HEИндивидуална стипендия2022–2024

ULICBat · User behaviour informed learning and intelligent control for charging of vehicle battery packs

„Хоризонт Европа“ — Действия „Мария Склодовска-Кюри“

Период: 2022-08-01 → 2024-09-30
Финансиране от ЕС: 222 728 €
Участници: 1
Схема: HORIZON-TMA-MSCA-PF-EF

Линиите свързват координатора с партньорите.

Накратко на български

Литиево-йонните батерии на електромобилите се изследват, за да се оптимизира зареждането им според нуждите на потребителя вместо чрез постоянно бързо зареждане. Това помага за забавяне на износването на капацитета им и повишаване на безопасността при употреба.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

User behaviour informed learning and intelligent control for charging of vehicle battery packs

Electric vehicles (EVs) have recently experienced rapid development as an ideal alternative to traditional fossil-fuel-powered vehicles. By transitioning away from fossil fuels to renewable energy sources, EVs significantly reduce carbon emissions and harmful pollutants, improving public health and environmental quality. Moreover, EV technology promotes energy diversification, decreasing dependence on non-renewable resources and enhancing energy affordability and stability. As one of the most expensive and perhaps the least understood components of EVs, the lithium-ion (Li-ion) battery pack necessitates advanced management for safe and efficient use since it is often associated with risks and uncertainty, exemplified by ageing and accidents. Charging is a critical process for Li-ion batteries to replenish energy. The widely adopted fast charging strategies can reduce the charging time, alleviating the mileage anxiety of EV users to a limited extent. However, the high electrical current rates utilised in these strategies inevitably lead to extensive battery capacity degradation. Hence, how to intelligently optimise the charging process according to different user demands instead of blindly fast charging to relieve battery degradation is an urgent and valuable research problem. The lack of accurate battery prediction models for practical use forms one of the biggest technical challenges in the field of advanced charging management. This fact has strongly motivated the design of a model-free charging control strategy that can still achieve multi-objective charging optimization. Last but not least, cell imbalances caused by inhomogeneous conditions and manufacturing variations result in insufficient energy use, accelerated capacity degradation, and even safety hazards of the entire battery pack. How to ensure that the battery cells' states in a battery pack are consistent at the end of the charging process is another major challenge that this project focuses on. The objective of this project is to create intelligent and health-aware charging strategies for Li-ion battery packs, in which the user’s short-term demands and long-term behaviours will be fully considered.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

The realisation of the EU industrial and political ambition of promoting transportation electrification to reduce carbon emissions depends heavily on the continued advancement of battery technologies. However, rapid battery pack degradation caused by improper charging behaviours is one of the most crucial factors restricting the wide usage of electric vehicles. This project aims to make step changes in research and innovation of battery management by developing 1) a user charging preference learning and demand prediction model methodology, 2) a user demand-informed optimisation methodology of charging trajectory, and 3) a distributed trajectory tracking-based battery pack charging control, and realise an advanced career development for the experienced researcher. As an MSCA-PF fellow, the experienced researcher, Dr. Quan Ouyang, will receive crucial career development at the Chalmers University of Technology and engage in detailed battery charging control works which span the areas of automatic control and artificial intelligence using a clearly defined training-through-research approach. The project results will include a generic smart charging system to prolong battery life and improve the vehicles resource efficiency and convenience. The incorporated battery pack charging strategies will benefit from revolutionarily considering the user preferences and demand, where the designed charging currents can be intelligently adjusted according to different user demands that can effectively restrict battery degradation. The hosting group at the Chalmers University of Technology has a tradition of projects in close cooperation with the vehicle industry (e.g., Volvo Cars, Scania, and CEVT), making it highly likely that the results of this project will be taken up by the industry. Ultimately, this project will contribute to the EU's carbon neutrality goal and the UN Sustainable Development Goals in ""affordable and clean energy"" and ""sustainable cities"".""

Оригинален текст от CORDIS (на английски).

Участници

CHALMERS TEKNISKA HOGSKOLA AB · GoteborgКоординаторШвеция