EVERLASTING: Electric Vehicle Enhanced Range, Lifetime And Safety Through INGenious battery management.
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Vlaamse Instelling voor Technologisch Onderzoek (VITO); Commissariat à l’Energie Atomique et aux Energies Alternatives (CEA); Siemens; Technische Universität München (TUM); TÜV SÜD Battery Testing et. al. (2021): EVERLASTING: Electric Vehicle Enhanced Range, Lifetime And Safety Through INGenious battery management. Version 4. 4TU.ResearchData. collection. https://doi.org/10.4121/c.5065445.v4
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The EVERLASTING project will develop innovative technologies to improve the reliability, lifetime and safety of Lithium-ion batteries by developing more accurate, and standardized, battery monitoring and management systems. This allows predicting the battery behavior in all circumstances and over its full lifetime and enables pro-active and effective management of the batteries, which leads to more reliability and safety which enables preventing issues rather than mitigating them.
Moreover, by exploiting the interaction between the battery and the vehicle, more accurate range predictions can be made to reduce the range anxiety for the driver and allows the battery to be kept in a safe and optimal operational state to improve the lifetime of the battery (target +20%) and to use the battery to its full capacity in a safe way. This will lead to lower overall costs.
history
- 2021-04-07 first online
- 2021-02-11 published, posted, revised
publisher
4TU.Centre for Research Data
references
DATASETS
- [dataset] A Global Optimal Solution to the Eco-Driving Problem
- [dataset] Dataset underlying Pulse resistance based online temperature estimation for lithium-ion cells
- [dataset] Dataset underlying the research of electrochemical model-based state estimation
- [dataset] Dataset underlying the research of Digital twin for battery systems
- [dataset] Dataset underlying the research of Online Aging Determination in Lithium-ion Battery Module with Forced Temperature Gradient
- [dataset] Dataset underlying the research of Reversible Self-discharge and Calendar Aging of 18650 Nickel-rich, Silicon-Graphite Lithium-ion Cells
- [dataset] Dataset underlying the research of Cell-to-Cell Variation of Calendar Aging and ReversibleSelf-discharge in 18650 Nickel-rich, Silicon-GraphiteLithium-ion Cells
- [dataset] Dataset underlying the research of Uncertainty-aware State Estimation for Electrochemical Model-based Fast Charging Control of Lithium-ion Batteries
- [dataset] Dataset underlying the research of Uncertainties in Entropy due to Temperature Path Dependent Voltage Hysteresis in Li-Ion Cells
- [dataset] Dataset underlying the research of Evolution of Initial Cell-to-Cell Variations During a Three-Year Production Cycle
- [dataset] Dataset underlying the research of Identification of Lithium Plating in Lithium-Ion Batteries by Electrical and Optical Methods
- [software] MATLAB-scripts describing a nonlinear steady-state cornering model for an electric city bus
- [dataset] Modeling and simulation of inhomogeneities in a 18650 nickel-rich, silicon-graphite lithium-ion cell during fast charging - Dataset
- [dataset] Non-Destructive Detection of Local Aging in Lithium-Ion Pouch Cells by Multi-Directional Laser Scanning
- [dataset] Parameter Estimation of an Electrochemistry-based Lithium-ion Battery Model using a Two-Step Procedure and a Parameter Sensitivity Analysis
- [dataset] State estimation of lithium-ion cells using a physicochemical model based extended Kalman filter - Dataset
- [dataset] Suitability of physicochemical models for embedded systems regarding a nickel-rich, silicon-graphite lithium-ion battery - Dataset
- [software] TU/e Microscopic Energy Consumption PRediction tOol 0.1 (TU/e MECPRO 0.1)