A smart high-voltage cell detecting and equalizing circuit for LiFePO4 batteries in electric vehicles

A battery management system (BMS) plays an important role in electric vehicles (EVs) in order to achieve a reasonable-lasting lifetime. An equalizing method is essential in order to obtain the best performance. A monitoring system is required to check if any cell voltage is high or low. In this paper, an equalizing and monitoring system for an ultra-light electric vehicle is proposed. The monitoring system detects if one cell is fully charged or all cells are fully charged and the equalizing system tops each cell at the desired voltage. To solve this issue, a light-emitting diode (LED) band gap is used as a voltage reference to inform the user if any cell is at its high voltage. A smart monitoring displays on the liquid crystal display (LCD), if one cell is high or all cells are high. This detection also provides a signal to the microcontroller to turn on/off the charger if all cells are high. Also, a Bluetooth module was designed to command the microcontroller the charger to turn on/off via voice/text message by using a smartphone. Additionally, a new smart monitoring system based on the Bluetooth model (HC05) and mobile app has been made in order to monitor individual cell voltage. A major feature of the system is to draw a very-low current, so that the system does not contribute significantly to the self-discharge of the battery and the circuit does not need sophisticated control. Manufacturers of large electric vehicles may have more intelligent systems that may require a permanent connection to the grid and allow high standby losses, where more state of charge (SOC) may be lost per day. The paper is rather focused on reducing the standby losses, and to activate the equalizer only when charging and/or driving. The experimental results are performed in order to verify the feasibility of the proposed circuit

Estimation Accuracy and Computational Cost Analysis of Artificial Neural Networks for State of Charge Estimation in Lithium Batteries

This paper presents a tradeoff analysis in terms of accuracy and computational cost between different architectures of artificial neural networks for the State of Charge (SOC) estimation of lithium batteries in hybrid and electric vehicles. The considered layouts are partly selected from the literature on SOC estimation, and partly are novel proposals that have been demonstrated to be effective in executing estimation tasks in other engineering fields. One of the architectures, the Nonlinear Autoregressive Neural Network with Exogenous Input (NARX), is presented with an unconventional layout that exploits a preliminary routine, which allows setting of the feedback initial value to avoid estimation divergence. The presented solutions are compared in terms of estimation accuracy, duration of the training process, robustness to the noise in the current measurement, and to the inaccuracy on the initial estimation. Moreover, the algorithms are implemented on an electronic control unit in serial communication with a computer, which emulates a real vehicle, so as to compare their computational costs. The proposed unconventional NARX architecture outperforms the other solutions. The battery pack that is used to design and test the networks is a 20 kW pack for a mild hybrid electric vehicle, whilst the adopted training, validation and test datasets are obtained from the driving cycles of a real car and from standard profiles

State of Charge Estimation for Lithium-Ion Battery by Using Dual Square Root Cubature Kalman Filter

The state of charge (SOC) plays an important role in battery management systems (BMS). However, SOC cannot be measured directly and an accurate state estimation is difficult to obtain due to the nonlinear battery characteristics. In this paper, a method of SOC estimation with parameter updating by using the dual square root cubature Kalman filter (DSRCKF) is proposed. The proposed method has been validated experimentally and the results are compared with dual extended Kalman filter (DEKF) and dual square root unscented Kalman filter (DSRUKF) methods. Experimental results have shown that the proposed method has the most balance performance among them in terms of the SOC estimation accuracy, execution time, and convergence rate

Toward Enhanced State of Charge Estimation of Lithium-ion Batteries Using Optimized Machine Learning Techniques.

State of charge (SOC) is a crucial index used in the assessment of electric vehicle (EV) battery storage systems. Thus, SOC estimation of lithium-ion batteries has been widely investigated because of their fast charging, long-life cycle, and high energy density characteristics. However, precise SOC assessment of lithium-ion batteries remains challenging because of their varying characteristics under different working environments. Machine learning techniques have been widely used to design an advanced SOC estimation method without the information of battery chemical reactions, battery models, internal properties, and additional filters. Here, the capacity of optimized machine learning techniques are presented toward enhanced SOC estimation in terms of learning capability, accuracy, generalization performance, and convergence speed. We validate the proposed method through lithium-ion battery experiments, EV drive cycles, temperature, noise, and aging effects. We show that the proposed method outperforms several state-of-the-art approaches in terms of accuracy, adaptability, and robustness under diverse operating conditions

Open circuit voltage and state of charge relationship functional optimization for the working state monitoring of the aerial lithium-ion battery pack.

