scholarly journals The State of Charge Estimating Methods for Battery: A Review

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Wen-Yeau Chang
Keyword(s):  
Future Development ◽  
Control Strategies ◽  
Literature Survey ◽  
State Of Charge ◽  
Estimation Methods ◽  
Accurate Estimation ◽  
Battery Life ◽  
Mathematical Methods ◽  
Soc Estimation ◽  
Development Direction

An overview of new and current developments in state of charge (SOC) estimating methods for battery is given where the focus lies upon mathematical principles and practical implementations. As the battery SOC is an important parameter, which reflects the battery performance, so accurate estimation of SOC cannot only protect battery, prevent overcharge or discharge, and improve the battery life, but also let the application make rationally control strategies to achieve the purpose of saving energy. This paper gives a literature survey on the categories and mathematical methods of SOC estimation. Based on the assessment of SOC estimation methods, the future development direction of SOC estimation is proposed.

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 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.

A Novel Method for Estimating State of Charge of Lithium Ion Battery Packs

2010 ◽  
Vol 152-153 ◽  
pp. 428-435 ◽  
Author(s):  
Yuan Liao ◽  
Ju Hua Huang ◽  
Qun Zeng
Keyword(s):  
Lithium Ion Battery ◽  
Estimation Method ◽  
Lithium Ion ◽  
State Of Charge ◽  
Estimation Methods ◽  
Counting Method ◽  
Soc Estimation ◽  
Battery Packs ◽  
Novel Method ◽  
Road Data

In this paper a novel method for estimating state of charge (SOC) of lithium ion battery packs in battery electric vehicle (BEV), based on state of health (SOH) determination is presented. SOH provides information on aging of battery packs and it declines with repeated charging and discharging cycles of battery packs, so SOC estimation depends considerably on the value of SOH. Previously used SOC estimation methods are not satisfactory as they haven’t given enough attention to the decline of SOH. Therefore a novel SOC estimation method based on SOH determination is introduced in this paper; trying to compensate the deficiency for lack of attention to SOH. Real time road data are used to compare the performance of the conventionally often used Ah counting method which doesn’t give any consideration to SOH with the performance of the proposed SOC estimation method, and better results are obtained by the proposed method in comparison with the conventional method.

A Machine Learning Method for State of Charge Estimation in Lead-Acid Batteries for Heavy-Duty Vehicles

2021 ◽  
Author(s):  
Sara Luciani ◽  
Stefano Feraco ◽  
Angelo Bonfitto ◽  
Andrea Tonoli ◽  
Nicola Amati ◽  
...  
Keyword(s):  
Machine Learning ◽  
Estimation Error ◽  
Estimation Algorithm ◽  
State Of Charge ◽  
Accurate Estimation ◽  
Heavy Duty ◽  
Soc Estimation ◽  
Lead Acid Batteries ◽  
Heavy Duty Vehicles ◽  
Lead Acid

Abstract In the automotive framework, an accurate assessment of the State of Charge (SOC) in lead-acid batteries of heavy-duty vehicles is of major importance. SOC is a crucial battery state that is non-observable. Furthermore, an accurate estimation of the battery SOC can prevent system failures and battery damage due to a wrong usage of the battery itself. In this context, a technique based on machine learning for SOC estimation is presented in this study. Thus, this method could be used for safety and performance monitoring purposes in electric subsystem of heavy-duty vehicles. The proposed approach exploits a Genetic Algorithm (GA) in combination with Artificial Neural Networks (ANNs) for SOC estimation. Specifically, the training parameters of a Nonlinear Auto-Regressive with Exogenous inputs (NARX) ANN are chosen by the GA-based optimization. As a consequence of the GA-based optimization, the ANN-based SOC estimator architecture is defined. Then, the proposed SOC estimation algorithm is trained and validated with experimental datasets recorded during real driving missions performed by a heavy-duty vehicle. An equivalent circuit model representing the retained lead-acid battery is used to collect the training, validation and testing datasets that replicates the recorded experimental data related to electrical consumers and the cabin systems or during overnight stops in heavy-duty vehicles. This article illustrates the architecture of the proposed SOC estimation algorithm along with the identification procedure of the ANN parameters with GA. The method is able to estimate SOC with a low estimation error, being suitable for deployment on common on-board Battery Management Systems (BMS).

