scholarly journals Lithium-Ion Battery Parameters and State of Charge Joint Estimation Using Bias Compensation Least Squares and the Alternate Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
Wei Xiong ◽  
Yimin Mo ◽  
Cong Yan
Keyword(s):  
Least Squares ◽  
Computational Efficiency ◽  
High Accuracy ◽  
State Of Charge ◽  
Joint Estimation ◽  
Model Parameters ◽  
Battery Management System ◽  
Battery Management ◽  
Soc Estimation ◽  
Bias Compensation

For safe and efficient operation of electric vehicles (EVs), battery management system is essential. Nevertheless, a challenge lying in battery management systems is how to obtain an algorithm for state of charge (SOC) estimation that has both high accuracy and low computational cost. For this purpose, the battery parameters and SOC joint estimation algorithm based on bias compensation least squares and alternate (BCLS-ALT) algorithm are proposed in this paper. The battery model parameters are identified online using the bias compensation least squares (BCLS), while the SOC is estimated applying the alternate (ALT) algorithm, which can switch the computational logic between H-infinity filter (HIF) and ampere-hour integral (AHI) to improve the computational efficiency and accuracy. The experimental results show that the accuracy of the SOC estimated by the BCLS-ALT algorithm is the highest, and the computational efficiency is also high, with the switching threshold SOCALT being set to 25%. Despite the 20% initial error and the 10% current drift, the proposed BCLS-ALT algorithm can obtain high accuracy and robustness of SOC estimation under different ambient temperatures and dynamic load profiles.

scholarly journals Online State of Charge Estimation for Lithium-Ion Battery by Combining Incremental Autoregressive and Moving Average Modeling with Adaptive H-Infinity Filter

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Zheng Liu ◽  
Xuanju Dang ◽  
Hanxu Sun
Keyword(s):  
Moving Average ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
State Of Charge ◽  
Parameters Identification ◽  
Estimation Methods ◽  
Model Parameters ◽  
Battery Management System ◽  
H Infinity ◽  
Soc Estimation

The state of charge (SOC) estimation is one of the most important features in battery management system (BMS) for electric vehicles (EVs). In this article, a novel equivalent-circuit model (ECM) with an extra noise sequence is proposed to reduce the adverse effect of model error. Model parameters identification method with variable forgetting factor recursive extended least squares (VFFRELS), which combines a constructed incremental autoregressive and moving average (IARMA) model with differential measurement variables, is presented to obtain the ECM parameters. The independent open circuit voltage (OCV) estimator with error compensation factors is designed to reduce the OCV error of OCV fitting model. Based on the IARMA battery model analysis and the parameters identification, an SOC estimator by adaptive H-infinity filter (AHIF) is formulated. The adaptive strategy of the AHIF improves the numerical stability and robust performance by synchronous adjusting noise covariance and restricted factor. The results of experiment and simulation have verified that the proposed approach has superior advantage of parameters identification and SOC estimation to other estimation methods.

scholarly journals Active Charge Balancing Strategy Using the State of Charge Estimation Technique for a PV-Battery Hybrid System

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3434
Author(s):  
Md Ohirul Qays ◽  
Yonis Buswig ◽  
Md Liton Hossain ◽  
Ahmed Abu-Siada
Keyword(s):  
Hybrid System ◽  
Storage System ◽  
Activation Function ◽  
Integrated System ◽  
State Of Charge ◽  
Maintenance Cost ◽  
Battery Management System ◽  
Battery Management ◽  
Charge Condition ◽  
Soc Estimation

Charging a group of series-connected batteries of a PV-battery hybrid system exhibits an imbalance issue. Such imbalance has severe consequences on the battery activation function and the maintenance cost of the entire system. Therefore, this paper proposes an active battery balancing technique for a PV-battery integrated system to improve its performance and lifespan. Battery state of charge (SOC) estimation based on the backpropagation neural network (BPNN) technique is utilized to check the charge condition of the storage system. The developed battery management system (BMS) receives the SOC estimation of the individual batteries and issues control signal to the DC/DC Buck-boost converter to balance the charge status of the connected group of batteries. Simulation and experimental results using MATLAB-ATMega2560 interfacing system reveal the effectiveness of the proposed approach.

