A Review and Perspective on Path Dependency in Batteries

Röder, Fridolin; Ramasubramanian, Srivatsan

doi:10.1002/ente.202200627

Cited by 9 publications

(11 citation statements)

References 132 publications

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“…b HC ¼ x FC À a HC � x HC (12) As the scale and shift parameters result from the fitting and C FC is measured during the RPT, LAM PE and LAM NE can further be calculated as in Eq. ( 13) and Eq.…”

Section: Degradation Modes Analysismentioning

confidence: 99%

“…As many interacting processes are involved in battery ageing, such an approach is not sufficient. [12] More modern ageing models try to tackle these challenges. For example, based on the superposition approach from Schmalstieg et al, [11] Hildenbrand et al [13] extended the ageing model with an additional RC circuit and a voltage source to include reversible capacity loss and recovery effects caused by the anodic overhang.…”

Section: Introductionmentioning

confidence: 99%

“…Despite the many efforts made to assess and depict these complex interactions of LIB-ageing, the question of whether LIBs show a path dependency in ageing and how it can be integrated into a model needs to be revised. As no clear definition of path dependency concerning LIB-ageing exists, Röder et al [12] gave different definitions of the path (in)dependency for the ageing of LIBs. Terms PD I, PD II, and PD III from their review on path dependence will further be used as they provide unambiguous definitions in the authors' view.…”

Section: Introductionmentioning

confidence: 99%

“…Figure 1. Definitions of path-dependent ageing of LIBs according to Röder et al [12] PD I (a) only ascribes the operation condition as important for the ageing path, whereas PD II (b) does include the temporal sequence in which operation conditions are applied. PD III (c) says that even if the state (e. g. the SOH) is the same, the ageing history influences the further course of ageing.…”

Section: Introductionmentioning

confidence: 99%

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Path‐Dependent Ageing of Lithium‐ion Batteries and Implications on the Ageing Assessment of Accelerated Ageing Tests

Rogge,

Jossen

2023

Batteries & Supercaps

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Path dependency in ageing of Lithium‐Ion batteries still needs to be fully understood, and gaps remain. For realistic operational scenarios that involve dynamic load profiles, understanding this path dependency is essential for effective monitoring and accurate modelling of LIBs‐ageing. Our research investigates the impact of different ageing sequences on capacity reduction and resistance increase, key metrics for determining the state of health (SOH). Moreover, we argue that relying solely on SOH‐based monitoring is insufficient for predicting the ongoing ageing trajectory. Our findings underscore that recent operational history influences subsequent degradation. This degradation is attributed to the emergence of uneven lithium distribution, which can both induce capacity recovery and amplify degradation during cycling phases. Such insights are particularly interesting for ageing studies where accelerated battery degradation is achieved through continuous cycling, a common approach in most cyclic ageing investigations. We demonstrate that capacity difference analysis (CDA) holds promise in tracking this unevenness and the potential for capacity restoration through re‐homogenisation. In conclusion, our work highlights the importance of utilising advanced tools, such as CDA and degradation mode (DM) analysis, to ensure accurate conclusions are drawn in accelerated LIB‐ageing experiments.

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Section: Degradation Modes Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Path‐Dependent Ageing of Lithium‐ion Batteries and Implications on the Ageing Assessment of Accelerated Ageing Tests

Rogge,

Jossen

2023

Batteries & Supercaps

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“…One of the difficulties in OCV estimation is to simulate the hysteresis of the battery, which is manifested by the fact that the OCV of the same SOC is inconsistent during charging and discharging. [33][34][35][36][37] Ref. [38] related the hysteresis behavior of OCV to the two-phase transition process in charge and discharge and believed that the hysteresis is mainly caused by the energy dissipation in the electrode crystal structure during the two-phase transition process.…”

