Title :
Battery design based upon Life Cycle statistics
Author :
Chiodo, Elio ; Lauria, Davide ; Fabrizi, V. ; Ortenzi, F. ; Sglavo, V.
Author_Institution :
Dept. of Electr. Eng. & Inf. Technol., Univ. of Naples, Naples, Italy
Abstract :
Battery Life Cycle modelling and estimation are key challenges in the modern electric systems, also due to the development of renewable energy and electrified transportation systems exploitation: in such fields indeed the battery has emerged as the most prominent energy storage device, attracting a significant amount of studies. It is however well known that battery lifetime depends on many parameters, such as power density, specific power, energy density, operating environment, etc., so that an accurate methods to analyze this dependence, also taking into account the randomness of the above parameters in real operating conditions, is a necessary even if difficult task. In this framework, in the paper, after a thorough statistical data analysis, a probabilistic method for battery design is proposed which ensures the optimization of a suitable cost function which has the expected lifetime as a basic input. The method takes properly into account, in particular, the random variations in specific power experienced by a lead-acid battery. The statistical features of lifetime are also explored by means of a large series of numerical simulations based upon suitable probability distributions which may characterize the above operating conditions, in order to obtain an efficient estimation of the above lifetime distribution.
Keywords :
lead acid batteries; life cycle costing; probability; statistical analysis; battery design; battery life cycle estimation; battery life cycle modelling; cost function optimization; electric systems; electrified transportation systems exploitation; energy storage device; lead-acid battery; life cycle statistics; lifetime distribution; numerical simulations; probabilistic method; probability distributions; random variations; renewable energy development; statistical data analysis; Lead-acid Batteries; electric vehicles; experimental measurements; life cycle estimation; probability distributions;
Conference_Titel :
Renewable Power Generation Conference (RPG 2014), 3rd
Conference_Location :
Naples
DOI :
10.1049/cp.2014.0876