Online State-of-Charge Estimation of Li-Ion Battery Based on the Second-Order RC Model

2013 ◽  
Vol 805-806 ◽  
pp. 1659-1663 ◽  
Author(s):  
Ze Cheng ◽  
Qiu Yan Zhang ◽  
Yu Hui Zhang
Keyword(s):  
Estimation Method ◽  
Equivalent Circuit Model ◽  
Second Order ◽  
State Of Charge ◽  
Battery Management System ◽  
Battery Management ◽  
Li Ion Battery ◽  
Soc Estimation ◽  
Counting Approach ◽  
Li Ion

The real-timely estimation of the SOC (state of charge) is the key technology in Li-ion battery management system. In this paper, to overcome the error of the SOC estimation of Extended Kalman filter (EKF), a new estimation method based on modified-strong tracking filter (MSTF) is applied to SOC estimation of Li-ion battery, based on the second-order RC equivalent circuit model. Experiments are made to compare the new filter with the EKF and Coulomb counting approach (Ah). The simulation results demonstrate that the new filter algorithm MSTF used in this paper has higher filtering accuracy under the same conditions.

Comprehensive Battery Equivalent Circuit Based Model for Battery Management Application

2013 ◽  
Author(s):  
Shijie Tong ◽  
Matthew P. Klein ◽  
Jae Wan Park
Keyword(s):  
Equivalent Circuit ◽  
Life Time ◽  
State Of Charge ◽  
Model Parameters ◽  
Battery Management System ◽  
Battery Management ◽  
Computationally Efficient ◽  
Battery Model ◽  
Battery State Of Charge ◽  
Dependent Model

This paper presents a comprehensive control oriented battery model. Described first is an equivalent circuit based battery model which captures particular battery characteristics of control interest. Then, the model categorizes the battery dynamics based on their different time constants (transient, long-term, life-time). This model uses a 2-D map representing the temperature and state-of-charge dependent model parameters. Also, the model uses new battery state-of-charge and state-of-health definitions that are more practical for a real battery management system. Battery testing and simulation on various types of batteries and use scenarios was completed to validate that the model is easy to parameterize, computationally efficient and of adequate accuracy.

scholarly journals A robust state of charge estimation for multiple models of lead acid battery using adaptive extended Kalman filter

2020 ◽  
Vol 9 (1) ◽  
pp. 1-11
Author(s):  
Maamar Souaihia ◽  
Bachir Belmadani ◽  
Rachid Taleb
Keyword(s):  
Kalman Filter ◽  
Management System ◽  
State Of Charge ◽  
Least Square ◽  
Accurate Estimation ◽  
Battery Management System ◽  
Battery Management ◽  
Lead Acid Battery ◽  
Soc Estimation ◽  
Lead Acid

An accurate estimation technique of the state of charge (SOC) of batteries is an essential task of the battery management system. The adaptive Kalman filter (AEKF) has been used as an obsever to investigate the SOC estimation effectiveness. Therefore, The SOC is a reflexion of the chemistry of the cell which it is the key parameter for the battery management system. It is very complex to monitor the SOC and control the internal states of the cell. Three battery models are proposed and their state space models have been established, their parameters were identified by applying the least square method. However, the SOC estimation accuracy of the battery depends on the model and the efficiency of the algorithm. In this paper, AEKF technique is presented to estimate the SOC of Lead acid battery. The experimental data is used to identify the parameters of the three models and used to build different open circuit voltage–state of charge (OCV-SOC) functions relationship. The results shows that the SOC estimation based-model which has been built by hight order RC model can effectively limit the error, hence guaranty the accuracy and robustness.

scholarly journals Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

Energies ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 446 ◽  
Author(s):  
Muhammad Umair Ali ◽  
Amad Zafar ◽  
Sarvar Hussain Nengroo ◽  
Sadam Hussain ◽  
Muhammad Junaid Alvi ◽  
...  
Keyword(s):  
Electric Vehicle ◽  
Management System ◽  
Lithium Ion ◽  
State Of Charge ◽  
Estimation Methods ◽  
Battery Management System ◽  
Battery Management ◽  
Li Ion Battery ◽  
Soc Estimation ◽  
Li Ion