The aerial lithium-ion battery pack works differently from the usual battery packs, the working characteristic of which is intermittent supplement charge and instantaneous large current discharge. An adaptive state of charge estimation method combined with the output voltage tracking strategy is proposed by using the reduced particle - unscented Kalman filter, which is based on the reaction mechanism and experimental characteristic analysis. The improved splice equivalent circuit model is constructed together with its state-space description, in which the operating characteristics can be obtained. The relationship function between the open circuit voltage and the state of charge is analyzed and especially optimized. The feasibility and accuracy characteristics are tested by using the aerial lithium-ion battery pack experimental samples with seven series-connected battery cells. Experimental results show that the state of charge estimation error is less than 2.00%. The proposed method achieves the state of charge estimation accurately for the aerial lithium-ion battery pack, which provides a core avenue for its high-power supply security

Are electric vehicle batteries being underused? A review of current practices and sources of circularity

The increasing demand for Lithium-ion batteries for Electric Vehicle calls for the adoption of sustainable practices and a switch towards a circular economy-based system to ensure that the electrification of transportation does not come at a high environmental cost. While driving patterns have not changed much over the years, the current Electric Vehicle market is evolving towards models with higher battery capacities. In addition, these batteries are considered to reach the End of Life at 70–80% State of Health, regardless of their capacity and application requirements. These issues may cause an underuse of the batteries and, therefore, hinder the sustainability of the Electric Vehicle. The goal of this study is to review and compare the circular processes available around Electric Vehicle batteries. The review highlights the importance of prioritizing the first-life of the battery onboard, starting with reducing the nominal capacity of the models. In cases where the battery is in risk of reaching the End of Life with additional value, Vehicle to Grid is encouraged over the deployment of second-life applications, which are being strongly promoted through institutional fundings in Europe. As a result of the identified research gaps, the methodological framework for the estimation of a functional End of Life is proposed, which constitutes a valuable tool for sustainable decision-making and allows to identify a more accurate End of Life, rather than considering the fixed threshold assumed in the literature.This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 963580. This funding includes funds to support research work and openaccess publications.Peer ReviewedPostprint (published version

Aplicación de Redes Neuronales en controladores de baterías

Trabajo de final de Curso presentado al Instituto Latino-Americano de Tecnología, Infraestructura y Territorio de la Universidad Federal de Integración Latinoamericana, como requisito para obtener el título de Bachiller en Ingeniería de Energías. Orientador:Dr. Jorge Javier Gimenez Ledesma y Coorientador:Dr. Oswaldo Hideo Ando JuniorEl presente estudio tiene como objetivo aplicar el uso de redes neuronales artificiales, que tiene como una de sus aplicaciones operar como un aproximador universal de funciones, mapeando la relación funcional entre las variables de un sistema a partir de un conjunto conocido de valores muestreados. En este contexto, este trabajo aborda un método para predecir el estado de carga de las baterías utilizando técnicas de redes neuronales artificiales a través de una base de datos y modelos de la curva de carga de las baterías de cloruro de sodio y níquel; y así analizar el comportamiento del sistema de gestión de baterías, a través de los modelos encontrados en las curvas de salida. Así este estudio en principio presenta una breve introducción del mercado de energía, seguido de la justificativa y motivación que llevaron a desarrollar el mismo. En seguida, se presenta la metodología empleada, en el software MATLAB, paso a paso para la obtención de las curvas de carga. Seguido de una breve descripciones de los sistemas de almacenamiento de energía, baterías. Continuando con una descripción del sistema de gerenciamiento de baterías, funcionalidades y aplicaciones. Y por fin, una descripción de redes neuronales, clasificación arquitectura y aplicaciones en ingeniería; que, para el caso, el método propuestos utiliza una red neuronal artificial Perceptron multicapa, una arquitectura de avance (feedforward) con algoritmo de entrenamiento de retropropagación. Con todo esto, finalmente, los resultados indican la capacidad del método para indicar el estado de carga de la batería, así como el análisis de los errores estipulados. Concluyendo que, la configuración utilizada tiene un mejor rendimiento al ajustar el número de capas, y puede ser aplicado en otras baterías, como es el caso de la batería de litio; con los errores y percances encontrados a lo largo de este estudio se presenta algunos trabajos a futuro.The present study aims to apply the use of artificial neural networks, which has as one of its applications to operate as a universal approximator of functions, mapping the functional relationship between the variables of a system from a known set of sampled values. In this context, this work addresses a method to predict the state of charge of batteries using artificialneural network techniques through a database and models of the charge curve of batteries of sodium chloride and nickel; and thus analyze the behavior of the battery management system, through the models found in the output curves. Thus, this study in principle presents a brief introduction to the energy market, followed by the justification and motivation that led to its development. Next, the methodology used is presented, in the MATLAB software, step by step to obtain the load curves. Followed by a briefdescription of the energy storage systems, batteries. Continuing with a description of the battery management system, features and applications. And finally, a description of neural networks, architectural classification and applications in engineering; that, for that matter, the proposed method uses a multilayer Perceptron artificial neural network, a feedforward architecture with backpropagation training algorithm. With all this, finally, the results indicate the ability of the method to indicate the state of charge of the battery, as well as the analysis of the stipulated errors. Concluding that, the configuration used has a better performance when adjusting the number of layers, and can be applied in other batteries, as in the case of the lithium battery; With the errors and mishaps found throughout this study, some future work is presented.Fundação Parque Tecnológico de Itaipu - Laboratório Bateria