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.

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.

Dispatch Analysis of Off-Grid Diesel Generator-Battery Power Systems

2014 ◽  
Vol 15 (2) ◽  
pp. 161-170 ◽  
Author(s):  
D. Hsu ◽  
L. Kang
Keyword(s):  
Power Systems ◽  
Power System ◽  
Fuel Consumption ◽  
Rural Areas ◽  
State Of Charge ◽  
Battery Life ◽  
Diesel Generator ◽  
Running Time ◽  
Battery Power ◽  
Battery Bank

Abstract Diesel generator (DG)–battery power systems are often adopted by telecom operators especially in semi-urban and rural areas of developing countries. System dispatch is one of the key factors to hybrid power system integration. The contradiction between battery dispatch and DG dispatch in DG–battery power systems is that shallow and medium cycling is preferred for long battery life, while deep cycling is preferred for DG fuel and maintenance saving. In this paper, two dispatch regimes, A of full cycle charge strategy and B of partial state of charge (PSOC) strategy, and the corresponding SOC (state of charge) set points of the DG–battery power system are analysed and compared in terms of system operational expenditure (OPEX) and net present cost (NPC). The system OPEX mainly consists of fuel-related, filters-related and battery bank replacement costs. The simulation programme is established based on system efficiency calculations and battery charging regimes. The results show that (1) shallow cycling may bring long DG running time and high fuel consumption, while deep cycling is in favour of reducing DG running time and fuel consumption; (2) shallow cycling is in favour of battery life under Regime A, while deep cycling is in favour of battery life under Regime B; (3) depth of discharge (DOD)$$ \in $$[0.8, 1.0] leads to the lowest NPC for both Regime A and Regime B; (4) Regime B wins with not large difference before the battery bank replacement happened, and after then Regime A wins.

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.

scholarly journals Parameter Identification and State-of-Charge Estimation for Lithium-Ion Batteries Using Separated Time Scales and Extended Kalman Filter

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1054
Author(s):  
Kuo Yang ◽  
Yugui Tang ◽  
Zhen Zhang
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Open Circuit Voltage ◽  
Lithium Ion ◽  
Open Circuit ◽  
State Of Charge ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Soc Estimation ◽  
Battery Model

With the development of new energy vehicle technology, battery management systems used to monitor the state of the battery have been widely researched. The accuracy of the battery status assessment to a great extent depends on the accuracy of the battery model parameters. This paper proposes an improved method for parameter identification and state-of-charge (SOC) estimation for lithium-ion batteries. Using a two-order equivalent circuit model, the battery model is divided into two parts based on fast dynamics and slow dynamics. The recursive least squares method is used to identify parameters of the battery, and then the SOC and the open-circuit voltage of the model is estimated with the extended Kalman filter. The two-module voltages are calculated using estimated open circuit voltage and initial parameters, and model parameters are constantly updated during iteration. The proposed method can be used to estimate the parameters and the SOC in real time, which does not need to know the state of SOC and the value of open circuit voltage in advance. The method is tested using data from dynamic stress tests, the root means squared error of the accuracy of the prediction model is about 0.01 V, and the average SOC estimation error is 0.0139. Results indicate that the method has higher accuracy in offline parameter identification and online state estimation than traditional recursive least squares methods.

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 Current Trends for State-of-Charge (SoC) Estimation in Lithium-Ion Battery Electric Vehicles

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3284
Author(s):  
Ingvild B. Espedal ◽  
Asanthi Jinasena ◽  
Odne S. Burheim ◽  
Jacob J. Lamb
Keyword(s):  
Electric Vehicles ◽  
Lithium Ion ◽  
Research Area ◽  
State Of Charge ◽  
Stable Operation ◽  
Battery Management ◽  
Soc Estimation ◽  
Current Trends ◽  
Battery Management Systems ◽  
Active Research

Energy storage systems (ESSs) are critically important for the future of electric vehicles. Despite this, the safety and management of ESSs require improvement. Battery management systems (BMSs) are vital components in ESS systems for Lithium-ion batteries (LIBs). One parameter that is included in the BMS is the state-of-charge (SoC) of the battery. SoC has become an active research area in recent years for battery electric vehicle (BEV) LIBs, yet there are some challenges: the LIB configuration is nonlinear, making it hard to model correctly; it is difficult to assess internal environments of a LIB (and this can be different in laboratory conditions compared to real-world conditions); and these discrepancies can lead to raising the instability of the LIB. Therefore, further advancement is required in order to have higher accuracy in SoC estimation in BEV LIBs. SoC estimation is a key BMS feature, and precise modeling and state estimation will improve stable operation. This review discusses current methods use in BEV LIB SoC modelling and estimation. The review culminates in a brief discussion of challenges in BEV LIB SoC prediction analysis.

scholarly journals A Fuzzy State-of-Charge Estimation Algorithm Combining Ampere-Hour and an Extended Kalman Filter for Li-Ion Batteries Based on Multi-Model Global Identification

