The HIDDEN agenda - High energy density battery with self-healing properties

Marja Vilkman (middle, left) and her research team in the labs at VTT, Finland.

A self-healing battery is a new star on the battery research sky. The idea is to slow down the aging process in the battery substantially. This is being done by hindering dendrite growth at the anode of the battery and has the potential to increase the lifetime and energy density of Li-metal batteries by 50 percent compared to today’s Li-ion batteries. This is what Hidden, one of the research projects within Battery 2030+, aims for.

Today’s most common electric vehicle (EV) batteries use graphite as the anode material. The main problem in the graphite anode is its limited energy density, which also eventually hinders the driving range of EV’s. The ideal anode material option for enabling high energy density batteries is metallic lithium. Unfortunately, the formation of dendrite growth, a form of “plaque” at the electrode surface, is an essential factor that limits the use of metallic lithium as the anode. Solid-State electrolytes (SSEs) could help controlling the nucleation and prevent the growth of dendrites, but it has proven to be difficult to develop and process SSEs combining high enough conductivity, mechanical properties, and stabilized, electrochemically active interphases with electrode.

To get hold of the dendrite nucleation and growth, one needs to know more fundamentally what is happening in the battery and find solutions to prevent unwanted reactions at cell level. To do this, several things need to be in place; one is to use non-invasive analyzing tools to detect dendrites and applying multiscale modelling to accelerate the discovery of next generation electrolytes for alkali-metal anode-based batteries.

  • We aim to develop just that, and in combination with advanced algorithms, we can monitor the dendrites in the lab, model the growth virtually, and trigger the self-healing when needed, says Dr. Marja Vilkman, project leader for Hidden. We are looking at chemistry-neutral thermotropic ionic liquid crystals (TILCs)-based self-healing electrolytes. Their structure will be simultaneously i) encoded according to their tailor-made molecular design and ii) changed with the temperature; thus breaking the formed dendrites mechanically during phase transitions and upon heating above their clearing point. This dynamic process is fully reversible with the temperature. When complemented by a polarized piezoelectric polymeric separator, that creates an electric field when bended, the dendrite formation will diminish. If TILCs are topped up with protective additives to generate self-healing electrolytes, dendrite nucleation and growth will be ultimately put under smart control to make batteries with enhanced energy density and improved lifetime.

Another important part of the Hidden project is to make sure that the materials and their processes are scalable and industrially fit for manufacturing. Europe has set forth to be the first carbon free continent by 2050. Hidden contributes by enabling sustainable energy storage with longer battery lifetime and higher energy storage capacity.

Short fact square
The Hidden project aims to:

  • develop novel self-healing thermotropic liquid crystalline electrolytes and piezoelectric separator technologies
  • investigate both technologies with protective additives
  • apply multiscale modelling means for electrolyte design and analysis algorithm to monitor the dendrite nucleation and growth

BIG-MAP, the Battery Interface Genome ‐ Materials Acceleration Platform is the largest of the seven BATTERY 2030+ projects.

BIG-MAP conf image

BIG-MAP, the Battery Interface Genome Materials Acceleration Platform is the largest of the seven BATTERY 2030+ projects.

The aim is to reinvent the way we invent batteries. This will lead to a dramatic acceleration in the discovery of new battery materials and chemistries. The ambition is to increase the discovery rate by at least a factor 5 relative to the current rate.

The ability to understand and control battery interfaces and interphases is essential for the development of ultra-performing, smart and sustainable batteries. The chemical space within a battery is comprised of a multitude of different elements and structures that cross influence each other. The chemical composition of electrodes, formulation of electrolytes, electrode manufacturing process, packaging, and cell aging are all examples of this. Even slight modifications in the electrode structure, the solid-electrolyte interphase (SEI) or the processing conditions can lead to a drastic change in the battery performance. The combinatorics of this space is enormous and exhaustive to explore in the lab.

If we want to accelerate the battery discovery process, today’s existing methodologies are simply not enough. A critical element is the development of an AI-orchestrated and fully autonomous Materials Acceleration Platform (MAP) that is capable of utilizing data from all domains, time- and length scales of the battery value chain. In order to do so, a shared and interoperable data infrastructure needs to be developed that is based on the FAIR (findability, accessibility, interoperability, and reusability) principles. The MAP will be unified by a community-wide battery language (the BattINFO ontology). This is a shared knowledge-based representation across all relevant scales, techniques and fields, capable of converting simulations and real-life observations to a common digital representation. This includes data from multiscale computer simulations, operando characterization, modular synthesis robotics and automated testing protocols. With this unique access to FAIR battery data, BIG-MAP will develop physics-aware machine and deep learning models that can efficiently utilize the petabytes training data to establish the Battery Interface Genome (BIG), and predict how battery materials and interfaces evolve in space and time. The aim is to create a cost-effective path to fast track inverse design of future battery materials and technologies, based on a profound understanding of the chemical and physical properties in battery systems.

To build the BIG-MAP platform is a large community effort and also needs to incorporate ways to protect IP, as well as provenance tracking of data from multiple sources. By developing a dedicated App Store BIG-MAP than openly shares software modules, automated analysis tools and autonomous workflows bridging simulations and experiments, BIG-MAP will contribute and facilitate smart, sustainable and affordable batteries for industrialisation in Europe.

Last modified: 2021-08-09