7 research outputs found
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
Unlocking Extra Value from Grid Batteries Using Advanced Models
Lithium-ion batteries are increasingly being deployed in liberalised
electricity systems, where their use is driven by economic optimisation in a
specific market context. However, battery degradation depends strongly on
operational profile, and this is particularly variable in energy trading
applications. Here, we present results from a year-long experiment where pairs
of batteries were cycled with profiles calculated by solving an economic
optimisation problem for wholesale energy trading, including a
physically-motivated degradation model as a constraint. The results confirm the
conclusions of previous simulations and show that this approach can increase
revenue by 20% whilst simultaneously decreasing degradation by 30% compared to
existing methods. Analysis of the data shows that conventional approaches
cannot increase the number of cycles a battery can manage over its lifetime,
but the physics-based approach increases the lifetime both in terms of years
and number of cycles, as well as the revenue per year, increasing the possible
lifetime revenue by 70%. Finally, the results demonstrate the economic impact
of model inaccuracies, showing that the physics-based model can reduce the
discrepancy in the overall business case from 170% to 13%. There is potential
to unlock significant extra performance using control engineering incorporating
physical models of battery ageing
Detection and Isolation of Small Faults in Lithium-Ion Batteries via the Asymptotic Local Approach
This contribution presents a diagnosis scheme for batteries to detect and
isolate internal faults in the form of small parameter changes. This scheme is
based on an electrochemical reduced-order model of the battery, which allows
the inclusion of physically meaningful faults that might affect the battery
performance. The sensitivity properties of the model are analyzed. The model is
then used to compute residuals based on an unscented Kalman filter. Primary
residuals and a limiting covariance matrix are obtained thanks to the local
approach, allowing for fault detection and isolation by chi-squared statistical
tests. Results show that faults resulting in limited 0.15% capacity and 0.004%
power fade can be effectively detected by the local approach. The algorithm is
also able to correctly isolate faults related with sensitive parameters,
whereas parameters with low sensitivity or linearly correlated are more
difficult to precise.Comment: 8 pages, 2 figures, 3 tables, conferenc
Oxford energy trading battery degradation dataset: Data associated with paper "Unlocking Extra Value from Grid Batteries Using Advanced Models"
Battery degradation data for energy trading with physical models contains data collected from a year-long experiment where six lithium-ion cells were following current profiles corresponding to real-world usage profiles. The profiles were designed for grid-connected batteries trading power on the day-ahead wholesale market. The data set contains monthly capacity measurements as well as measurements of current, voltage and temperature while the cells were being cycled. See Readme.txt for a full description of the data and the licence under which it is made available
Spectral_li-ion_SPM: Initial release
Spectral li-ion SPM is a MATLAB code that solves the so-called lithium-ion battery Single Particle Model (SPM) using spectral numerical methods