A detailed review on the parameters to be considered for an accurate estimation on the Plug-in Electric Vehicle's final State of Charge

2016 ◽  
Author(s):  
Claude Ziad El-Bayeh ◽  
Imad Mougharbel ◽  
Maarouf Saad ◽  
Ambrish Chandra ◽  
Serge Lefebvre ◽  
...  
Keyword(s):  
State Of Charge ◽  
Accurate Estimation ◽  
Final State ◽  
Detailed Review

scholarly journals Online State-of-Charge Estimation Based on the Gas–Liquid Dynamics Model for Li(NiMnCo)O2 Battery

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 324
Author(s):  
Haobin Jiang ◽  
Xijia Chen ◽  
Yifu Liu ◽  
Qian Zhao ◽  
Huanhuan Li ◽  
...  
Keyword(s):  
Stress Test ◽  
Robustness Analysis ◽  
Open Circuit ◽  
Combined Cycle ◽  
State Of Charge ◽  
Constant Current ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Battery Management System ◽  
Liquid Dynamics

Accurately estimating the online state-of-charge (SOC) of the battery is one of the crucial issues of the battery management system. In this paper, the gas–liquid dynamics (GLD) battery model with direct temperature input is selected to model Li(NiMnCo)O2 battery. The extended Kalman Filter (EKF) algorithm is elaborated to couple the offline model and online model to achieve the goal of quickly eliminating initial errors in the online SOC estimation. An implementation of the hybrid pulse power characterization test is performed to identify the offline parameters and determine the open-circuit voltage vs. SOC curve. Apart from the standard cycles including Constant Current cycle, Federal Urban Driving Schedule cycle, Urban Dynamometer Driving Schedule cycle and Dynamic Stress Test cycle, a combined cycle is constructed for experimental validation. Furthermore, the study of the effect of sampling time on estimation accuracy and the robustness analysis of the initial value are carried out. The results demonstrate that the proposed method realizes the accurate estimation of SOC with a maximum mean absolute error at 0.50% in five working conditions and shows strong robustness against the sparse sampling and input error.

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.

Introduction to The Hybrid Power Train Architecture Evolution

2013 ◽  
pp. 1-22
Keyword(s):  
Hybrid Electric Vehicle ◽  
Combustion Engine ◽  
Current Source ◽  
Point Of View ◽  
State Of Charge ◽  
Hybrid Drive ◽  
Final State ◽  
Power Train ◽  
Hybrid Power ◽  
Battery Energy

The hybrid power train is a complex system. It consists of mechanical and electrical components, and each of them is important. The evolution of the Hybrid Electric Vehicle (HEV) power trains is presented from the historical point of view. This chapter discusses the selected review of the hybrid power train’s architectural engineering. It includes the development of the hybrid vehicle power train’s construction from the simple series and parallel drives to the planetary gear hybrid power trains. The fuel consumption difference between the pure Internal Combustion Engine (ICE) drive and the hybrid drive is especially emphasized. Generally, there are two main hybrid drive types that are possible to define. Both these hybrid drive types are not mainly differentiated by their power train architecture. The first is the “full hybrid” drive, which is a power train equipped with a relatively low capacity battery that is not rechargeable from an external current source, and whose battery energy balance—its State-Of-Charge (SOC)—has to be obtained. The second one is the “plug in hybrid,” which means the necessity of recharging the battery by plugging into the grid when the final State-Of-Charge (SOC) of the battery is not acceptable. Additionally, the chapter focuses on the fuel cell series hybrid power train, which is only shown because its operation and design are beyond the scope of this book.

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 Evaluation of the State of Charge of a Solid/Liquid Phase Change Material in a Thermal Energy Storage Tank

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1425 ◽  
Author(s):  
Gabriel Zsembinszki ◽  
Christian Orozco ◽  
Jaume Gasia ◽  
Tilman Barz ◽  
Johann Emhofer ◽  
...  
Keyword(s):  
Energy Storage ◽  
Phase Change ◽  
Phase Change Material ◽  
Thermal Energy ◽  
Thermal Energy Storage ◽  
Residential Buildings ◽  
The State ◽  
State Of Charge ◽  
Accurate Estimation ◽  
Change Material

Monitoring of the state of charge of the thermal energy storage component in solar thermal systems for space heating and/or cooling in residential buildings is a key element from the overall system control strategy point of view. According to the literature, there is not a unique method for determining the state of charge of a thermal energy storage system that could generally be applied in any system. This contribution firstly provides a classification of the state-of-the-art of available techniques for the determination of the state of charge, and secondly, it presents an experimental analysis of different methods based on established sensor technologies, namely temperature, mass flow rates, and pressure measurements, tested using a lab-scale heat exchanger filled with a commercial phase change material for cooling applications. The results indicate that, depending on the expected accuracy and available instrumentation, each of the methods studied here can be used in the present application, the deviations between the methods generally being below 20%. This study concludes that a proper combination of two or more of these methods would be the ideal strategy to obtain a more reliable and accurate estimation of the state of charge of the latent heat thermal energy storage.

