Battery charge and discharge schemes for minimising battery wear in aggregated systems

Supervisor: A/Prof Arnfinn A. Eielsen
(Last updated: 04 Nov 2025)

This project focusses on how to share and minimise individual battery wear in a system with several individual batteries connected to a shared power bus, on order to supply flexibility to grid operators.

This is applicable in e.g. housing cooperatives or apartment housing complexes where several housing units may have a grid connected battery pack installed, og in parking facilities where several vehicles are connected to the power grid in vehicle-to-grid (V2G) mode.

The batteries may be individually owned, or all owned by an organisation; but in either case it is of interest to exploit the presence of several connected batteries and operate the batteries in a manner that overall minimises wear in order to maximise the longevity and maintain battery performance, thus minimising the lifetime cost.

There exact objectives will depend on circumstance such as the operating entity (individual or organisation), what kind of battery (stationary or in-vehicle), and the type of flexibility offered (e.g. peak shaving).

Salient variables include the rate of charge and discharge, the depth of discharge, the state-of-charge, the capacity, recommended minimum and maximum charge levels, ambient temperature, and the number of charge-discharge cycles.

The initial investigation will into how wear cost can be minimised and shared under different schemes, but may be extended to determining the overall economics of owning and operating a battery considering revenues from flexibility products.

The exact scope, content and outcomes of the project will be agreed upon with the students considering their background and interests.

Expected outcomes

Resources

Collaboration

The work is done in collaboration with Chargely AS, a company that makes V2G solutions. Chargerly provides an industrially relevant context, and can liaise with partners to supply the project with realistic and relevant data and scenarios.