A fractional-order model for calendar aging with dynamic storage conditions

Funding for open access charge: Universidad de Granada / CBUA.Due to the increasing importance of lithium-ion batteries in electric vehicle and renewable energy applications, battery aging is a subject of intense research. Although many laboratory experiments are performed under well-controlled static conditions, batteries are stored and operated under varying conditions of temperature and state of charge in their real-life performance, so that a suitable model for predicting the effects of calendar aging in lithium-ion batteries with dynamic conditions is highly desirable. In this paper, we review previous models to calculate capacity loss due to calendar aging under variable temperature and state-of-charge conditions according to experimentally observed power-law behavior, and propose a novel model based on fractional calculus. To validate the new model, we compare its predictions with experimental results showing that it can reproduce the non-monotonic behavior that is observed when the state of charge or the temperature change significantly. This is an interesting application of fractional calculus since this characteristic is not obtained with non-fractional models.Universidad de Granada / CBU

Improving optimal control of grid-connected lithium-ion batteries through more accurate battery and degradation modelling

The increased deployment of intermittent renewable energy generators opens up opportunities for grid-connected energy storage. Batteries offer significant flexibility but are relatively expensive at present. Battery lifetime is a key factor in the business case, and it depends on usage, but most techno-economic analyses do not account for this. For the first time, this paper quantifies the annual benefits of grid-connected batteries including realistic physical dynamics and nonlinear electrochemical degradation. Three lithium-ion battery models of increasing realism are formulated, and the predicted degradation of each is compared with a large-scale experimental degradation data set (Mat4Bat). A respective improvement in RMS capacity prediction error from 11\% to 5\% is found by increasing the model accuracy. The three models are then used within an optimal control algorithm to perform price arbitrage over one year, including degradation. Results show that the revenue can be increased substantially while degradation can be reduced by using more realistic models. The estimated best case profit using a sophisticated model is a 175% improvement compared with the simplest model. This illustrates that using a simplistic battery model in a techno-economic assessment of grid-connected batteries might substantially underestimate the business case and lead to erroneous conclusions

A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries.

As widely used for secondary energy storage, lithium-ion batteries have become the core component of the power supply system and accurate remaining useful life prediction is the key to ensure its reliability. Because of the complex working characteristics of lithium-ion batteries as well as the model parameter changing along with the aging process, the accuracy of the online remaining useful life prediction is difficult but urgent to be improved for the reliable power supply application. The deep learning algorithm improves the accuracy of the remaining useful life prediction, which also reduces the characteristic testing time requirement, providing the possibility to improve the power profitability of predictive energy management. This article analyzes, reviews, classifies, and compares different adaptive mathematical models on deep learning algorithms for the remaining useful life prediction. The features are identified for the modeling ability, according to which the adaptive prediction methods are classified. The specific criteria are defined to evaluate different modeling accuracy in the deep learning calculation procedure. The key features of effective life prediction are used to draw relevant conclusions and suggestions are provided, in which the high-accuracy deep convolutional neural network — extreme learning machine algorithm is chosen to be utilized for the stable remaining useful life prediction of lithium-ion batteries

Global sensitivity analysis of the single particle lithium-ion battery model with electrolyte

The importance of global sensitivity analysis (GSA) has been well established in many scientific areas. However, despite its critical role in evaluating a model’s plausibility and relevance, most lithium ion battery models are published without any sensitivity analysis. In order to improve the lifetime performance of battery packs, researchers are investigating the application of physics based electrochemical models, such as the single particle model with electrolyte (SPMe). This is a challenging research area from both the parameter estimation and modelling perspective. One key challenge is the number of unknown parameters: the SPMe contains 31 parameters, many of which are themselves non-linear functions of other parameters. As such, relatively few authors have tackled this parameter estimation problem. This is exacerbated because there are no GSAs of the SPMe which have been published previously. This article addresses this gap in the literature and identifies the most sensitive parameter, preventing time being wasted on refining parameters which the output is insensitive to

On the possibility of extending the lifetime of lithium-ion batteries through optimal V2G facilitated by a flexible integrated vehicle and smart-grid system

Renewable energies are a key pillar of power sector decarbonisation. Due to the variability and uncertainty they add however, there is an increased need for energy storage. This adds additional infrastructure costs to a degree that is unviable: for an optimal case of 15GW of storage by 2030, the cost of storage is circa: £1000/kW. A promising solution to this problem is to use the batteries contained within electric vehicles (EVs) equipped with bi-directional charging systems to facilitate ancillary services such as frequency regulation and load balancing through vehicle to grid (V2G) technologies. Some authors have however dismissed V2G as economically unviable claiming the cost of battery degradation is larger than arbitrage. To thoroughly address the viability of V2G technologies, in this work we develop a comprehensive battery degradation model based on long-term ageing data collected from more than fifty long-term degradation experiments on commercial C6/LiNiCoAlO2 batteries. The comprehensive model accounts for all established modes of degradation including calendar age, capacity throughput, temperature, state of charge, depth of discharge and current rate. The model is validated using six operationally diverse real-world usage cycles and shows an average maximum transient error of 4.6% in capacity loss estimates and 5.1% in resistance rise estimates for over a year of cycling. This validated, comprehensive battery ageing model has been integrated into a smart grid algorithm that is designed to minimise battery degradation. We show that an EV connected to this smart-grid system can accommodate the demand of the power network with an increased share of clean renewable energy, but more profoundly that the smart grid is able to extend the life of the EV battery beyond the case in which there is no V2G. Extensive simulation results indicate that if a daily drive cycle consumes between 21% and 38% state of charge, then discharging 40% to 8% of the batteries state of charge to the grid can reduce capacity fade by approximately 6% and power fade by 3% over a three month period. The smart-grid optimisation was used to investigate a case study of the electricity demand for a representative University office building. Results suggest that the smart-grid formulation is able to reduce the EVs’ battery pack capacity fade by up to 9.1% and power fade by up to 12.1%

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

Design of experiments applied to lithium-ion batteries : a literature review

The statistical design of experiments methodology (DoE) has been a valuable tool since its conception for the understanding of the relationship between factors and responses. Although it has been employed successfully in different research fields and industries for years, its application to the evaluation of lithium-ion batteries (LIBs) is just getting recognition. LIBs are one of the most promising technologies for a complete transition to sustainable energies, are the main technology behind electric vehicles and are fundamental for the continual development of portable electronic devices. This paper presents a critical literature review of the available DoE works applied to the manufacturing and characterisation of LIBs. An overview of DoE and the most important available designs are first presented, followed by a general introduction of the statistical analysis required for the interpretation of the results including regression models. Several aspects of the LIBs such as ageing, capacity, electrode formulation, active material synthesis, thermal design, charging and parameterisation are discussed based on the main objective of the respective DoE studies found in the literature. A case study is presented to visualise the practical application of DoE to the LIBs field. Perspectives and future outlook are given to highlight opportunities and potential areas of research in the application of traditional and modern designs to the LIB’s field. This critical review contributes to a better understanding of the DoE methodology with a focus on LIBs or LIBs related aspects which will lead to faster developments in the field