Fast computational framework for optimal life management of lithium ion batteries

Abstract

Summary The determination of optimal charging profiles over cycle life of a lithium ion battery is a challenging problem that is extremely important for commercial applications. It is a difficult problem to solve owing to the complex degradation processes occurring inside the battery. Further, modeling of a realistic battery operation, let alone the degradation mechanisms, results in computationally expensive mathematical models. In the present study, a framework is developed towards addressing this problem by (1) developing a method to formulate extremely fast and accurate algebraic models that capture essential features such as charging time and aging characteristics described by battery models and (2) utilizing these algebraic models in an optimization framework involving genetic algorithms for determining the optimal charging profiles over the cycle life of the battery. The utility of the present framework in determining the optimal charging solutions is illustrated with various real‐life usage scenarios such as fast charging and extension of cycle life. The proposed solution can be utilized onboard for generating the optimal charging profiles over cycle life of the battery.

A Joint Estimation Method Based on Kalman Filter of Battery State of Charge and State of Health

“…The accurate estimation of a battery's SOC and SOH is an important function of a battery management system [6][7][8][9], which can formulate a reasonable charge and discharge strategy for the vehicle, so that the battery can be fully and reasonably utilized and its service life can be extended. As a battery's SOC and SOH cannot be measured directly, they can be estimated based on the parameters that can be directly detected, such as the voltage, current, temperature, and so on [10,11].…”

A novel adaptive dual extended Kalman filtering algorithm for the Li‐ion battery state of charge and state of health co‐estimation

“…As Li-ion batteries often operate under complex power conditions, their state detection is easily affected by environmental noise. 4 In addition, the internal electrochemical reaction of the Li-ion battery is complicated, often accompanied by polarization effects and ohmic effects, combined with variable discharge currents under complex conditions, internal battery temperature, selfdischarge of the battery, and repeated use of materials to cause aging. 5,6 Due to the interference of other factors, accurate real-time estimation of the SOC and SOH of Liion batteries is facing greater difficulties.…”

Novel reduced-order modeling method combined with three-particle nonlinear transform unscented Kalman filtering for the battery state-of-charge estimation

“…It is of great importance to accurately estimate the state of charge (SOC) of lithium-ion batteries for maximizing their performance and for realizing real-time state detection and safety control of lithium-ion batteries [3]. Lithium-ion batteries are often used under complex working conditions, and their state detection is susceptible to environmental noise [4,5]. Moreover, the internal electrochemical reaction of the lithium-ion battery is complicated and is often accompanied by polarization effects and ohmic effects [6,7].…”