Lithium-ion battery models: a comparative study and a model-based powerline communication

Abstract

Abstract. In this work, various Lithium-ion (Li-ion) battery models are evaluated according to their accuracy, complexity and physical interpretability. An initial classification into physical, empirical and abstract models is introduced. Also known as "white", "black" and "grey" boxes, respectively, the nature and characteristics of these model types are compared. Since the Li-ion battery cell is a thermo-electrochemical system, the models are either in the thermal or in the electrochemical state-space. Physical models attempt to capture key features of the physical process inside the cell. Empirical models describe the system with empirical parameters offering poor analytical, whereas abstract models provide an alternative representation.In addition, a model selection guideline is proposed based on applications and design requirements. A complex model with a detailed analytical insight is of use for battery designers but impractical for real-time applications and in situ diagnosis. In automotive applications, an abstract model reproducing the battery behavior in an equivalent but more practical form, mainly as an equivalent circuit diagram, is recommended for the purpose of battery management. As a general rule, a trade-off should be reached between the high fidelity and the computational feasibility. Especially if the model is embedded in a real-time monitoring unit such as a microprocessor or a FPGA, the calculation time and memory requirements rise dramatically with a higher number of parameters.Moreover, examples of equivalent circuit models of Lithium-ion batteries are covered. Equivalent circuit topologies are introduced and compared according to the previously introduced criteria. An experimental sequence to model a 20 Ah cell is presented and the results are used for the purposes of powerline communication.

A Circular Economy of Electrochemical Energy Storage Systems: Critical Review of SOH/RUL Estimation Methods for Second-Life Batteries

“…Similar categorizations can be found in the literature for RUL estimation methods and have been organized as adaptive filter, intelligent, and stochastic techniques [28]. Particularly, the classifications made by Saidani et al [33] and Liao et al [34] are interesting as they introduce a comprehensible way to group both SOH and RUL estimation methods in three categories, based on system theory concepts: white-box, black-box, and gray-box methods (see Figure 3). In general, these concepts refer to the level of theoretical or experimental knowledge needed to describe or model a process.…”

“…This model exemplifies two of the main disadvantages of white-box methods: the need to estimate a lot of parameters and the solution of complex PDE systems. Most of the times, white-box methods derive results that are not costeffective [33,37].…”

Critical Review of Intelligent Battery Systems: Challenges, Implementation, and Potential for Electric Vehicles

“…Saleem et al [591] present a BMS with UART over power line with FSK, but the validation comprises only three nodes. Saidani et al [592] developed a new PLC concept where an AC current source in parallel to the cell is used instead of an inductive or capacitive isolator. Unfortunately, the cell impedance is too small to obtain the necessary amplitude.…”

Soft Sensors for State of Charge, State of Energy, and Power Loss in Formula Student Electric Vehicle

“…For cell parameter estimation, the series internal resistance (R 0 ) was first approximated as per the direct current resistance (DCR) −% SOC characteristics prescribed in the A123 manufacturer design guide (based on actual experiments) [46]. The remaining cell parameters, namely, parallel internal resistance (R 1 ) and capacitance (C 1 ), were determined by mutual substitution in the following equations (also derived from actual experiments) [47,48]…”