scholarly journals Dual Nonlinear Kalman Filter-Based SoC and Remaining Capacity Estimation for an Electric Scooter Li-NMC Battery Pack

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 540 ◽  
Author(s):  
Filip Maletić ◽  
Mario Hrgetić ◽  
Joško Deur
Keyword(s):  
Kalman Filter ◽  
Time Estimation ◽  
Detection Algorithm ◽  
Open Circuit ◽  
Battery Management ◽  
Adaptation Mechanism ◽  
Sigma Point ◽  
Estimation Algorithms ◽  
Electric Scooter ◽  
Remaining Capacity

Accurate, real-time estimation of battery state-of-charge (SoC) and state-of-health represents a crucial task of modern battery management systems. Due to nonlinear and battery degradation-dependent behavior of output voltage, the design of these estimation algorithms should be based on nonlinear parameter-varying models. The paper first describes the experimental setup that consists of commercially available electric scooter equipped with telemetry measurement equipment. Next, dual extended Kalman filter-based (DEKF) estimator of battery SoC, internal resistances, and parameters of open-circuit voltage (OCV) vs. SoC characteristic is presented under the assumption of fixed polarization time constant vs. SoC characteristic. The DEKF is upgraded with an adaptation mechanism to capture the battery OCV hysteresis without explicitly modelling it. Parameterization of an explicit hysteresis model and its inclusion in the DEKF is also considered. Finally, a slow time scale, sigma-point Kalman filter-based capacity estimator is designed and inter-coupled with the DEKF. A convergence detection algorithm is proposed to ensure that the two estimators are coupled automatically only after the capacity estimate has converged. The overall estimator performance is experimentally validated for real electric scooter driving cycles.

Estimation of SI Engine Load Torque: Adaptive Kalman Filter vs. Luenberger Estimator

2004 ◽  
Author(s):  
Josˇko Deur ◽  
Danijel Pavkovic´ ◽  
Davor Hrovat
Keyword(s):  
Kalman Filter ◽  
Detection Algorithm ◽  
Si Engine ◽  
Adaptive Kalman Filter ◽  
Third Order ◽  
Load Torque ◽  
Adaptation Mechanism ◽  
Engine Load ◽  
Train Control ◽  
On Line

The SI engine load torque is key information for many engine and power train control systems. Since the torque is not measured in production vehicles, it needs to be estimated on-line. The paper presents design and analysis of second-order and third-order Luenberger load torque estimators. With the aim to reduce the estimator noise sensitivity without deteriorating its transient performance, an adaptive Kalman filter is proposed and compared with the Luenberger estimator. The adaptation mechanism is based on a load torque change detection algorithm. The estimators are examined by computer simulations and experiments.

scholarly journals A variable-length window-wise parameter-dependent state of charge estimation by Kalman filters

2021 ◽  
Author(s):  
Bataa Lkhagvasuren ◽  
Minkyu Kwak ◽  
Hong Sung Jin ◽  
Gyuwon Seo ◽  
Sungyool Bong ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Kalman Filters ◽  
Real Data ◽  
Estimation Algorithm ◽  
Open Circuit ◽  
State Of Charge ◽  
Least Square ◽  
State Equations ◽  
Sigma Point ◽  
Parameter Dependent

<div>This paper proposes a new window-wise state of charge (SOC) estimation algorithm based on Kalman filters (KF). In the first stage, the equivalent circuit model's parameters are estimated by a least square estimation window-wise, assuming a linear SOC and open-circuit voltage (OCV) relation. The algorithm accurately estimates the parameters and observes the changes that depend on SOC. Moreover, based on the estimated parameters, the OCV values are identified. In the next stage, window-wise linear Kalman filter(ES-LKF) without hysteresis and extended Kalman filter (ES-EKF) and sigma-point Kalman filter (ES-SPKF) algorithm with hysteresis are executed to estimate SOC. Having fewer state equations and hysteresis parameters tuned up in an off-line way, the ES-EKF and ES-SPKF perform better than the algorithms considered in previous works. The algorithms are validated by experiments with real data obtained from lab tests.</div>

scholarly journals A variable-length window-wise parameter-dependent state of charge estimation by Kalman filters