2018 ◽  
Vol 8 (11) ◽  
pp. 2028 ◽  
Author(s):  
Xin Lai ◽  
Dongdong Qiao ◽  
Yuejiu Zheng ◽  
Long Zhou
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Estimation Algorithm ◽  
State Of Charge ◽  
Distribution Area ◽  
Model Parameters ◽  
Model Accuracy ◽  
Soc Estimation ◽  
Fuzzy Fusion ◽  
Fuzzy State

The popular and widely reported lithium-ion battery model is the equivalent circuit model (ECM). The suitable ECM structure and matched model parameters are equally important for the state-of-charge (SOC) estimation algorithm. This paper focuses on high-accuracy models and the estimation algorithm with high robustness and accuracy in practical application. Firstly, five ECMs and five parameter identification approaches are compared under the New European Driving Cycle (NEDC) working condition in the whole SOC area, and the most appropriate model structure and its parameters are determined to improve model accuracy. Based on this, a multi-model and multi-algorithm (MM-MA) method, considering the SOC distribution area, is proposed. The experimental results show that this method can effectively improve the model accuracy. Secondly, a fuzzy fusion SOC estimation algorithm, based on the extended Kalman filter (EKF) and ampere-hour counting (AH) method, is proposed. The fuzzy fusion algorithm takes advantage of the advantages of EKF, and AH avoids the weaknesses. Six case studies show that the SOC estimation result can hold the satisfactory accuracy even when large sensor and model errors exist.

scholarly journals An All-Region State-of-Charge Estimator Based on Global Particle Swarm Optimization and Improved Extended Kalman Filter for Lithium-Ion Batteries

Electronics ◽  
2018 ◽  
Vol 7 (11) ◽  
pp. 321 ◽  
Author(s):  
Xin Lai ◽  
Wei Yi ◽  
Yuejiu Zheng ◽  
Long Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Kalman Filter ◽  
Lithium Ion Batteries ◽  
Extended Kalman Filter ◽  
Particle Swarm ◽  
Lithium Ion ◽  
State Of Charge ◽  
Model Parameters ◽  
Swarm Optimization ◽  
Soc Estimation

In this paper, a novel model parameter identification method and a state-of-charge (SOC) estimator for lithium-ion batteries (LIBs) are proposed to improve the global accuracy of SOC estimation in the all SOC range (0–100%). Firstly, a subregion optimization method based on particle swarm optimization is developed to find the optimal model parameters of LIBs in each subregion, and the optimal number of subregions is investigated from the perspective of accuracy and computation time. Then, to solve the problem of a low accuracy of SOC estimation caused by large model error in the low SOC range, an improved extended Kalman filter (IEKF) algorithm with variable noise covariance is proposed. Finally, the effectiveness of the proposed methods are verified by experiments on two kinds of batteries under three working cycles, and case studies show that the proposed IEKF has better accuracy and robustness than the traditional extended Kalman filter (EKF) in the all SOC range.

scholarly journals Ethno-Religious Identification and Social Distance Between Muslims and Christians: Analysis on Social Identity Theory

2018 ◽  
Vol 6 (1) ◽  
pp. 53-70
Author(s):  
Cahyo Pamungkas
Keyword(s):  
Social Identity Theory ◽  
Religious Identity ◽  
Social Distance ◽  
Identity Theory ◽  
Negative Attitudes ◽  
Daily Lives ◽  
Religious Identification ◽  
Qualitative Approaches ◽  
Positive Attitudes ◽  
The Individual

This article aims to investigate the relationship between ethno-religious identity and the social distancebetween Muslims and Christians in Ambon and Yogyakarta, taking into account factors at the individual level.Also, this research is addressed to fll a gap in the literature between studies that emphasize economic andpolitical competition as the main sources of con?ict, and studies that focus on prejudice and discriminationas causes of con?ict. The central question is: to what extent is ethno-religious identifcation present amongMuslims and Christians in Ambon and Yogyakarta and observable in their daily lives? This research usessocial identity theory that attempts to question why people like their in-group, and dislike out-groups. Thetheory says that individuals struggle for positive in-group distinctiveness, and have positive attitudes towardtheir in-group and negative attitudes towards out-groups. This research uses both quantitative and qualitative approaches. A survey was conducted with 1500 university students from six universities in Ambon andYogyakarta. By using quantitative and qualitative methods of analysis, this study came up with several fndings. Firstly, the study found high levels of religious identifcation among Muslim and Christian respondents,demonstrated by their participation in religious practices, which we defne as frequency of praying, attendingreligious services, and reading the Holy Scriptures. Secondly, social distance consists of contact avoidance,avoidance of future spouses from another religion, and the support for residential segregation. Di?erencesfrom the mean show that Muslim respondents tend to display higher contact avoidance and support forresidential segregation compared to Christian respondents. Thirdly, analysis of variance demonstrates thatelements of ethno-religious identity are related signifcantly to elements of social distance.

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