Quantitative Analysis of Lithium-Ion Battery Capacity Prediction via Adaptive Bathtub-Shaped Function

Abstract

Batteries are one of the most important components in many mechatronics systems, as they supply power to the systems and their failures may lead to reduced performance or even catastrophic results. Therefore, the prediction analysis of remaining useful life (RUL) of batteries is very important. This paper develops a quantitative approach for battery RUL prediction using an adaptive bathtub-shaped function (ABF). ABF has been utilised to model the normalised battery cycle capacity prognostic curves, which attempt to predict the remaining battery capacity with given historical test data. An artificial fish swarm algorithm method with a variable population size (AFSAVP) is employed as the optimiser for the parameter determination of the ABF curves, in which the fitness function is defined in the form of a coefficient of determination (R2). A 4 x 2 cross-validation (CV) has been devised, and the results show that the method can work valuably for battery health management and battery life prediction

Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error

“…To fully utilize the degradation data of congeneric batteries, He et al [18] applied the Dempster-Shafer theory to evaluate the initial model parameters. Other new prognostics-related methods and models, e.g., autoregressive model, Verhulst model, fusion prognostic algorithm, adaptive bathtub-shaped function, relevance vectors, etc., can be found in [19][20][21][22][23][24][25].…”

Cycle Life of Commercial Lithium-Ion Batteries with Lithium Titanium Oxide Anodes in Electric Vehicles

“…The capacity of new batteries can be derived easily, but the capacity of aged cells should be estimated according to the battery cycle life properties. Besides, the battery remaining useful life (RUL) could be analyzed based on the battery cycle life [3].…”

Review of the Remaining Useful Life Prognostics of Vehicle Lithium-Ion Batteries Using Data-Driven Methodologies

“…Hu et al [37] put forward a nonlinear kernel regression model of lithium battery degradation, obtained degradation parameters through the K-nearest neighbor, and used PSO to optimize the weight of the K-nearest neighbor regression model. Chen et al [38] developed a quantitative approach for the battery RUL prediction based on an adaptive bathtub-shaped function and used the artificial fish swarm algorithm method to optimize the parameter model. This prognostic model can capture the dynamic behaviors of the battery capacity.…”

“…Now, existing research on lithium battery degradation modeling and RUL is aimed at fixed loads that build a relationship model of cycles and degradation through the constant current discharge to predict the RUL. Although studies [38,48,49,51] have considered several groups of lithium battery RUL prediction problems under different constant discharge currents, they did not consider the random variation of the current under actual operation process. Random-variable current affects battery degradation rate, which results in the fact that lithium battery degradation function is a nonlinear time-varying function influenced by random effects.…”