2021 ◽  
Author(s):  
Bataa Lkhagvasuren ◽  
Minkyu Kwak ◽  
Hong Sung Jin ◽  
Gyuwon Seo ◽  
Sungyool Bong ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Kalman Filters ◽  
Real Data ◽  
Estimation Algorithm ◽  
Open Circuit ◽  
State Of Charge ◽  
Least Square ◽  
State Equations ◽  
Sigma Point ◽  
Parameter Dependent

<div>This paper proposes a new window-wise state of charge (SOC) estimation algorithm based on Kalman filters (KF). In the first stage, the equivalent circuit model's parameters are estimated by a least square estimation window-wise, assuming a linear SOC and open-circuit voltage (OCV) relation. The algorithm accurately estimates the parameters and observes the changes that depend on SOC. Moreover, based on the estimated parameters, the OCV values are identified. In the next stage, window-wise linear Kalman filter(ES-LKF) without hysteresis and extended Kalman filter (ES-EKF) and sigma-point Kalman filter (ES-SPKF) algorithm with hysteresis are executed to estimate SOC. Having fewer state equations and hysteresis parameters tuned up in an off-line way, the ES-EKF and ES-SPKF perform better than the algorithms considered in previous works. The algorithms are validated by experiments with real data obtained from lab tests.</div>

SoC Evaluation of Power Battery Remaining Capacity Based on Battery Model and Extended Kalman Filter

2014 ◽  
Vol 971-973 ◽  
pp. 1152-1155
Author(s):  
Min Wu ◽  
Hai Pu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Management System ◽  
New Method ◽  
Battery Management System ◽  
Battery Management ◽  
Traditional Methods ◽  
Battery Model ◽  
Power Battery ◽  
Remaining Capacity

The estimate of the remaining capacity of power battery is one of the most important function of battery management system. Using traditional methods is difficult to estamate the remaining capacity, this paper put forward a new method which is based on the battery model and extended Kalman filter.Then carries on the concrete analysis and discussion.

scholarly journals Design of an Embedded Energy Management System for Li–Po Batteries Based on a DCC-EKF Approach for Use in Mobile Robots

Machines ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 313
Author(s):  
Arezki Abderrahim Chellal ◽  
José Gonçalves ◽  
José Lima ◽  
Vítor Pinto ◽  
Hicham Megnafi
Keyword(s):  
Embedded System ◽  
Mobile Robotics ◽  
Low Cost ◽  
Open Circuit ◽  
Battery Management ◽  
Computing Power ◽  
Estimation Algorithms ◽  
Soc Estimation ◽  
Battery Management Systems ◽  
Li Po

In mobile robotics, since no requirements have been defined regarding accuracy for Battery Management Systems (BMS), standard approaches such as Open Circuit Voltage (OCV) and Coulomb Counting (CC) are usually applied, mostly due to the fact that employing more complicated estimation algorithms requires higher computing power; thus, the most advanced BMS algorithms reported in the literature are developed and verified by laboratory experiments using PC-based software. The objective of this paper is to describe the design of an autonomous and versatile embedded system based on an 8-bit microcontroller, where a Dual Coulomb Counting Extended Kalman Filter (DCC-EKF) algorithm for State of Charge (SOC) estimation is implemented; the developed prototype meets most of the constraints for BMSs reported in the literature, with an energy efficiency of 94% and an error of SOC accuracy that varies between 2% and 8% based on low-cost components.

scholarly journals State of Charge Estimation of a Composite Lithium-Based Battery Model Based on an Improved Extended Kalman Filter Algorithm

Inventions ◽  
2019 ◽  
Vol 4 (4) ◽  
pp. 66 ◽  
Author(s):  
Ning Ding ◽  
Krishnamachar Prasad ◽  
Tek Tjing Lie ◽  
Jinhui Cui
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Open Circuit ◽  
State Of Charge ◽  
Battery Management System ◽  
Battery Management ◽  
Soc Estimation ◽  
Battery Model ◽  
Operation Conditions ◽  
Battery State Of Charge

