Gaussian process regression for forecasting battery state of health

Accurately predicting the future capacity and remaining useful life of batteries is necessary to ensure reliable system operation and to minimise maintenance costs. The complex nature of battery degradation has meant that mechanistic modelling of capacity fade has thus far remained intractable; however, with the advent of cloud-connected devices, data from cells in various applications is becoming increasingly available, and the feasibility of data-driven methods for battery prognostics is increasing. Here we propose Gaussian process (GP) regression for forecasting battery state of health, and highlight various advantages of GPs over other data-driven and mechanistic approaches. GPs are a type of Bayesian non-parametric method, and hence can model complex systems whilst handling uncertainty in a principled manner. Prior information can be exploited by GPs in a variety of ways: explicit mean functions can be used if the functional form of the underlying degradation model is available, and multiple-output GPs can effectively exploit correlations between data from different cells. We demonstrate the predictive capability of GPs for short-term and long-term (remaining useful life) forecasting on a selection of capacity vs. cycle datasets from lithium-ion cells.Comment: 13 pages, 7 figures, published in the Journal of Power Sources, 201

Data-driven nonparametric Li-ion battery ageing model aiming at learning from real operation data – Part A : storage operation

Conventional Li-ion battery ageing models, such as electrochemical, semi-empirical and empirical models, require a significant amount of time and experimental resources to provide accurate predictions under realistic operating conditions. At the same time, there is significant interest from industry in the introduction of new data collection telemetry technology. This implies the forthcoming availability of a significant amount of real-world battery operation data. In this context, the development of ageing models able to learn from in-field battery operation data is an interesting solution to mitigate the need for exhaustive laboratory testing

A rest time-based prognostic framework for state of health estimation of lithium-ion batteries with regeneration phenomena

State of health (SOH) prognostics is significant for safe and reliable usage of lithium-ion batteries. To accurately predict regeneration phenomena and improve long-term prediction performance of battery SOH, this paper proposes a rest time-based prognostic framework (RTPF) in which the beginning time interval of two adjacent cycles is adopted to reflect the rest time. In this framework, SOH values of regeneration cycles, the number of cycles in regeneration regions and global degradation trends are extracted from raw SOH time series and predicted respectively, and then the three sets of prediction results are integrated to calculate the final overall SOH prediction values. Regeneration phenomena can be found by support vector machine and hyperplane shift (SVM-HS) model by detecting long beginning time intervals. Gaussian process (GP) model is utilized to predict the global degradation trend, and nonlinear models are utilized to predict the regeneration amplitude and the cycle number of each regeneration region. The proposed framework is validated through experimental data from the degradation tests of lithium-ion batteries. The results demonstrate that both the global degradation trend and the regeneration phenomena of the testing batteries can be well predicted. Moreover, compared with the published methods, more accurate SOH prediction results can be obtained under this framewor

Remaining useful life estimations applied on the sizing and the prognosis of lithium ion battery energy storage systems

The present thesis develops an accurate sizing tool for the most relevant lithium ion battery energy storage system applications considering the aging and the remaining useful life. The developed tool involves firstly, the construction of the aging models of the lithium ion battery health indicators; secondly, the calculation of the end of life based on the evolution of the modelled health indicators; thirdly, the calculation of the levelized cost of the most relevant applications of lithium ion battery energy storage systems; and fourthly, the minimization of the committed error with the constructed aging models supported by electrode level data and prognosis algorithms. The methodology behind the construction and calculation of all the elements integrated on the sizing tool is described throughout the chapters of this thesis. Firstly, the end of life state of the battery is determined as a combined threshold of all the health indicators of interest. Its calculation requires the implementation of an electro-thermal model in a simulation environment defined by the end of life criteria specified by the application requirements. Secondly, the evolution of health indicators of interest are modelled based on the most relevant stress factors. The methodology to acquire the aging data and the construction of the posterior empirical models are presented. The validation of the constructed models based on the acquired data is performed based on three aspects: the accuracy describing the observed cases, the correctness of interpolations and the real life applicability. Thirdly, the simulation environments for lithium ion battery energy storage systems applied on an electric vehicle application and on a stationary application are developed where the levelized cost of different battery solution sizes is calculated. The simulation environment integrates the already developed electric-thermal model, end of life map and aging models. Fourthly, the error done by the constructed aging models is minimized by focusing on the errors done when extrapolating in time and when facing odd events. On one hand, electrode level data is analysed to generate data artificially and reduce the errors when extrapolating in time. On the other hand, a prognosis stochastic algorithm is selected and employed with real life data to deal with the effect that odd events have on the evolution of the health indicators. The validity of many assumptions made for the development of the end of life map, the aging models, the simulation environment used on the sizing tool, the artificial data generator and the real time prognosis tool are proved experimentally

State of health estimation of Li-ion batteries with regeneration phenomena: a similar rest time-based prognostic framework

State of health (SOH) prediction in Li-ion batteries plays an important role in intelligent battery management systems (BMS). However, the existence of capacity regeneration phenomena remains a great challenge for accurately predicting the battery SOH. This paper proposes a novel prognostic framework to predict the regeneration phenomena of the current battery using the data of a historical battery. The global degradation trend and regeneration phenomena (characterized by regeneration amplitude and regeneration cycle number) of the current battery are extracted from its raw SOH time series. Moreover, regeneration information of the historical battery derived from corresponding raw SOH data is utilized in this framework. The global degradation trend and regeneration phenomena of the current battery are predicted, and then the prediction results are integrated together to calculate the overall SOH prediction values. Particle swarm optimization (PSO) is employed to obtain an appropriate regeneration threshold for the historical battery. Gaussian process (GP) model is adopted to predict the global degradation trend, and linear models are utilized to predict the regeneration amplitude and the cycle number of each regeneration region. The proposed framework is validated using experimental data from the degradation tests of Li-ion batteries. The results demonstrate that both the global degradation trend and the regeneration phenomena of the testing batteries can be well predicted. Moreover, compared with the published methods, more accurate SOH prediction results can be obtained under this framewor