mentioning

confidence: 99%

Battery Modeling Considering Hysteresis Effect and Temperature Adaptability

et al. 2023

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With the increasing severity of energy crisis and the gradual increase of environmental awareness, countries around the world have devoted themselves to the new energy vehicle industry. [1] As the foundation and core component of electric vehicles, the power battery pack and its management system play a decisive role in the performance and development of electric vehicles. The power battery pack often uses lithium-ion batteries with higher energy density, lower self-discharge rate, and long cycle life. [2][3][4][5][6] Accurate stateof-charge (SOC) estimation is an important factor to control the stable operation of batteries, which directly affects the life and safety of the battery. [7][8][9] At present, SOC estimation can be roughly divided into two categories: principle-based and model-based methods. [10,11] The modelbased SOC estimation method is widely used because of its high robustness and accuracy under variable battery operating conditions. [12,13] Battery models, as an important part of model-based SOC estimation methods, commonly include electrochemical models, blackbox models and equivalent circuit models (ECMs). The electrochemical model describes the diffusion process and charge transfer process of lithium ions in the cell based on porous electrodes and concentrated solution theory and uses a set of coupled partial differential equations to achieve real-time estimation of SOC. [14][15][16] Ref.[17] designed a nonlinear observer with terminal-voltage feedback injection based on the electrochemical single-particle model, which improved the accuracy of SOC estimation. Although the electrochemical model can reflect the actual electrochemical reaction process inside the battery more accurately, the structure is complicated and not conducive to practical simulation and calculation. The blackbox model establishes an abstract mapping relationship between inputs and outputs, reflecting a generalized direct causal relationship between the factors involved. Real-time estimation of SOC can be achieved by taking real-time state quantities (e.g., voltage, current, etc.) during battery operation as inputs. [18][19][20][21][22] Ref. [23] proposed an improved feedforward-long short-term memory (FF-LSTM) modeling method to realize an accurate whole-life-cycle SOC prediction by effectively considering the current, voltage, and temperature variations. Although the calculation of the blackbox model is simple, the accuracy of the estimation depends to a large extent on the quality and quantity of the training data, and the training process may take a long time. From the perspective of external electrical characteristics, ECM uses circuit elements such as resistance, capacitance, and voltage source to form a circuit network to express the relationship between voltage and current in the cell. [24] It cleverly avoids the real structure and complex electrochemical reactions inside the battery and can estimate SOC in real-time by combining it with the filtering algorithm. [25][26][27][28][29] Ref. [30] proposed a novel feedback

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Li‐Ion Battery State of Health Estimation Using Hybrid Decision Tree Model Optimized by Bayesian Optimization

Qiang,

Tang,

et al. 2023

Energy Tech

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Accurately battery state of health (SOH) estimation in electric vehicles (EVs) is crucial for optimal performance and safety. This article presents an in‐depth investigation into the utilization of an optimized decision tree (DT) model for precise SOH estimation. Two aging features (AFs) are extracted through incremental capacity analysis from the partial charging process, and their efficacy is thoroughly validated on both battery cells and packs. To capture the relationship between AFs and capacity degradation, a robust DT model is established. Furthermore, Bayesian optimization is employed to optimize the model hyperparameters, enhancing the learning and generalization capabilities of the DT model. To validate the optimized DT model, a battery management system (BMS) is developed using a hybrid programming approach with LabVIEW and MATLAB, enabling online SOH estimation. The BMS undergoes rigorous evaluation using publicly available aging datasets of battery cells, as well as private datasets of battery packs. The results demonstrate that the optimized DT model outperforms traditional DT models, back propagation neural networks, and support vector machine approaches in accurately estimating battery SOH across diverse datasets. This research contributes to the advancement of BMS and holds profound implications for the efficient utilization of batteries in EVs.

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A Review and Perspective on Path Dependency in Batteries

Cited by 9 publications

References 132 publications

Path‐Dependent Ageing of Lithium‐ion Batteries and Implications on the Ageing Assessment of Accelerated Ageing Tests

Path‐Dependent Ageing of Lithium‐ion Batteries and Implications on the Ageing Assessment of Accelerated Ageing Tests

Battery Modeling Considering Hysteresis Effect and Temperature Adaptability

Li‐Ion Battery State of Health Estimation Using Hybrid Decision Tree Model Optimized by Bayesian Optimization

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