Battery Mathematical Model Research Articles

Control of Fuel Lithium Battery Hybrid Vehicle Power Supply System and Vehicle Energy Management Strategy

<p>The main research object of this article is the increasing number of new energy vehicles, focusing on the energy management issues of new energy vehicles. Firstly, a real car model was established: an engine mathematical model, an electric motor mathematical model, and a power battery mathematical model. Then, in order to achieve optimal management of vehicle energy management, an algorithm model for vehicle energy dynamic planning was established with the goal of minimizing energy consumption. Finally, simulation experiments were conducted to verify the performance improvement of the proposed algorithm in vehicle energy management.</p> <p> </p>

Machine learning-based model development for battery state of charge–open circuit voltage relationship using regression techniques

The development of the Battery Management System mainly depends on an accurate equivalent circuit of the battery model which can be used for the estimation of State of Charge (SoC) and State of Health (SoH). The accuracy of the predicted battery model mainly depends on the exactness of the SoC–Open Circuit Voltage (OCV) relationship which is obtained through the Static Test. Numerous research works reported on various higher-order SoC–OCV battery mathematical models which are unsuitable for real-time applications. Also, there is no unique model for the estimation of battery parameters. This work proposes a Machine Learning (ML) based battery static SoC–OCV prediction method using the concept of supervised learning. The performance of the proposed ML based techniques is compared with the conventional curve fitting techniques. The real-time data of Lithium Ion battery at different temperature profiles are used to analyze the performance of the proposed techniques. The Root Mean Square Error (RMSE) and R-Square (R2) indices are considered in the study to validate the performance of the proposed algorithm. From the study, it is revealed that the Neural network based prediction method is found to be the best method with high R2 and low RMSE indices. The proposed method has the advantages such as simplicity and high accuracy. This predicted battery model can be used in electric vehicle applications.

Comparison study of lead-acid and lithium-ıon batteries for solar photovoltaic applications

The battery energy storage systems are very essential for maintaining constant power supply when using solar photovoltaic systems for power generation. The viability and ability of battery energy storage systems are assessed based on battery usage in Solar Photovoltaic utility grid-connected systems. The power supply quality and reliability are improved by utilizing battery energy storage technologies in conjunction with solar photovoltaic systems. This paper presents a comparative analysis of Lead-Acid Storage battery and Lithium-ion battery banks connected to a utility grid. The battery mathematical model simulation study gives their performance characteristics of these batteries under grid-connected loads. Cost-benefit analysis of battery usage for determining the best battery suitable for solar photovoltaic system applications is also presented in this paper.

Optimal Operation of a Residential Battery Energy Storage System in a Time-of-Use Pricing Environment

Premature ageing of lithium-ion battery energy storage systems (BESS) is a common problem in applications with or without renewable energy sources (RES) in the household sector. It can result to significant issues for such systems such as inability of the system to cover load demand for a long period of time. Consequently, the necessity of limiting the degradation effects at a BESS leads to the development and application of energy management strategies (EMS). In this work, EMSs are proposed in order to define optimal operation of a BESS without RES under time-of-use (ToU) tariff conditions. The objective of the developed EMSs is to reduce the capacity loss at the BESS in order to extend its lifetime expectancy and therefore increase the economic profit in the long-term. The EMSs utilize a widely used battery mathematical model which is experimentally validated for a specific BESS and a battery degradation mathematical model from the literature. Indicative simulation results of the proposed strategies are presented. The outcomes of these simulated scenarios illustrate that the objectives are achieved. The BESS operates efficiently by preventing premature ageing and ensuring higher economic profit at the long term.

Design of portable intelligent photovoltaic system

For current photovoltaic devices, the portability is poor, the intelligence of device is low, and the stability of the system and the power conversion efficiency are not high. This design uses the photovoltaic panel as the input. By setting up the battery mathematical model and simulation, the maximum power MPPT is tracked to extend the service life of the photovoltaic cell system. The constant current charging and constant voltage discharge of the battery should be as high as possible to achieve efficient conversion control. The output of the battery is boosted to a stable value through the BOOST circuit, and the DC-DC and DC-AC systems are built separately. DC-DC and DC-AC perform linear regression using the SVM training parameters for the voltage and set values of the BUCK circuit and the inverter. By adjusting the fitting parameters offline, the PWM and SPWM of the BUCK circuit and the inverter circuit are respectively adjusted to meet the use of low-power AC-DC devices. The design of the control section is based on the MSP430F149. When the CPU is idle, it enters a low-power mode to meet the low-power requirements of the PV system. The device realizes the portability of the photovoltaic system and the intelligent voltage output, and the system has a stable conversion efficiency.

Simulation-Supported Analysis of Calendering Impacts on the Performance of Lithium-Ion-Batteries