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 Compensation Method for Estimating the State of Charge of Li-Polymer Batteries Using Multiple Long Short-Term Memory Networks Based on the Extended Kalman Filter

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 349
Author(s):  
Donghoon Shin ◽  
Beomjin Yoon ◽  
Seungryeol Yoo
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Short Term Memory ◽  
Estimation Method ◽  
State Of Charge ◽  
Estimation Methods ◽  
Short Term ◽  
Term Memory ◽  
Soc Estimation ◽  
Long Short Term Memory

Many battery state of charge (SOC) estimation methods have been studied for decades; however, it is still difficult to precisely estimate SOC because it is nonlinear and affected by many factors, including the battery state and charge–discharge conditions. The extended Kalman filter (EKF) is generally used for SOC estimation, however its accuracy can decrease owing to the uncertain and inaccurate parameters of battery models and various factors with different time scales affecting the SOC. Herein, a SOC estimation method based on the EKF is proposed to obtain robust accuracy, in which the errors are compensated by a long short-term memory (LSTM) network. The proposed approach trains the errors of the EKF results, and the accurate SOC is estimated by applying calibration values corresponding to the condition of the battery and its load profiles with the help of LSTM. Furthermore, a multi-LSTM structure is implemented, and it adopts the ensemble average to guarantee estimation accuracy. SOC estimation with a root mean square error of less than 1% was found to be close to the actual SOC calculated by coulomb counting. Moreover, once the EKF model was established and the network trained, it was possible to predict the SOC online.

scholarly journals State of Charge Estimation Techniques for Lithium-ION Batteries Used in Electric Vehicle Applications

2021 ◽  
Vol 9 (8) ◽  
pp. 2750-2760
Author(s):  
Nikhil P
Keyword(s):  
Lithium Ion Battery ◽  
Electric Vehicles ◽  
Lithium Ion ◽  
High Energy ◽  
State Of Charge ◽  
High Energy Density ◽  
Estimation Methods ◽  
Estimation Techniques ◽  
Soc Estimation ◽  
Battery Packs

Abstract: Lithium-ion battery packs constitute an important part of Electric vehicles. The usage of Lithium-ion based chemistries as the source of energy has various advantages like high efficiency, high energy density, high specific energy, longevity among others. However, the management of lithium-ion battery packs require a Battery Management System (BMS). The BMS deals with functions like safety, prevention of abusive usage of battery pack, overcharging & over-discharging protection, cell balancing and others. One of the prominent features of the BMS is the estimation of State of charge (SOC). SOC is like a fuel gauge in automobile, it indicates how much more the battery can be used before charging it again. SOC is also required for other functions of BMS like State of Health (SOH) tracking, Range calculation, power & energy availability calculations. However, there is no means of measuring it directly (at least not on-board a vehicle) or estimating it easily. Various techniques should be used to estimate SOC indirectly. This paper starts from classical techniques that have existed since long time and reviews some of the modern & developing methods for SOC estimation. It contains a brief review about most of these SOC estimation methods, thus highlighting the methodology, advantages & disadvantages of each of these techniques. A brief review of other developing SOC estimation techniques is also provided. Keywords: State of Charge, SOC, Lithium-ion battery packs, Electric vehicles, Kalman Filter.

State of Charge Estimation for an Electric Wheelchair Using a Fuel Gauge Model

2016 ◽  
Author(s):  
Christopher Miller ◽  
Kelilah Wolkowicz ◽  
Jariullah Safi ◽  
Sean N. Brennan
Keyword(s):  
State Of Charge ◽  
Battery Life ◽  
Warning Systems ◽  
Linear Region ◽  
Daily Lives ◽  
Wheelchair Users ◽  
Electric Wheelchair ◽  
Soc Estimation ◽  
Battery Power ◽  
The Individual

Electric wheelchair users depend on a reliable power system in order to regain mobility in their daily lives. If a wheelchair’s battery power depletes without the user being aware, the individual may become stranded, further limiting their freedom of mobility and potentially placing the user in a harmful situation. This research seeks to develop a State-of-Charge (SOC) estimator for the batteries of an electric wheelchair. A second-order equivalent circuit battery model is developed and parameterized for a wheelchair’s lead-acid battery pack. To simplify the SOC estimation, this algorithm models a vehicle’s fuel gauge. A coulomb accumulator is incorporated to estimate energy usage in the non-linear region of the OCV-SOC curve, while a Kalman filter is used to estimate SOC in the linear region of the curve. The estimator is verified using experimentally collected data on-board a robotic wheelchair. The implementation of these algorithms with powered wheelchairs can significantly improve the estimation of wheelchair battery power and can ultimately be coupled with warning systems to alert users of depleting battery life, as well as enable low-power modes to increase wheelchair user safety.

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