A Comparative Computational Study of Li, Na, and Mg Insertion in α-Sn

2014 ◽  
Vol 1678 ◽  
Author(s):  
Fleur Legrain ◽  
Oleksandr I. Malyi ◽  
Sergei Manzhos
Keyword(s):  
Density Functional ◽  
Room Temperature ◽  
Computational Study ◽  
State Of Charge ◽  
Final States ◽  
Final State ◽  
Functional Theory ◽  
Effects Of Temperature ◽  
Metal Insertion ◽  
And Diffusion

ABSTRACTWe present a comparative density functional theory study of Li, Na, and Mg storage energetics and diffusion in α-Sn, including the effects of temperature (vibrations). We study several concentrations corresponding to initial stages of insertion (number densities x= 1/64, 1/32, 1/16, and 1/8) as well as the final state of charge (Li17Sn4, Na15Sn4, and Mg2Sn). While final states of charge correspond to positive anode voltages for all three types of metal, insertion energetics is favorable for insertion for Li at all concentrations studied, for Na up to the concentration of x = 3/64, and Mg insertion is thermodynamically disfavored at all x. Diffusion barriers at dilute concentrations are computed to be 0.23, 0.51, and 0.44 eV for Li, Na, and Mg, respectively. Vibrations have a noticeable and temperature-, concentration-, and dopant-type dependent effect on voltages, of the order of 0.1 eV at room temperature.

The State of Charge Estimation of Lithium Battery in Electric Vehicle Based on Extended Kalman Filter

2014 ◽  
Vol 953-954 ◽  
pp. 796-799
Author(s):  
Huan Huan Sun ◽  
Jun Bi ◽  
Sai Shao
Keyword(s):  
Kalman Filter ◽  
Electric Vehicle ◽  
Extended Kalman Filter ◽  
Nonlinear Problems ◽  
Least Square Method ◽  
State Equation ◽  
State Of Charge ◽  
Least Square ◽  
Accurate Estimation ◽  
Battery State Of Charge

Accurate estimation of battery state of charge (SOC) is important to ensure operation of electric vehicle. Since a nonlinear feature exists in battery system and extended kalman filter algorithm performs well in solving nonlinear problems, the paper proposes an EKF-based method for estimating SOC. In order to obtain the accurate estimation of SOC, this paper is based on composite battery model that is a combination of three battery models. The parameters are identified using the least square method. Then a state equation and an output equation are identified. All experimental data are collected from operating EV in Beijing. The results of the experiment show  that the relative error of estimation of state of charge is reasonable, which proves this method has good estimation performance.

scholarly journals Research on the State of Charge of Lithium-Ion Battery Based on the Fractional Order Model

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6307
Author(s):  
Lin Su ◽  
Guangxu Zhou ◽  
Dairong Hu ◽  
Yuan Liu ◽  
Yunhai Zhu
Keyword(s):  
Kalman Filter ◽  
Fractional Order ◽  
Colored Noise ◽  
Unscented Kalman Filter ◽  
The State ◽  
State Of Charge ◽  
Accurate Estimation ◽  
Order Model ◽  
Discrete State ◽  
Fractional Order Model

Accurate estimation of the state of charge (SOC) of lithium batteries is paramount to ensuring consistent battery pack operation. To improve SOC estimation accuracy and suppress colored noise in the system, a fractional order model based on an unscented Kalman filter and an H-infinity filter (FOUHIF) estimation algorithm was proposed. Firstly, the discrete state equation of a lithium battery was derived, as per the theory of fractional calculus. Then, the HPPC experiment and the PSO algorithm were used to identify the internal parameters of the second order RC and fractional order models, respectively. As discovered during working tests, the parameters identified via the fractional order model proved to be more accurate. Furthermore, the feasibility of using the FOUHIF algorithm was evaluated under the conditions of NEDC and UDDS, with obvious colored noise. Compared with the fractional order unscented Kalman filter (FOUKF) and integer order unscented Kalman filter (UKF) algorithms, the FOUHIF algorithm showed significant improvement in both the accuracy and robustness of the estimation, with maximum errors of 1.86% and 1.61% under the two working conditions, and a terminal voltage prediction error of no more than 5.29 mV.

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).

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