Influence Analysis and Optimization of Sampling Frequency on the Accuracy of Model and State-of-Charge Estimation for LiNCM Battery

Gu, Pingwei; Zhou, Zhongkai; Qu, Shaofei; Zhang, Chenghui; Duan, Bin

doi:10.3390/en12071205

Cited by 11 publications

(8 citation statements)

References 29 publications

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“…As can be seen from the analysis in Section 4.1 and Figure 10, in the SoC interval of 0.45-0.75, the difference in the different fitting curves is not large, and the relationship between dQ/dV and SoH under different SoCs is quite similar, that is, the accuracy of the SoC estimation in this interval has relatively inconspicuous influence on SoH estimation, while in the SoC region at both ends there are a few obvious fluctuation points, that is, when the error of the SoC is amplified to 2%, the estimation accuracy of the SoH estimation method has begun to be affected by the SoC error in the region of the SoC from 0.3-0.45. Fortunately, all the commonly used SoC estimation methods limit the SoC error to 1% or less [21][22][23]. Therefore, in this paper, the selection of the SoC estimation methods has not been discussed.…”

Section: Influence Of Soc Estimation Error On Soh Estimation Resultsmentioning

confidence: 99%

A Fast Online State of Health Estimation Method for Lithium-Ion Batteries Based on Incremental Capacity Analysis

Kang

et al. 2019

Energies

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Efficient and accurate state of health (SoH) estimation is an important challenge for safe and efficient management of batteries. This paper proposes a fast and efficient online estimation method for lithium-ion batteries based on incremental capacity analysis (ICA), which can estimate SoH through the relationship between SoH and capacity differentiation over voltage (dQ/dV) at different states of charge (SoC). This method estimates SoH using arbitrary dQ/dV over a large range of charging processes, rather than just one or a limited number of incremental capacity peaks, and reduces the SoH estimation time greatly. Specifically, this method establishes a black box model based on fitting curves first, which has a smaller amount of calculation. Then, this paper analyzes the influence of different SoC ranges to obtain reasonable fitting curves. Additionally, the selection of a reasonable dV is taken into account to balance the efficiency and accuracy of the SoH estimation. Finally, experimental results validate the feasibility and accuracy of the method. The SoH estimation error is within 5% and the mean absolute error is 1.08%. The estimation time of this method is less than six minutes. Compared to traditional methods, this method is easier to obtain effective calculation samples and saves computation time.

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Section: Influence Of Soc Estimation Error On Soh Estimation Resultsmentioning

confidence: 99%

A Fast Online State of Health Estimation Method for Lithium-Ion Batteries Based on Incremental Capacity Analysis

Kang

et al. 2019

Energies

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“…The system's effectiveness was contrasted with the traditional coulomb counting approach. By pursuing the work described in [45], the current and voltage of each cell were recorded with 12-bit resolution conventional ADCs for the classical fixed-rate approach. It promised a proper SOC estimation while intended battery parameters were acquired at an appropriate rate [8,45], and F sI = 10 Hz was used [45].…”

Section: Resultsmentioning

confidence: 99%

“…By pursuing the work described in [45], the current and voltage of each cell were recorded with 12-bit resolution conventional ADCs for the classical fixed-rate approach. It promised a proper SOC estimation while intended battery parameters were acquired at an appropriate rate [8,45], and F sI = 10 Hz was used [45]. For the calibration purpose, the voltage was acquired at the rate of F sV = 16.66 MHz, meaning a sample was acquired every minute [16].…”

Section: Resultsmentioning

confidence: 99%

Event-Driven Coulomb Counting for Effective Online Approximation of Li-Ion Battery State of Charge

Qaisar

2020

Energies

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Lithium-ion batteries are deployed in a range of modern applications. Their utilization is evolving with the aim of achieving a greener environment. Batteries are costly, and battery management systems (BMSs) ensure long life and proper battery utilization. Modern BMSs are complex and cause a notable overhead consumption on batteries. In this paper, the time-varying aspect of battery parameters is used to reduce the power consumption overhead of BMSs. The aim is to use event-driven processing to realize effective BMSs. Unlike the conventional approach, parameters of battery cells, such as voltages and currents, are no longer regularly measured at a predefined time step and are instead recorded on the basis of events. This renders a considerable real-time compression. An inventive event-driven coulomb counting method is then presented, which employs the irregularly sampled data information for an effective online state of charge (SOC) determination. A high energy battery model for electric vehicle (EV) applications is studied in this work. It is implemented by using the equivalent circuit modeling (ECM) approach. A comparison of the developed framework is made with conventional fixed-rate counterparts. The results show that, in terms of compression and computational complexities, the devised solution surpasses the second order of magnitude gain. The SOC estimation error is also quantified, and the system attains a ≤4% SOC estimation error bound.

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“…However, due to the complicated structure of lithium‐ion batteries, usually SOC is estimated through some physical characteristics of the battery and cannot be directly sampled with sensors 5,6 . Therefore, accurate SOC identification plays a key role in the charge/discharge management of lithium‐ion batteries and power optimization management of the EVs, which directly affects the battery service life and car performance 7 …”

Section: Introductionmentioning

confidence: 99%

“…5,6 Therefore, accurate SOC identification plays a key role in the charge/discharge management of lithium-ion batteries and power optimization management of the EVs, which directly affects the battery service life and car performance. 7 1.1 | The SOC estimation schemes Many mature SOC estimation schemes have been researched and published. 8,9 Generally, the mainstream estimation schemes contain three categories: the direct estimation schemes, the model-based estimation schemes and the intelligent estimation schemes.…”

Section: Introductionmentioning

confidence: 99%

A beetle antennae search optimized recurrent extreme learning machine for battery state of charge estimation

Wang

2022

Intl J of Energy Research

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Summary The extreme learning machine (ELM) is a single‐hidden layer feedforword neural network (FNN) without training hidden layer weights/biases. By constructing a recurrent extreme learning machine (Recurrent‐ELM) with time delay lines to model battery dynamic characteristics, a beetle antennae search based recursive least squares (BAS‐RLS) method is explored to online realize the state of charge (SOC) estimation. The contents include: (1) To decrease the computational burden, the ELM model with fixed hidden layer weights is adopted to model battery SOC, and a RLS algorithm is studied to online estimate SOC by using the sampled terminal voltages and currents; (2) To solve the modeling accuracy problem, a Recurrent‐ELM model with past/present voltages and currents, and past SOC as inputs is constructed by adding time delay lines to capture battery dynamic characteristics, so as to promote the battery modeling accuracy; (3) To determine suitable neuron numbers in the hidden layer, the BAS method is introduced to find the optimal neuron number in the hidden layer to promote intelligence of the Recurrent‐ELM based RLS algorithm. Simulation results indicate that the proposed model and method has high precision in SOC estimation compared with traditional method.

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Influence Analysis and Optimization of Sampling Frequency on the Accuracy of Model and State-of-Charge Estimation for LiNCM Battery

Cited by 11 publications

References 29 publications

A Fast Online State of Health Estimation Method for Lithium-Ion Batteries Based on Incremental Capacity Analysis

A Fast Online State of Health Estimation Method for Lithium-Ion Batteries Based on Incremental Capacity Analysis

Event-Driven Coulomb Counting for Effective Online Approximation of Li-Ion Battery State of Charge

A beetle antennae search optimized recurrent extreme learning machine for battery state of charge estimation

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