An Algorithm for an Online Electrochemical Impedance Spectroscopy and Battery Parameter Estimation: Development, Verification and Validation

Kuipers, Matthias; Schröer, Philipp; Nemeth, Thomas; Zappen, Hendrik; Blömeke, Alexander; Sauer, Dirk Uwe

doi:10.1016/j.est.2020.101517

Cited by 37 publications

(22 citation statements)

References 12 publications

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“…Before transformation of the acquired data to the frequency domain, preprocessing is performed. Preprocessing steps may include, for example, digital filtering [268,290,314], gain and offset compensation [290,292], segmentation [112,315], windowing [112,306,307,316], zeropadding [308], and down sampling [293,296,312]. Potential uses of digital filtering include HPFs to remove low frequency components [290] and compensate for drift [314], Bandpass Filters (BPFs) as preparation for correlation based postprocessing [268] as well as decimation filters for down sampling [293,296].…”

Section: Impedancementioning

confidence: 99%

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Critical Review of Intelligent Battery Systems: Challenges, Implementation, and Potential for Electric Vehicles

et al. 2021

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This review provides an overview of new strategies to address the current challenges of automotive battery systems: Intelligent Battery Systems. They have the potential to make battery systems more performant and future-proof for coming generations of electric vehicles. The essential features of Intelligent Battery Systems are the accurate and robust determination of cell individual states and the ability to control the current of each cell by reconfiguration. They enable high-level functions like fault diagnostics, multi-objective balancing strategies, multilevel inverters, and hybrid energy storage systems. State of the art and recent advances in these topics are compiled and critically discussed in this article. A comprising, critical discussion of the implementation aspects of Intelligent Battery Systems complements the review. We touch on sensing, battery topologies and management, switching elements, communication architecture, and impact on the single-cell. This review contributes to transferring the best technologies from research to product development.

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Section: Impedancementioning

confidence: 99%

“…These approaches, however, are best suited for sinusoidal excitation and the absence of load current or other disturbances. More robust alternative approaches are based on correlation of current and voltage in a selected frequency range [268], the Goertzel algorithm [151,308,314], or single frequency DFT methods [296,300,311].…”

Section: Impedancementioning

confidence: 99%

Critical Review of Intelligent Battery Systems: Challenges, Implementation, and Potential for Electric Vehicles

et al. 2021

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“…During the entire cycle of the battery, the observer parameter is treated as a fixed value, and the compensation factor ξ alters the model characteristic parameters according to the usage of the battery. Since the battery SOH usually presents an exponential change with the number of cycles [28], the changes in the compensation factor mentioned will be updated in the form of Equation (12). R f resh is used as the initial value of battery impedance.…”

Section: Second-level Pi Observer For Soc Estimationmentioning

confidence: 99%

“…Many other methods were proposed to solve the drawbacks of the Coulomb counting method. One of the effective methods to improve the accuracy of SOC estimation is to use the unique correlation between the measurable parameters of battery and SOC, such as open-circuit voltage method [11] or electrochemical impedance spectroscopy [12]. However, these methods require long periods of resting for batteries, which is not suitable for practical control and these measurable parameters are also affected by internal or external factors, such as battery aging [13].…”

Section: Introductionmentioning

confidence: 99%

A Fast Lithium-Ion Battery Impedance and SOC Estimation Method Based on Two-Stage PI Observer

Chen

Huo

Yang

2021

WEVJ

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Due to the complex changes in battery state, the accurate and fast estimation of battery state of charge (SOC) is still a great challenge. Here, a fast estimation method of battery impedance and SOC based on a multi-level PI observer is proposed. The observer model reflects the change of the battery state characteristics through the dynamic impedance, and then the system compensation factor is added to the observer to dynamically adjust the parameters of the battery model. The effectiveness of the algorithm is verified by the compound dynamic stress test (DST) experiment. The results show that the introduction of the compensation factor enables the system to tolerate a certain degree of impedance fluctuation and capacity attenuation and the maximum SOC estimation error can be kept within 2%.

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“…To solve this problem, some online battery impedance measurement methods have been proposed [1,2,5,8]. These methods can be classified into two categories: external signal injection and converter-based perturbation [1, 2,5,[8][9][10][11]. Specifically, the first category mainly uses the external source to generate small current or voltage signals to perturb the battery.…”

Section: Introductionmentioning

confidence: 99%

Accurate Online Battery Impedance Measurement Method with Low Output Voltage Ripples on Power Converters

Yao

Lei

2021

Energies

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The conventional online battery impedance measurement method works by perturbing the duty cycle of the DC-DC power converter and measuring the response of the battery voltage and current. This periodical duty cycle perturbation will continuously generate large voltage ripples at the output of power converters. These large ripples will not easily be removed due to the high amplitude and wide frequency range and would be a challenge to meet tight output regulation. To solve this problem, this paper presents a new online battery impedance measurement technique by inserting a small switched resistor circuit (SRC) into the converter. The first contribution of this work is that the perturbation source is moved from the main switch to the input-side of the converter, so the ripples are reduced. The analysis and experimental results of the proposed method show a reduction of 16-times compared with the conventional method. The second contribution tackles the possible change of the battery state of charge (SOC) during the online battery measurement process, which will inevitably influence the impedance measurement accuracy. In this proposed method, battery impedance at multiple frequencies can be measured simultaneously using only one perturbation to accelerate measurement speed and minimize possible SOC change. The experimental impedance results coincide with a high-accuracy laboratory battery impedance analyzer.

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An Algorithm for an Online Electrochemical Impedance Spectroscopy and Battery Parameter Estimation: Development, Verification and Validation

Cited by 37 publications

References 12 publications

Critical Review of Intelligent Battery Systems: Challenges, Implementation, and Potential for Electric Vehicles

Critical Review of Intelligent Battery Systems: Challenges, Implementation, and Potential for Electric Vehicles

A Fast Lithium-Ion Battery Impedance and SOC Estimation Method Based on Two-Stage PI Observer

Accurate Online Battery Impedance Measurement Method with Low Output Voltage Ripples on Power Converters

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