Design and implementation of machine learning algorithms for degradation estimation of Lithium-ion batteries and electrochemical capacitors

Abstract

Lithium-ion (Li-ion) batteries and electrochemical capacitors (EC) are the preferred energy storage technology in stationary storage systems as well as other critical applications to the decarbonisation of our infrastructure including electrical vehicles, busses, trains and planes. The gradual degradation of energy storage devices, however, is a major concern for the adoption of storage technology across a wide range of industries. Li-ion batteries and EC cells continuously degrade with time. Their degradation rate is highly dependent on their construction and the wide variation in operating conditions. Given that a cell’s degradation is an unavoidable effect, it is critical to understand its degradation and predict its lifetime to ensure resilience and reliability. First, the research in this thesis concentrates on understanding which factors affect the degradation of Lithium-ion batteries. Whilst the operating temperature is regarded as one of the highest stress factors accelerating battery degradation, the effect of other stress factors such as discharge and charge current, charge cut-off current and depth of discharge, is an understudied topic. The research in this thesis describes a half-factorial design of experiment (16 test cases) consisting of a total of 96 batteries from two manufactures. Two machine learning algorithms, random forest and lasso regression are trained on the generated data and subsequently ranked all five operational factors and their two-way interaction. The results indicate that the two-way interaction effects of charge current and depth of discharge are in the top 3 significant stress factors for the capacity fade in Li-ion batteries, which was previously not commonly accepted knowledge in the battery literature. Secondly, in this thesis, EC degradation at extreme temperatures is investigated due to a lack of studies addressing EC cell operation outside manufacturer specified temperature operating envelope. This is critical for EC operation in extreme environments such as drilling where temperatures can reach up to 200 °C. The research first develops a design of experiment approach to generate data for EC degradation under high-temperature conditions ranging from 80°C to 200 °C. The obtained data is then used as input to a Gaussian process algorithm that estimates cell degradation in pre-specified temperature conditions with a mean absolute per cent error of 1.47%. Finally, the thesis addresses the problem of state of health estimation of Li-ion battery cells. State of health is a variable that characterises the condition of an energy storage device throughout its lifetime and is typically measured as capacity fade or resistance increases. SOH estimation is critical to recognise a sudden degradation of a battery cell and greatly affects battery state of charge (SOC) calculation. Therefore, the research proposes a battery chemistry and design agnostic machine learning pipeline. The method uses Li-ion battery charge curves as input and estimates degradation measured as capacity. This eliminates the need for time-consuming and computationally expensive physics of failure and electrochemical models traditionally used for SOH estimation purposes. The pipeline operates by passing incoming data streams through a hierarchical sequence of processing steps to fuse them into a model. Each step of the pipeline has the goal of eliminating or minimising the typical disadvantages plaguing machine learning-based algorithms to date. Namely, the pipeline engineers feature by summarising each incoming charge curve data stream to one single value, thus becoming robust to different data captures. In the case of limited training data, the pipeline introduces adversarial examples, minimising overfitting. Additionally, the pipeline also associates a confidence interval with each SOH estimation and re-calibrates the models. Results indicated that when deployed on Li-ion batteries subjected to a fast-charge protocol, the pipeline achieved a mean absolute percent error of 0.45%. Overall, the research in this thesis highlights the promise of combining machine learning models with a design of experiment-based data generation phase for a better understating of degradation of complex dynamic electrochemical systems such as lithium-ion battery and electrochemical capacitors. Furthermore, the proposed methodology accurately estimates state of health of complex non-linear systems and has the capability to scale to other energy storage devices in the future.