Comparative Study Between Internal Ohmic Resistance and Capacity for Battery State of Health Estimation

Ramadan, M. Nisvo; Pramana, Bhisma Adji; Widayat, Sigit Agung; Amifia, Lora Khaula; Cahyadi, Adha Imam; Wahyunggoro, Oyas

doi:10.14203/j.mev.2015.v6.113-122

Cited by 15 publications

(6 citation statements)

References 17 publications

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“…Differently from the approaches in the above references, SOH is a standard approach that uses some indirect estimations. It is possible to calculate SOH based on capacity or internal resistance [30,38,39]. Calculating the SOH based on capacity is a traditional mathematical approach that takes into account the entire accumulated discharge current for a cycle.…”

Section: State-of-the-artmentioning

confidence: 99%

Random Forest-Based Grouping for Accurate SOH Estimation in Second-Life Batteries

Gotz,

Galvão,

Corrêa

et al. 2024

Vehicles

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Retired batteries pose a significant current and future challenge for electric mobility due to their high cost and the need for a state of health (SOH) above 80% to supply energy efficiently. Recycling and alternative applications are the primary options for these batteries, with recycling still undergoing research as regards more efficient and cost-effective techniques. While advancements have been made, researchers are actively seeking improved methods. Repurposing retired batteries for lower-performance applications like stationary systems or low-speed vehicles is recommended. Second-life batteries (SLB) can be directly reused or reconstructed, with the latter involving the disassembly, measurement, and separation of cells based on their characteristics. The traditional measurement process, involving full charge and discharge cycles, is time-consuming. To address this, a Machine Learning (ML)-based SOH estimator is introduced in this work, offering the instant measurement and estimation of battery health without complete discharge. The results indicate that the model can accurately identify SOH within a nominal capacity range of 1400–2300 mAh, with a resolution near 45.70 mAh, in under five minutes of discharging. This innovative technique could be instrumental in selecting and assembling SLB packs.

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Section: State-of-the-artmentioning

confidence: 99%

Random Forest-Based Grouping for Accurate SOH Estimation in Second-Life Batteries

Gotz,

Galvão,

Corrêa

et al. 2024

Vehicles

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show abstract

“…As a result, only ions are allowed to pass between the electrodes when using this method. Thus, individual Li ions do not need to entirely drive from the anode to the cathode through the circuit [21]. The space between the electrodes is filled with liquid.…”

Section: Electrolytementioning

confidence: 99%

Present and Future Generation of Secondary Batteries: A Review

Hemavathi,

Srirama,

Prakash

2023

ChemBioEng Reviews

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Major support for the future energy storage and application will benefit from lithium‐ion batteries (LIBs) with high energy density and high power. LIBs are currently the most common battery type for most applications, but soon a broader range of battery types and higher energy densities will be available. In the near future, hundreds of millions of electric vehicles are expected to be on the road, and a large amount of cobalt will be depleted. Various kinds of batteries are developed today to store energy, including Li‐ion, lead‐acid, Ni‐MH, redox flow, Na‐ion, Mg‐ion, Li‐air, Al‐ion, Li/S, NC‐based batteries, Al‐based batteries, metal‐air batteries, solid‐state batteries, etc. There are several types of battery components, such as electrodes, electrolytes, separators, etc. Cell chemistry and component diversity will continue to increase with future generations of batteries. Next‐generation LIBs and sodium‐ion batteries are explored for their ability to reduce active ion loss and increase energy density by pre‐lithiation. To maximize the electrochemical system's performance, various scientific and technological approaches are needed to maximize the potential of battery chemistry.

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“…Estimation speed and reliability have been compared between internal ohmic resistance based estimation and capacity based estimation. It can be concluded that SOH estimation based on internal resistance is faster and more reliable [12].…”

Section: Introductionmentioning

confidence: 96%

Comparison between RLS-GA and RLS-PSO for Li-ion battery SOC and SOH estimation: a simulation study

Rozaqi

Rijanto

Kanarachos

2017

J. Mechatron. Electr. Power Veh. Technol.

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This paper proposes a new method of concurrent SOC and SOH estimation using a combination of recursive least square (RLS) algorithm and particle swarm optimization (PSO). The RLS algorithm is equipped with multiple fixed forgetting factors (MFFF) which are optimized by PSO. The performance of the hybrid RLS-PSO is compared with the similar RLS which is optimized by single objective genetic algorithms (SOGA) as well as multi-objectives genetic algorithm (MOGA). Open circuit voltage (OCV) is treated as a parameter to be estimated at the same time with internal resistance. Urban Dynamometer Driving Schedule (UDDS) is used as the input data. Simulation results show that the hybrid RLS-PSO algorithm provides little better performance than the hybrid RLS-SOGA algorithm in terms of mean square error (MSE) and a number of iteration. On the other hand, MOGA provides Pareto front containing optimum solutions where a specific solution can be selected to have OCV MSE performance as good as PSO.

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Comparative Study Between Internal Ohmic Resistance and Capacity for Battery State of Health Estimation

Cited by 15 publications

References 17 publications

Random Forest-Based Grouping for Accurate SOH Estimation in Second-Life Batteries

Random Forest-Based Grouping for Accurate SOH Estimation in Second-Life Batteries

Present and Future Generation of Secondary Batteries: A Review

Comparison between RLS-GA and RLS-PSO for Li-ion battery SOC and SOH estimation: a simulation study

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