Future Trends and Aging Analysis of Battery Energy Storage Systems for Electric Vehicles

Asef, Pedram; Milan, Marzia; Lapthorn, Andrew; Padmanaban, Sanjeevikumar

doi:10.3390/su132413779

Cited by 17 publications

(2 citation statements)

References 255 publications

(197 reference statements)

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“…Prolonged storage, transport, or large temperature changes can cause high-capacity degradation [36,37]. In order to establish that the expected value is fully met in all cases, tests are required.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Diagnostics and Risk Assessment for Energy Supply in Military Vehicles

et al. 2022

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Hybrid vehicles are gaining increasing global prominence, especially in the military, where unexpected breakdowns or even power deficits are not only associated with greater expense but can also cost the lives of military personnel. In some cases, it is extremely important that all battery cells and modules deliver the specified amount of capacity. Therefore, it is recommended to introduce a new measurement line of rapid diagnostics before deployment, in addition to the usual procedures. Using the results of rapid testing, we recommend the introduction of a hierarchical three-step diagnostics and assessment procedure. In this procedure, the key factor is the building up of a hierarchical tree-structured fuzzy signature that expresses the partial interdependence or redundancy of the uncertain descriptors obtained from the rapid tests. The fuzzy signature structure has two main important components: the tree structure itself, and the aggregations assigned to the internal nodes. The fuzzy signatures that are thus determined synthesize the results from the regular maintenance data, as well as the effects of the previous operating conditions and the actual state of the battery under examination; a signature that is established this way can be evaluated by “executing the instructions” coded into the aggregations. Based on the single fuzzy membership degree calculated for the root of the signature, an overall decision can be made concerning the general condition of the batteries.

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Section: Introductionmentioning

confidence: 99%

Hierarchical Diagnostics and Risk Assessment for Energy Supply in Military Vehicles

et al. 2022

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“…Lithium-ion (Li-ion) batteries are becoming the most promising key component for the vision of a sustainable future with the rise of e-mobility. It has earned this well-deserved reputation due to its high power and energy density and long cycle life 1 . Although their proven success, as any other system does, Li-ion batteries also suffer from degradations over time because of different ageing phenomenons such as cyclic and calendar ageing.…”

Section: Introductionmentioning

confidence: 99%

Calendar ageing modelling using machine learning: an experimental investigation on lithium ion battery chemistries

Celen¹,

Ozcelik²,

Turgut³

et al. 2022

Open Res Europe

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Background: The phenomenon of calendar ageing continues to have an impact on battery systems worldwide by causing them to have undesirable operation life and performance. Predicting the degradation in the capacity can identify whether this phenomenon is occurring for a cell and pave the way for placing mechanisms that can circumvent this behaviour. Methods: In this study, the machine learning algorithms, Extreme Gradient Boosting (XGBoost) and artificial neural network (ANN) have been used to predict the calendar ageing data belonging to six types of cell chemistries namely, Lithium Cobalt Oxide, Lithium Iron Phosphate, Lithium Manganese Oxide, Lithium Titanium Oxide, Nickle Cobalt Aluminum Oxide and Nickle Manganese Cobalt Oxide. Results: Prediction results with overall Mean Absolute Percentage Error of 0.0126 have been obtained for XGBoost algorithm. Among these results, Nickle Cobalt Aluminum Oxide and Nickle Manganese Cobalt Oxide type cell chemistries stand out with their mean absolute percentage errors of 0.0035 and 0.0057 respectively. Also, algorithm fitting performance is relatively better for these chemistries at 100% state of charge and 60°C temperature compared to ANN results. ANN algorithm predicts with mean absolute error of approximately 0.0472 overall and 0.0238 and 0.03825 for Nickle Cobalt Aluminum Oxide and Nickle Manganese Cobalt Oxide. The fitting performance of ANN for Nickle Manganese Cobalt Oxide at 100% state of charge and 60°C temperature is especially poor compared to XGBoost. Conclusions: For an electric vehicle battery calendar ageing prediction application, XGBoost can establish itself as the primary choice more easily compared to ANN. The reason is XGBoost’s error rates and fitting performance are more usable for such application especially for Nickel Cobalt Aluminum Oxide and Nickel Manganese Cobalt Oxide chemistries, which are amongst the most demanded cell chemistries for electric vehicle battery packs.

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LSTM Model-Based Fault Detection for Electric Vehicle’s Battery Packs

Wójcik

Przystałka

2022

Intelligent and Safe Computer Systems in Control and Diagnostics

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Future Trends and Aging Analysis of Battery Energy Storage Systems for Electric Vehicles

Cited by 17 publications

References 255 publications

Hierarchical Diagnostics and Risk Assessment for Energy Supply in Military Vehicles

Hierarchical Diagnostics and Risk Assessment for Energy Supply in Military Vehicles

Calendar ageing modelling using machine learning: an experimental investigation on lithium ion battery chemistries

LSTM Model-Based Fault Detection for Electric Vehicle’s Battery Packs

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