Energy storage system (ESS) technology is still the logjam for the electric vehicle (EV) industry. Lithium-ion (Li-ion) batteries have attracted considerable attention in the EV industry owing to their high energy density, lifespan, nominal voltage, power density, and cost. In EVs, a smart battery management system (BMS) is one of the essential components; it not only measures the states of battery accurately, but also ensures safe operation and prolongs the battery life. The accurate estimation of the state of charge (SOC) of a Li-ion battery is a very challenging task because the Li-ion battery is a highly time variant, non-linear, and complex electrochemical system. This paper explains the workings of a Li-ion battery, provides the main features of a smart BMS, and comprehensively reviews its SOC estimation methods. These SOC estimation methods have been classified into four main categories depending on their nature. A critical explanation, including their merits, limitations, and their estimation errors from other studies, is provided. Some recommendations depending on the development of technology are suggested to improve the online estimation.

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 Effect of Sensors Sensitivity on Lithium-Ion Battery Modeled Parameters and State of Charge: A Comparative Study

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 709 ◽  
Author(s):  
Muhammad Umair Ali ◽  
Amad Zafar ◽  
Sarvar Hussain Nengroo ◽  
Sadam Hussain ◽  
Hee-Je Kim
Keyword(s):  
Lithium Ion ◽  
State Of Charge ◽  
Current Sensor ◽  
Accurate Estimation ◽  
Battery Management System ◽  
Battery Management ◽  
Soc Estimation ◽  
Optimal Value ◽  
Current Transducer ◽  
Research Outcome

The accurate estimation of the state of charge (SOC) is usually acknowledged as one of the essential features in designing of battery management system (BMS) for the lithium-ion batteries (LIBs) in electric vehicles (EVs). A suitable battery model is a prerequisite for correct SOC measurement. In this work, the first and second order RC autoregressive exogenous (ARX) battery models are adopted to check the influence of voltage and current transducer measurement uncertainty. The Lagrange multiplier method is used to estimate the battery parameters. The sensitivity analysis is performed under the following conditions: Current sensor precision of ±5 mA, ±50 mA, ±100 mA, and ±500 mA and voltage sensor precision of ±1 mV, ±2.5 mV, ±5 mV, and ±10mV. The comparative analysis of both models under the perturbed environment has been carried out. The effects of the sensor’s sensitivity on the different battery structures and complexity are also analyzed. Results shows that the voltage and current sensor sensitivity has a significant influence on SOC estimation. This research outcome assists the researcher in selecting the optimal value of sensor accuracy to accurately estimate the SOC of the LIB.

State of charge estimation for lithium-ion batteries using dynamic neural network based on sine cosine algorithm

2021 ◽  
pp. 095440702110180
Author(s):  
Meng Wei ◽  
Min Ye ◽  
Jia Bo Li ◽  
Qiao Wang ◽  
Xin Xin Xu
Keyword(s):  
Neural Network ◽  
Lithium Ion Batteries ◽  
Unscented Kalman Filter ◽  
Lithium Ion ◽  
State Of Charge ◽  
Support Vector ◽  
Battery Management System ◽  
Dynamic Neural Network ◽  
Soc Estimation ◽  
Sine Cosine Algorithm

State of charge (SOC) of the lithium-ion batteries is one of the key parameters of the battery management system, which the performance of SOC estimation guarantees energy management efficiency and endurance mileage of electric vehicles. However, accurate SOC estimation is a difficult problem owing to complex chemical reactions and nonlinear battery characteristics. In this paper, the method of the dynamic neural network is used to estimate the SOC of the lithium-ion batteries, which is improved based on the classic close-loop nonlinear auto-regressive models with exogenous input neural network (NARXNN) model, and the open-loop NARXNN model considering expected output is proposed. Since the input delay, feedback delay, and hidden layer of the dynamic neural network are usually selected by empirically, which affects the estimation performance of the dynamic neural network. To cover this weakness, sine cosine algorithm (SCA) is used for global optimal dynamic neural network parameters. Then, the experimental results are verified to obtain the effectiveness and robustness of the proposed method under different conditions. Finally, the dynamic neural network based on SCA is compared with unscented Kalman filter (UKF), back propagation neural network based on particle swarm optimization (BPNN-PSO), least-squares support vector machine (LS-SVM), and Gaussian process regression (GPR), the results show that the proposed dynamic neural network based on SCA is superior to other methods.

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