Battery health prediction under generalized conditions using a Gaussian process transition model

Accurately predicting the future health of batteries is necessaryElectrochemical batteries, such as lithium-ion and leadacid to ensure reliable operation, minimise maintenance cells, experience degradation over time and during costs, and calculate the value of energy storage investments.usage, leading to decreased energy storage capacity and The complex nature of degradation renders datadrivenincreased internal resistance. Being able to predict the approaches a promising alternative to mechanistic rate of degradation and the remaining useful life (RUL) modelling. This study predicts the changes in batteryof a battery is important for performance and economic capacity over time using a Bayesian nonparametric reasons. For example, in an electric vehicle, the driveable approach based on Gaussian process regression. These range is directly related to the battery capacity. For energy changes can be integrated against an arbitrary input sequence storage asset valuation, depreciation, warranty, insurance to predict capacity fade in a variety of usage scenarios, and preventative maintenance purposes, predicting forming a generalised health model. The approach RUL at design stage and during operation is crucial, and naturally incorporates varying current, voltage and temperaturethe investment case is strongly dependent on the degradation inputs, crucial for enabling real world application.