaptive kalman filtering algorithm to estimate battery SOC. The results show that the adaptive kalman filter algorithm can effectively improve battery SOC estimation precision.

The SOC Estimation for Power Battery Using KF Method which Parameters are Updated by Least Square Method

2014 ◽  
Vol 694 ◽  
pp. 67-72 ◽  
Author(s):  
Ying Zhou Pang ◽  
Xing Yong Zhang ◽  
Jian Tao Tian
Keyword(s):  
Kalman Filter ◽  
Least Square Method ◽  
Least Square ◽  
Constant Current ◽  
Counting Method ◽  
Adaptive Kalman Filter ◽  
Soc Estimation ◽  
Battery Model ◽  
Power Battery ◽  
Fixed Parameter

In this article we first introduced some methods for estimating battery’s SOC and their advantages and shortcomings respectively. With experimental data, we proved that parameters of battery model are time variant. So fixed parameter Kalman Filter (FPKF) will not be suitable, then we came up with a new algorithm named adaptive Kalman Filter (APKF),which associated two algorithms—Kalman Filter and Least Square method. Kalman Filer estimates SOC of battery, while Least Square method updates parameters used in Kalman Filter. Then we used battery’s discharging data to test whether this new algorithm took effect. The results produced by Ah-counting method was viewed as a reference because of constant current discharging situation. According to the estimating results, the results produced by APKF have much smaller deviation than that produced by fixed parameters Kalman Filter (FPKF).

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