Dynamic data-driven and model-based recursive analysis for estimation of battery state-of-charge

Li, Yue; Chattopadhyay, Pabitra; Xiong, Sihan; Ray, Asok; Rahn, Christopher D.

doi:10.1016/j.apenergy.2016.10.025

Cited by 29 publications

(16 citation statements)

References 25 publications

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“…Thanks for the development of information technology, a large amount of high-quality data covering various working conditions can be obtained, which makes it possible to develop data-driven SOC estimation methods. 151 Chen et al 152 designed a feedforward neural network (NN) model to achieve high accurate SOC estimation. Experimental results demonstrate that the method can converge at erroneous initial SOC value and can reach an estimation error within 2%.…”

Section: Soc-based Equalization Strategy (Sbes)mentioning

confidence: 99%

A comprehensive review on inconsistency and equalization technology of lithium‐ion battery for electric vehicles

et al. 2020

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The rapid growth of transportation demand has been enlarged strongly which has promoted electric vehicles powered by lithium-ion batteries. However, the inconsistencies within the battery pack will deteriorate over the lifecycle and affect the performance of electric vehicles. Therefore, various thermal management systems and equalization systems have been applied in battery management system to deal with the inconsistencies, extend battery service life, and improve safety performance. This review summarizes the origination of inconsistency within lithium-ion batteries from production to usage process, and then introduces the classification methods and application scenarios of the balance management system in detail. Based on the circuit topology, equalization systems can be classified into passive and active topologies. Active topologies

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Section: Soc-based Equalization Strategy (Sbes)mentioning

confidence: 99%

A comprehensive review on inconsistency and equalization technology of lithium‐ion battery for electric vehicles

et al. 2020

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“…Therefore, the transition relation between parameter k and POECPK can be deduced by combining Eqs. (5) and (10) as follows:…”

Section: Model Framementioning

confidence: 99%

“…The methods focus on determining driving range through obtaining the estimation of residual usable energy of BEVs. There are several studies using battery SOC methods to realize range estimation [5][6][7][8][9][10][11][12][13][14][15]. This factor, while important, is insufficient for driving range or distance estimation.…”

Section: Introductionmentioning

confidence: 99%

A data-based model for driving distance estimation of battery electric logistics vehicles

Wang

Zhang

2018

J Wireless Com Network

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Battery electric logistics vehicles (BELVs) reduce transportation costs and air pollution unlike conventional logistics vehicles. However, the limited driving range of BELVs creates new problems for logistics transport. Accurate driving distance estimation of BELVs can help logistics companies determine transport strategies and alleviate the range anxiety of drivers. Based on mass data from BELVs operating in Beijing, China, this study uses a practical and effective data-based modeling method, regression analysis, to establish the data-based model of driving distance estimation. During the modeling process, a nonlinear relation between percentage of energy consumption per kilometer and driving speed is explored based on the experimental data. After determining the model variables, the model frame of driving distance in consideration of driving speed and state of charge is established. The forgetting factor recursive least-squares algorithm is applied to estimate the parameter values of the model. Verification results confirm the feasibility of the model and show that the model errors are small. The proposed model is also used to explore the economical driving speed of BELVs.

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“…As stated previously, the techniques employed in ECM, suffer from inaccuracy owing to the lack of thorough understanding of the electrochemical dynamics and physics of the battery [21]. This drawback could be lessened via data-driven models, utilizing the information of the measurement ensemble.…”

Section: Introductionmentioning

confidence: 99%

Battery Aging Prediction Using Input-Time-Delayed Based on an Adaptive Neuro-Fuzzy Inference System and a Group Method of Data Handling Techniques

et al. 2018

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In this article, two techniques that are congruous with the principle of control theory are utilized to estimate the state of health (SOH) of real-life plug-in hybrid electric vehicles (PHEVs) accurately, which is of vital importance to battery management systems. The relation between the battery terminal voltage curve properties and the battery state of health is modelled via an adaptive neuron-fuzzy inference system and a group method of data handling. The comparison of the results demonstrates the capability of the proposed techniques for accurate SOH estimation. Moreover, the estimated results are compared with the direct actual measured SOH indicators using standard tests. The results indicate that the adaptive neuron-fuzzy inference system with fifteen rules based on a SOH estimator has better performances over the other technique, with a 1.5% maximum error in comparison to the experimental data.

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Dynamic data-driven and model-based recursive analysis for estimation of battery state-of-charge

Cited by 29 publications

References 25 publications

A comprehensive review on inconsistency and equalization technology of lithium‐ion battery for electric vehicles

A comprehensive review on inconsistency and equalization technology of lithium‐ion battery for electric vehicles

A data-based model for driving distance estimation of battery electric logistics vehicles

Battery Aging Prediction Using Input-Time-Delayed Based on an Adaptive Neuro-Fuzzy Inference System and a Group Method of Data Handling Techniques

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