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

Water management in an alkaline fuel cell

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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

Sodium-ion battery materials and electrochemical properties reviewed

The demand for electrochemical energy storage technologies is rapidly increasing due to the proliferation of renewable energy sources and the emerging markets of grid- scale battery applications. The properties of batteries and electrochemical energy storage (EES) technologies ideal for most of these applications, yet, faced with resource constraints, the ability of current lithium-ion batteries (LIB) to match this overwhelming demand is uncertain. Sodium-ion batteries (SIB) are a novel class of batteries with similar performance characteristics to LIB. Since they are composed of earth abundant elements, cheaper and utility scale battery modules can be assembled. As a result of the learning curve in LIB technology, a phenomenal progression in material development has been realised in the SIB concept. In this SIB review, various innovative strategies used in material development, as well as the electrochemical properties of possible anode, cathode and electrolyte combinations are unravelled. Attractive performance characteristics are herein evidenced, based on comparative gravimetric and volumetric energy densities to state-of-the-art LIB. Furthermore, opportunities and challenges towards commercialization are herein discussed. Combined with more industrial adaptations, the commercial prospects of SIB look promising and this challenging new technology is set to play a major role in grid-scale EES applications

A Hybrid backward Euler Control Volume Method To Solve The Concentration-dependent Solid-State Diffusion Problem in Battery Modeling

Several efficient analytical methods have been developed to solve the solid-state diffusion problem, for constant diffusion coefficient problems. However, these methods cannot be applied for concentration-dependent diffusion coefficient problems and numerical methods are used instead. Herein, grid-based numerical methods derived from the control volume discretization are presented to resolve the characteristic nonlinear system of partial differential equations. A novel hybrid backward Euler control volume (HBECV) method is presented which requires only one iteration to reach an implicit solution. The HBECV results are shown to be stable and accurate for a moderate number of grid points. The computational speed and accuracy of the HBECV, justify its use in battery simulations, in which the solid-state diffusion coefficient is a strong function of the concentration

From Li‐Ion Batteries toward Na‐Ion Chemistries: Challenges and Opportunities

Among the existing energy storage technologies, lithium‐ion batteries (LIBs) have unmatched energy density and versatility. From the time of their first commercialization in 1991, the growth in LIBs has been driven by portable devices. In recent years, however, large‐scale electric vehicle and stationary applications have emerged. Because LIB raw material deposits are unevenly distributed and prone to price fluctuations, these large‐scale applications have put unprecedented pressure on the LIB value chain, resulting in the need for alternative energy storage chemistries. The sodium‐ion battery (SIB) chemistry is one of the most promising “beyond‐lithium” energy storage technologies. Herein, the prospects and key challenges for the commercialization of SIBs are discussed. By comparing the technological evolutions of both LIBs and SIBs, key differences between the two battery chemistries are unraveled. Based on outstanding results in power, cyclability, and safety, the path toward SIB commercialization is seen imminent

Physics-based modeling of sodium-ion batteries part II. Model and validation

Sodium-ion batteries (SIBs) have recently been proclaimed as the frontrunner 'post lithium' energy storage technology. This is because SIBs share similar performance metrics with lithium-ion batteries, and sodium is 1000 times more abundant than lithium. In order to understand the electrochemical characteristics of SIBs and improve present-day designs, physics-based models are necessary. Herein, a physics-based, pseudo-two-dimensional (P2D) model is introduced for SIBs for the first time. The P2D SIB model is based on Na3V2(PO4)2F3 (NVPF) and hard carbon (HC) as positive and negative electrodes, respectively. Charge transfer in the NVPF and HC electrodes is described by concentration-dependent diffusion coefficients and kinetic rate constants. Parametrization of the model is based on experimental data and genetic algorithm optimization. It is shown that the model is highly accurate in predicting the discharge profiles of full cell HC//NVPF SIBs. In addition, internal battery states, such as the individual electrode potentials and concentrations, can be obtained from the model at applied currents. Several key challenges in both electrodes and the electrolyte are herein unraveled, and useful design considerations to improve the performance of SIBs are highlighted