Comparison of different approaches for lifetime prediction of electrochemical systems—Using lead-acid batteries as example

By Dirk Uwe Sauer, H. Wenzl. 2008

300 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/2a645369d0912a8d56e2357b23a570d8280213e3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Battery State-of-Health Perceptive Energy Management for Hybrid Electric Vehicles (2012, background): “2) State-of-Health: Capacity fade models of batteries are commonly divided into three categories [23]: 1) electrochemical models; 2) event-based models; and 3) energy-throughputbased models.”
Economics of Residential Photovoltaic Battery Systems in Germany: The Case of Tesla’s Powerwall (2016, background): “Experimentally gained Wöhler-curves (also referred to as S/N-curves) describe the amount of stress cycles related to the applied force onto the material, until it fails [18].”
Economic Analysis of the Dynamic Charging Electric Vehicle (2015, methodology): “Although the fatigue model is a high-level approximation for the battery life, it is widely used due to its simplicity and succinctness [32].”
Smart households: Dispatch strategies and economic analysis of distributed energy storage for residential peak shaving (2015, background): “Lifetime prediction models vary with different battery technologies [36-40].”
Influence of Battery/Ultracapacitor Energy-Storage Sizing on Battery Lifetime in a Fuel Cell Hybrid Electric Vehicle (2009, background): “The loss of lifetime of the battery is defined by [21]”
Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming (2018, methodology): “The blue1 curve shows the number of expected cycles (NPOS) in battery life time depending on DOD and the red curve shows equivalent full cycles in BSS operation.”