Data-driven nonparametric Li-ion battery ageing model aiming at learning from real operation data – Part A : storage operation

Conventional Li-ion battery ageing models, such as electrochemical, semi-empirical and empirical models, require a significant amount of time and experimental resources to provide accurate predictions under realistic operating conditions. At the same time, there is significant interest from industry in the introduction of new data collection telemetry technology. This implies the forthcoming availability of a significant amount of real-world battery operation data. In this context, the development of ageing models able to learn from in-field battery operation data is an interesting solution to mitigate the need for exhaustive laboratory testing

Lithium-ion battery data and where to find it

Lithium-ion batteries are fuelling the advancing renewable-energy based world. At the core of transformational developments in battery design, modelling and management is data. In this work, the datasets associated with lithium batteries in the public domain are summarised. We review the data by mode of experimental testing, giving particular attention to test variables and data provided. Alongside highlighted tools and platforms, over 30 datasets are reviewed

Electric vehicle range and battery lifetime: a trade-off

32 nd Electric Vehicle Symposium (EVS32), LYON, FRANCE, 19-/05/2019 - 22/05/201

Batteries and Supercapacitors Aging

Electrochemical energy storage is a key element of systems in a wide range of sectors, such as electro-mobility, portable devices, and renewable energy. The energy storage systems (ESSs) considered here are batteries, supercapacitors, and hybrid components such as lithium-ion capacitors. The durability of ESSs determines the total cost of ownership, the global impacts (lifecycle) on a large portion of these applications and, thus, their viability. Understanding ESS aging is a key to optimizing their design and usability in terms of their intended applications. Knowledge of ESS aging is also essential to improve their dependability (reliability, availability, maintainability, and safety). This Special Issue includes 12 research papers and 1 review article focusing on battery, supercapacitor, and hybrid capacitor aging

Data-driven nonparametric Li-ion battery ageing model aiming at learning from real operation data - Part B : cycling operation

Conventional Li-ion battery ageing models, such as electrochemical, semi-empirical and empirical models, require a significant amount of time and experimental resources to provide accurate predictions under realistic operating conditions. At the same time, there is significant interest from industry in the introduction of new data collection telemetry technology. This implies the forthcoming availability of a significant amount of real-world battery operation data. In this context, the development of ageing models able to learn from in-field battery operation data is an interesting solution to mitigate the need for exhaustive laboratory testing. In a series of two papers, a data-driven ageing model is developed for Li-ion batteries under the Gaussian Process framework. A special emphasis is placed on illustrating the ability of the Gaussian Process model to learn from new data observations, providing more accurate and confident predictions, and extending the operating window of the model. The first paper of the series focussed on the systematic modelling and experimental verification of cell degradation through calendar ageing. Conversantly, this second paper addresses the same research challenge when the cell is electrically cycled. A specific covariance function is composed, tailored for use in a battery ageing application. Over an extensive dataset involving 124 cells tested during more than three years, different training possibilities are contemplated in order to quantify the minimal number of laboratory tests required for the design of an accurate ageing model. A model trained with only 26 tested cells achieves an overall mean-absolute-error of 1.04% in the capacity curve prediction, after being validated under a broad window of both dynamic and static cycling temperatures, Depth-of-Discharge, middle-SOC, charging and discharging C-rates

Analysis and modelling of calendar ageing in second-life lithium-ion batteries from electric vehicles

The reuse of Li-ion batteries from electric vehicles is a promising alternative to recycling nowadays. However, the technical and economic viability of these second-life (SL) batteries is not yet clear. Degradation assessment plays a key role not only to analyse the impact of ageing factors in reused batteries, but also to quantify their durability. In this context, this contribution aims to analyse calendar ageing behaviour in SL cells. 16 reused Nissan Leaf modules are aged during 750 days under three temperatures and four State of Charge (SOC), covering a State of Health range from 72.2 % to 13 %. The impact of temperature and SOC as stress factors is firstly analysed, concluding that their increase accelerates ageing. Temperature rise is found to have a major impact, accelerating up to 27 times capacity fade and almost 6 times resistance increase when compared to light ageing conditions, while increasing SOC nearly doubles ageing rates. The worst ageing case is found to be the combination of 60 ◦C and 66 % of SOC. Regarding degradation trends, they are proven to be constant during all SL lifetime. This work also proposes and validates a calendar ageing model for SL cells. Accuracy of validation results show a fitting Rsq of 0.9941 in capacity fade and 0.9557 in resistance increase, thereby tracking the heterogeneous degradation of the SL cells under calendar ageing.We would like to acknowledge the support of the Spanish State Research Agency (AEI) grant PID2019-111262RB-I00/AEI/10.13039/501100011033, the European Union H2020 Project STARDUST (74094) and the Government of Navarre Ph.D. scholarship

Lifetime Prediction and Simulation Models of Different Energy Storage Devices

Energy storage is one of the most important enablers for the transformation to a sustainable energy supply with greater mobility. For vehicles, but also for many stationary applications, the batteries used for energy storage are very flexible but also have a rather limited lifetime compared to other storage principles. This Special Issue is a collection of articles that collectively address the following questions: What are the factors influencing the aging of different energy storage technologies? How can we extend the lifetime of storage systems? How can the aging of an energy storage be detected and predicted? When do we have to exchange the storage device? The articles cover lithium-ion batteries, supercaps, and flywheels