Lithium-Ion-Batteries (LIB) are promising energy storages for electric vehicles. However, to realize driving ranges equivalent to ones of combustion engine powered vehicles they need increased energy densities. The cells could be improved by targeted optimization of certain electrode manufacturing parameters, such as the calendering strenght [1]. However, calendering does not only affect several geometric parameters of the LIB, like electrode thicknesses and porosities, which can experimentally be measured. Calendering also affects other parameters, such as effective electronic and ionic conductivities and solid-liquid interfacial area [1, 2, 3]. The latter parameters are more complex to investigate, and exclusively experimental or exclusively simulative methods which are usually applied, are not sufficient to understand calendering impacts on battery behaviour. Therefore in this talk we introduce a method where experimental and simulative methods are combined in order to improve the understanding of calendering impact. For the experimental part inhouse made pouchcells were examined by measuring capacity, discharge curves at 1 C and electrochemical impedance spectroscopy. To investigate calendering impacts, these experiments were carried out for cells containing non-calendered NMC cathodes and ones containing cathodes calendered to a degree of 22% compaction of their original thickness, which is a moderate calendering strenght where no electrode damage by compression forces is expected, respectively. Additionally the samples' electronic conductivity was measured. To achieve consistency between experiments and simulation we implemented a pseudo 2D physico-chemical model [4, 5] which enables to simulate discharge curves and Li transport within solid and liquid during discharge process. This model was parameterized [6] and validated to achieve agreement between experimental and simulated discharge curves of pouchcells. Then impacts of the particular parameters affected by calendering (Figure 1 a) were simulated and evaluated stepwisely. With the aid of simulation-supported analysis we show to what extend each of the particular parameters described above contributes to the impact of calendering (Figure 1 b). Geometric parameters, being namely electrode thickness and porosity contribute relatively sparsely, whereas the most significant impact is caused as calendering improves effective ionic and electronic conductivities and solid-liquid interfacial area. Larger electrode thickness and porosity in non-calendered electrodes only cause a slight voltage loss, whereas lower effective ionic and electronic conductivities in non-calendered electrodes not only cause additional voltage losses but also lead to capacity losses due to kinetic limitations of electron and Li ion transport at 1 C discharge. Furthermore the effective solid-liquid interfacial area where electrons and Li ions are exchanged between electrolyte and active material particles appears to be reduced in non-calendered electrodes. Contributions of these effects can be quantified and corresponding cell internal correlations can be analyzed by additionally investigating simulated Li transport within electrolyte and active material. Especially for LIB, where electrodes are complex particle-pore networks with solid and liquid diffusion in addition of binder and carbon black, simulation-supported investigation is shown to yield physically sound correlations between calendering and battery performance. The method presented in this work is also a useful approach to achieve a targeted optimization of LIB manufacturing parameters in order to improve crucial performance quantities, such as the energy density of automotive batteries. Figure 1: Parameters affected by calendering and their particular impacts on electrochemical performance at 1 C [1] W. Haselrieder, S. Ivanov, D. K. Christen, H. Bockholt, A. Kwade, Impact of the Calendering Process on the Interfacial Structure and the Related Electrochemical Performance of Secondary Lithium-Ion Batteries, ECS Transactions 50 (26) (2013) 59-70. [2] G.-F. Yang, S.-K. Joo, Calendering effect on the electrochemical performances of the thick Li-ion battery electrodes using a three dimensional Ni alloy foam current collector, Electrochimica Acta 170 (2015) 263-268. [3] H. Zheng, L. Tan, G. Liu, X. Song, V.S. Battaglia Calendering effects on the physical and electrochemical properties of Li[Ni1/3Mn1/3Co1/3]O2cathode, Journal of Power Sources 208 (2012), 52-57. [4] J. Newman, W. Tiedemann Porous-Electrode Theory with Battery Applications, AlChE Journal 21(1) (1975) 25-41. [5] N. Legrand, S. Rael, B. Knosp, M. Hinaje, P. Desprez, F. Lapicque, Including double-layer capacitance in lithium-ion battery mathematical models, Journal of Power Sources 251 (2014) 370-378. [6] G. Lenze, N. Lin, U. Krewer, Analysis of Parameterization Steps for a Physico-Chemical Lithium-Ion-Battery Model, ModVal12, Freiburg, Germany, Mar. 31 – Apr. 1, 2015. Figure 1

Including double-layer capacitance in lithium-ion battery mathematical models

A fundamental model of lithium-ion battery cell based on Newman's works is presented in this article, including migration and diffusion of species and charges in electrodes and electrolyte. The particularity of our work is that the double layer capacitance, which is commonly neglected, is integrated in the model. We especially emphasize how to include correctly, in a physical point of view, the double layer capacitance in the electrochemical lithium-ion cell model. It is first demonstrated that the widely used mathematical formulation of the model leads to a physical error when the double layer capacitance is included, and a new formulation free of this error is proposed. Then are given the conditions for which the physical error mentioned before may cause appreciable inaccuracy in the prediction. Simulations have been carried out to validate the present analysis, and the results obtained show that the difference in cell voltage can attain several tens of millivolts.

Peukert Revisited—Critical Appraisal and Need for Modification for Lithium-Ion Batteries

The Peukert relationship was originally introduced in 1897 for lead-acid batteries and defines one of the most common parameters for battery performance evaluation. This article assesses its application for lithium-ion batteries. From the performed analysis, we can conclude that the Peukert relationship is suitable in a narrow working range such as limited current range and almost constant working temperature. Taking into account however that lithium-ion traction batteries in battery electric vehicle applications operate under strongly variable conditions, a novel relationship has been developed, allowing a more accurate description of the discharge capacity of lithium-ion batteries than the Peukert relationship does. The proposed new relationship has been derived based on comprehensive experimental analysis of the parameters that affect the battery discharge capacity and can be implemented in battery mathematical models.

Capacity Fade Mechanisms and Side Reactions in Lithium‐Ion Batteries

The capacity of a lithium‐ion battery decreases during cycling. This capacity loss or fade occurs due to several different mechanisms which are due to or are associated with unwanted side reactions that occur in these batteries. These reactions occur during overcharge or overdischarge and cause electrolyte decomposition, passive film formation, active material dissolution, and other phenomena. These capacity loss mechanisms are not included in the present lithium‐ion battery mathematical models available in the open literature. Consequently, these models cannot be used to predict cell performance during cycling and under abuse conditions. This article presents a review of the current literature on capacity fade mechanisms and attempts to describe the information needed and the directions that may be taken to include these mechanisms in advanced lithium‐ion battery models.