Estimation and forecasting battery state of health in real-world conditions using a data-driven approach

Context:
Given the increasing drive to reduce CO2 emissions, energy storage has become a dominant topic with the emergence of a larger share of renewable, intermittent generating capacity in the global energy mix. From grid-scale to mobile applications, batteries have become one the most promising alternatives to deliver an efficient energy storage solution. Consequently, addressing the issues that come with the aging of batteries has received commensurate interest - the fade of both storage capacity and ability to provide power over time is perhaps the most significant economic hurdle for battery adoption in applications ranging from grid storage to electric vehicles. The degrading state of health over aging is a problem that affects all rechargeable batteries via specific design- and chemistry-dependent mechanisms.
In a simple sense, a battery's state of health may be defined as total energy it can release upon discharge at a given point in time relative to that at the time of manufacture. Estimation and measurement of this quantity has a substantial body of existing research. However, accuracy necessitates invasive, time-consuming tests resulting in almost all analysis having been conducted on data generated in controlled laboratory conditions. For batteries operating in a real-world production environment, estimation becomes considerably more difficult as state of health can only be inferred, rather than measured from data generated under operating conditions. The difficulty is compounded by the noisiness of available data resulting from a variety of factors including sensor inaccuracy and variations in discharge current, time and temperature.
Aim:
The aim of this project is to develop methods to give an online state of health estimate for batteries operating in real-world conditions, followed by forecasting its evolution over time given the observed pattern of usage.
The main challenge is the construction of a mathematical model of battery behaviour. This serves to tie the unobserved state of health to the observed variables, consisting (exclusively) of the voltage across the battery terminals, current drawn from the battery, time and temperature. Complexity of this model is key - the model has to adequately represent reality, in that its outputs have to map to the observed variables to an acceptable degree of accuracy. On the other hand, the model has to be simple enough to be applicable over all batteries manufactured to the same specification.
Availability of large datasets is paramount and is often the limiting factor for model complexity and validation accuracy, as inferring parameters from an insufficient quantity of noisy data results in an unacceptable level of uncertainty around the model itself.
Novelty:
Leveraging the "big data" aspect allows for state-of-the-art machine learning techniques to be applied to infer potentially complex relationships in the data while maintaining model robustness. Applying machine learning to data extracted from multiple batteries across a broad range of real-world operating conditions to estimate and forecast battery state of health is an area unexplored thus far. Additionally, using real-world usage patterns as opposed to laboratory ones, it will highlight the extent to which results from the latter translate to the real operating environment.
Value:
Reliable state of health estimates and forecasts are critical to battery operators. This issue affects the economics of operating batteries over the whole life cycle of the investment, from the expectation of depreciation, to the variable cost of operation. Of immediate concern for a battery operator would be maintenance planning to minimise down time as well as customer alerts for usage patterns particularly detrimental to battery health. Ultimately, models may be built into automated battery management systems which can optimise usage to maximise economic value.

This falls under the EPSRC Energy theme.