Structural Identifiability of Impedance Spectroscopy Fractional-Order Equivalent Circuit Models With Two Constant Phase Elements

Structural identifiability analysis of fractional-order equivalent circuit models (FO-ECMs), obtained through electrochemical impedance spectroscopy (EIS) is still a challenging problem. No peer-reviewed analytical or numerical proof does exist showing that whether impedance spectroscopy FO-ECMs are structurally identifiable or not, regardless of practical issues such as measurement noises and the selection of excitation signals. By using the coefficient mapping technique, this paper proposes novel computationally-efficient algebraic equations for the numerical structural identifiability analysis of a widely used FO-ECM with Gr\"{u}nwald-Letnikov fractional derivative approximation and two constant phase elements (CPEs) including the Warburg term. The proposed numerical structural identifiability analysis method is applied to an example from batteries, and the results are discussed. Matlab codes are available on github

Novel Fitting Algorithm for Parametrization of Equivalent Circuit Model of Li-Ion Battery from Broadband Impedance Measurements

The impedance of Li-ion batteries contains information about the dynamics and state parameters of the battery. This information can be utilized to improve the performance and safety of the battery application. The battery impedance is typically modeled by an equivalent-circuit-model (ECM) which provides the dynamic information of the battery. In addition, the variations in the model parameters can be used for the battery state-estimation. A fitting algorithm is required to parametrize the ECM due to the non-linearity of both the battery impedance and ECM. However, conventional fitting algorithms, such as the complex-nonlinear-least-squares (CNLS) algorithm, often have a high computational burden and require selection of initial conditions which can be difficult to obtain adaptively. This paper proposes a novel fitting algorithm for the parametrization of battery ECM based on the geometric shape of the battery impedance in the complex-plane. The algorithm is applied to practical and fast broadband pseudo random sequence impedance measurements carried out at various state-of-charges (SOC) and temperatures for lithium-iron-phosphate cell. The performance of the method is compared to conventional CNLS algorithm with different initial conditions. The results show that the proposed method provides fast and accurate fit with low computational effort. Moreover, specific ECM parameters are found to be dependent on the battery SOC at various temperature.publishedVersionPeer reviewe

Design, Implementation and Validation of a Hardware-in-the-Loop Test Bench for Heating Systems in Conventional Coaches

Experimental work with heating systems installed in public transport vehicles, particularly for optimisation and control design, is a challenging task due to cost and space limitations, primarily imposed by the heating hardware and the need to have a real vehicle available. In this work, a hybrid hardware-in-the-loop (HIL) test bench for heating systems in conventional coaches is introduced. This approach consists of a hardware system made up of the main heating components, assembled as a lab experimental plant, along with a simulation component including a cabin thermal model, both exchanging real-time data using a standard communication protocol. This scheme presents great flexibility regarding data logging for further analysis and easily changing the experimental operational conditions and disturbances under different scenarios (i.e., solar irradiance, outside temperature, water temperature from the engine cooling circuit, number of passengers, etc.). Comparisons between the hybrid system’s transient and steady-state responses and those from selected experiments conducted on an actual coach allowed us to conclude that the proposed system is a suitable test bed to aid in optimisation and design tasks. In this context, several closed-loop test experiments using the test bench were additionally carried out to assess the performance of the proposed control system

Data-Driven Methods for the State of Charge Estimation of Lithium-Ion Batteries: An Overview

In recent years, there has been a noticeable shift towards electric mobility and an increasing emphasis on integrating renewable energy sources. Consequently, batteries and their management have been prominent in this context. A vital aspect of the BMS revolves around accurately determining the battery pack’s SOC. Notably, the advent of advanced microcontrollers and the availability of extensive datasets have contributed to the growing popularity and practicality of data-driven methodologies. This study examines the developments in SOC estimation over the past half-decade, explicitly focusing on data-driven estimation techniques. It comprehensively assesses the performance of each algorithm, considering the type of battery and various operational conditions. Additionally, intricate details concerning the models’ hyperparameters, including the number of layers, type of optimiser, and neuron, are provided for thorough examination. Most of the models analysed in the paper demonstrate strong performance, with both the MAE and RMSE for the estimation of SOC hovering around 2% or even lower

Fast Charging Technique For Lithium-Ion Cell

Climate change is a big concern among the people. Day by day people are trying to increase the use of the sustainable energy in every sector of their life. Like other sectors, transportation sector manufacturers are beginning to shifting from fossil fuels based models to electric models. Manufacturers are trying to introduce electrified models from bicycles to cars. For energy storage, these electrified models are highly dependent on the battery. Lithium-ion cells have a high energy density, no memory effect, long cycle life and low self discharge quality, and are therefor highly used from portable electronics to electric vehicles everywhere. A main concern with a rechargeable battery is that it needs to recharge in regular intervals. This charging procedure is time consuming and can have a great impact on the total capacity of the battery, cycle life, and charging efficiency (or energy efficiency). A gasoline-base vehicle takes 3-5 minutes to fill the gas tank, but, an electric vehicle may need up to 10-12 hours (depend on the battery pack capacity) to be fully charged. For that, electric vehicles can become unreliable under emergency conditions and a deterent to regular users. As a result, charging technology has become a major concern among the manufacturers of electric vehicles. Using fast charging techniques can create unwanted side effects, like, thermal runaway, capacity fade, lithium platting and other electrochemical changes. In this thesis we developed an optimal fast charging technique for lithium-ion cells, which will be able to charge the cell faster compared with present industrial charging methods and maintain the long cycle life without significant decay of the capcity. We used 18650 lithium-ion cells for testing. During testing continuous cycling test was stopped when the capacity degraded by 20% of it’s original capacity. We compared our proposed fast charging technique with an available industrial charging technique. Due to differences in the charging times, when our proposed fast charging technique goes through more than 1600 cycles, the industrial charging technique had completed only 660 cycles. For comparision purposes, we chose 600 cycles as the common comparision point. We had found that our proposed technique took an average 63.7 minutes to charge 100% of the cell after 600th cycle. At the same time, the industrial charging technique took an average of 150 minutes to charge 100% of the cell. From this comparision it was clear the our proposed method is 135% faster than the available industrial charging technique. Capacity degradation was 10.5% for the fast charging technique and 6.6% for the industrial charging. As a result, we can say our proposed fast charging technique is faster and capabale of maintaining the capacity degradation rate within reasonable limits

A novel weight coefficient calculation method for the real‐time state monitoring of the lithium‐ion battery packs under the complex current variation working conditions.

A novel real-time state monitoring method is proposed to realize the real-time energy management of the lithium-ion battery packs, which is conducted in the iterative computational calculation process by introducing an improved weighting factor-unscented Kalman filtering algorithm. The accurate state monitoring treatment is investigated by applying a new iterate calculation thought, in which the improved weight coefficient parameter is constructed and its numerical stability is improved. Meanwhile, the recursive calculation is derived by using the real-time measured factors, according to which the state-of-charge estimation is realized accurately. Aiming to adapt the complex current variation working conditions, the nonlinear treatment is introduced to construct the mathematical unscented transforming function. As can be known from the experimental results, the state-of-charge estimation accuracy is 98.34% under the complex current charge-discharge working conditions. Meanwhile, the effective closed-circuit voltage trackage is also investigated accurately and its tracking error is within 3.51% in the complex working conditions, which provides a good security guarantee for the reliable energy supply of the lithium-ion battery packs

分数階微積分システムのモデリングとシミュレーション方式

東京電機大学201