The battery State of Charge (SoC) estimation is one of the basic and significant functions for Battery Management System (BMS) in Electric Vehicles (EVs). The SoC is the key to interoperability of various modules and cannot be measured directly. An improved Extended Kalman Filter (iEKF) algorithm based on a composite battery model is proposed in this paper. The approach of the iEKF combines the open-circuit voltage (OCV) method, coulomb counting (Ah) method and EKF algorithm. The mathematical model of the iEKF is built and four groups of experiments are conducted based on LiFePO4 battery for offline parameter identification of the model. The iEKF is verified by real battery data. The simulation results with the proposed iEKF algorithm under both static and dynamic operation conditions show a considerable accuracy of SoC estimation.

scholarly journals A New Cascaded Framework for Lithium-Ion Battery State and Parameter Estimation

2020 ◽  
Vol 10 (3) ◽  
pp. 1009 ◽  
Author(s):  
Jianwen Meng ◽  
Moussa Boukhnifer ◽  
Demba Diallo ◽  
Tianzhen Wang
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Lithium Ion Battery ◽  
Equivalent Circuit ◽  
Open Circuit Voltage ◽  
Lithium Ion ◽  
Open Circuit ◽  
The State ◽  
Estimation Algorithms ◽  
Battery Capacity

Lithium-ion battery on-line monitoring is challenging due to the unmeasurable characteristic of its internal states. Up to now, the most effective approach for battery monitoring is to apply advanced estimation algorithms based on equivalent circuit models. Besides, a usual method for estimating slowly varying unmeasurable parameters is to include them in the state vector with the zero-time derivative condition, which constitutes the so-called extended equivalent circuit model and has been widely used for the battery state and parameter estimation. Although various advanced estimation algorithms are applied to the joint estimation and dual estimation frameworks, the essence of these estimation frameworks has not been changed. Thus, the improvement of the battery monitoring result is limited. Therefore, a new battery monitoring structure is proposed in this paper. Firstly, thanks to the superposition principle, two sub-models are extracted. For the nonlinear one, an observability analysis is conducted. It shows that the necessary conditions for local observability depend on the battery current, the initial value of the battery capacity, and the square of the derivative of the open circuit voltage with respect to the state of charge. Then, the obtained observability analysis result becomes an important theoretical support to propose a new monitoring structure. Commonly used estimation algorithms, namely the Kalman filter, extended Kalman filter, and unscented Kalman filter, are selected and employed for it. Apart from providing a simultaneous estimation of battery open circuit voltage, more rapid and less fluctuating battery capacity estimation are the main advantages of the new proposed monitoring structure. Numerical studies using synthetic data have proven the effectiveness of the proposed framework.

Simulation Study on Battery State of Charge Estimation Using Kalman Filter

2016 ◽  
Vol 20 (6) ◽  
pp. 861-866 ◽  
Author(s):  
Furqan Asghar ◽  
◽  
Muhammad Talha ◽  
Sung Ho Kim ◽  
In-Ho Ra ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Estimation Method ◽  
Open Circuit ◽  
The State ◽  
Operating Conditions ◽  
State Of Charge ◽  
Estimation Algorithms ◽  
Battery State Of Charge ◽  
And Performance ◽  
State Of Charge Estimation

Low power dissipation and maximum battery run-time are crucial in portable electronics and EV’s. Battery characteristics and performance varied at different operating conditions. By using accurate, efficient circuit and battery models, designers can predict and optimize battery runtime, current state of charge (SOC) and circuit performance. A great factor in determining the stability of battery system lies within the state of charge estimation. Failing to predict SOC will cause overcharge or over discharge which potentially will bring permanent damage to the battery cells. Open circuit voltage (OCV) has been widely used to estimate the state of charge in estimation algorithms. This paper proposed an accurate and comprehensive battery state of charge (SOC) estimation method by using the Kalman filter. First, Kalman filter for Li-ion battery state of charge estimation was mathematically designed. Then Electrical battery model is being implemented with Kalman filter in matlab Simulink to estimate the exact battery state of charge using estimated battery open circuit voltages. The proposed model shows that system is estimating battery state of charge more accurately than commonly used methods which can help to improve battery performance and lifetime.

Sign in / Sign up

Export Citation Format

Share Document