Equalization of series connected lithium‐ion batteries based on back propagation neural network and fuzzy logic control

Wang, Biao; Qin, Feifei; Zhao, Xuebing; Ni, Xianpo; Xuan, Dongji

doi:10.1002/er.5274

Cited by 35 publications

(14 citation statements)

References 34 publications

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“…Similar experiments can be performed with a Second-order RC equivalent circuit model and BPNN to reduce the battery inconsistency among lithium-ion battery packs. 96 However, Reference 97 conducted almost the exact opposite experiment, where an artificial neural network (ANN) was used to identify the battery parameters. Then the identified parameters were passed to the Thevenin equivalent circuit model to finally obtain an estimate of SOC using the OCV-SOC function, which also yields error results within the accepted range of the system.…”

Section: Hybrid Model-based Methodsmentioning

confidence: 99%

A comprehensive review on the state of charge estimation for lithium‐ion battery based on neural network

Cui

Wang

et al. 2021

Intl J of Energy Research

205

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Implementing carbon neutrality and emission peak policies requires a highlevel electric vehicle field. Lithium-ion batteries have been considered an essential component of electric vehicle power batteries. Effective state of charge (SOC) estimation for lithium-ion batteries is a critical problem that needs to be addressed at present. With the feature extraction and fitting capability, the neural network can achieve accurate SOC estimation without considering the internal electrochemical state of the battery. This article overviews the definition of SOC and the relationship with battery aging state. Then, by examining recent literature on estimating the SOC of Lithium-ion batteries using neural network methods, the methods are classified into three categories: feed-forward neural network method, deep learning method, and hybrid method. The progress of neural network methods in SOC estimation applications is systematically reviewed, including principles, advantages, disadvantages, current status, and estimation errors. Possible recommendations for next-generation intelligent battery management systems and SOC estimation are also presented. This review's highlighted insights will inspire researchers in the battery field and point the way to developing electric vehicles.

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Section: Hybrid Model-based Methodsmentioning

confidence: 99%

A comprehensive review on the state of charge estimation for lithium‐ion battery based on neural network

Cui

Wang

et al. 2021

Intl J of Energy Research

205

View full text Add to dashboard Cite

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“…e AFNN algorithm is combined with fuzzy logic control (FLC) and neural network to achieve high selfadaptability and good fault tolerance. e AFNN can adjust the membership function parameters as well as the weights between neurons [40]. In the paper, the AFNN is a firstorder Takagi-Sugeno (T-S) fuzzy neural network based on the hybrid algorithm based on BP and least squares.…”

Section: Equalization Control Algorithmmentioning

confidence: 99%

Active Equalization Strategy for Lithium-Ion Battery Packs Based on Multilayer Dual Interleaved Inductor Circuits in Electric Vehicles

Lei

Fan

et al. 2022

Journal of Advanced Transportation

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Lithium-ion batteries (LIBs) are widely used in electric vehicles (EVs) due to their superior power performance over other batteries. However, when connected in series, overcharged cells of LIBs face the risk of explosion, and undercharged cells decrease the life cycle of the battery. Eventually, the inconsistency phenomenon between cells resulting from manufacturing tolerance and usage process reduces the overall charging capacity of the battery and increases the risk of explosion after long-time use. Research has focused on synthesizing active material to achieve higher energy density and extended life cycle for LIBs while neglecting a comparative analysis of equalization technology on the performance of battery packs. In this paper, a nondissipative equalization structure is proposed to reconcile the inconsistency of series-connected LIB cells. In this structure, a circuit uses high-level equalization units to enable direct energy transfer between any two individual cells, and dual interleaved inductors in each equalization unit increase the equalization speed of a single cell in one equalization cycle by a factor of two. The circuit is compared with the classical inductor equalization circuit (CIEC), dual interleaved equalization circuit (DIEC), and parallel architecture equalization circuit (PAEC) in the states of standing, charging, and discharging, respectively, to validate the advantages of the proposed scheme. Considering the diversity of imbalance states, the state of charge (SOC) and terminal voltage are both chosen as the equalization criterion. The second-order RC model of the LIB and the adaptive unscented Kalman filter (AUKF) algorithm are employed for SOC estimation. For effective equalization, the adaptive fuzzy neural network (AFNN) is utilized to further reduce energy consumption and equalization time. The experiment results show that the AFNN algorithm reduces the total equalization time by approximately 37.4% and improves equalization efficiency by about 4.89% in contrast with the conventional mean-difference algorithm. Particularly, the experiment results of the equalization circuit verification certify that the proposed equalization structure can greatly accelerate the equalization progress and reduce the equalization loss compared to the other three equalization circuits.

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“…The voltage‐based methods offer least complexity in implementation and therefore are extensively used in e‐mobility systems 9 . The balancing control approaches include intelligent algorithms 10‐12 (e.g., neural network, fuzzy, graph theory, and genetic algorithm) to achieve balancing features such as accuracy, transition time, and stability. However, it increases the complexity significantly with the cell extension.…”

Section: Introductionmentioning

confidence: 99%

Adaptive passive balancing in battery management system for e‐mobility

Duraisamy

Deepa

2021

Int J Energy Res

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Summary Balancing the lithium‐ion battery pack is essential to enhance the energy usage and life cycle of the battery. This paper analyses passive cell balancing method of Li‐ion battery for e‐mobility application based on the energy loss and cost estimation through simulation and hardware implementation. As a part of the simulation, an electrical equivalent circuit battery model is developed and model parameters are obtained using electrochemical impedance spectroscopy test. The experimented passive balancing topologies include a switched shunting resistor circuit with appropriate control logic. Final voltage‐based balancing algorithm is implemented to balance the cells at high state of charge. Experimental results are compared against the theoretical investigation. Based on the outcome of this experiment, the most significant characteristics of the passive balancing system can be developed by considering the impact on the battery performance, energy loss, and cost.

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Equalization of series connected lithium‐ion batteries based on back propagation neural network and fuzzy logic control

Cited by 35 publications

References 34 publications

A comprehensive review on the state of charge estimation for lithium‐ion battery based on neural network

A comprehensive review on the state of charge estimation for lithium‐ion battery based on neural network

Active Equalization Strategy for Lithium-Ion Battery Packs Based on Multilayer Dual Interleaved Inductor Circuits in Electric Vehicles

Adaptive passive balancing in battery management system for e‐mobility

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