Query: battery management system

title: A brief review on key technologies in the battery management system of electric vehicles
abstract: Batteries have been widely applied in many high power applications, such as electric vehicles (EVs) and hybrid electric vehicles, where a suitable battery management system (BMS) is vital in ensuring safe and reliable operation of batteries. This paper aims to give a brief review on several key technologies of BMS, including battery modelling, state estimation and battery charging. First, popular battery types used in EVs are surveyed, followed by the introduction of key technologies used in BMS. Various battery models, including the electric model, thermal model and coupled electro thermal model are reviewed. Then, battery state estimations for the state of charge, state of health and internal temperature are comprehensively surveyed. Finally, several key and traditional battery charging approaches with associated optimization methods are discussed.

title: Lithium Ion Battery Health Prognosis Based on a Real Battery Management System Used in Electric Vehicles
abstract: This paper developed an effective health indicator to indicate lithium ion battery state of health and moving window based method to predict battery remaining useful life. The health indicator was extracted based on the partial charge voltage curve of cells. Battery remaining useful life was predicted using a linear aging model constructed based on the capacity data within a moving window, combined with Monte Carlo simulation to generate prediction uncertainties. Both the developed capacity estimation and remaining useful life prediction methods were implemented based on a real battery management system used in electric vehicles. Experimental data for cells tested at different current rates, including 1 and 2 C, and different temperatures, including 25 and 40 degC, were collected and used. The implementation results show that the capacity estimation errors were within 1.5% During the last 20% of battery lifetime, the root mean square errors of remaining useful life predictions were within 20 cycles, and the 95% confidence intervals mainly cover about 20 cycles.

title: Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium Ion Battery State of Charge Estimation
abstract: Energy storage system (ESS) technology is still the logjam for the electric vehicle (EV) industry. Lithium ion (Li ion) batteries have attracted considerable attention in the EV industry owing to their high energy density, lifespan, nominal voltage, power density, and cost. In EVs, a smart battery management system (BMS) is one of the essential components; it not only measures the states of battery accurately, but also ensures safe operation and prolongs the battery life. The accurate estimation of the state of charge (SOC) of a Li ion battery is a very challenging task because the Li ion battery is a highly time variant, non linear, and complex electrochemical system. This paper explains the workings of a Li ion battery, provides the main features of a smart BMS, and comprehensively reviews its SOC estimation methods. These SOC estimation methods have been classified into four main categories depending on their nature. A critical explanation, including their merits, limitations, and their estimation errors from other studies, is provided. Some recommendations depending on the development of technology are suggested to improve the online estimation.

title: A Battery Management System With a Lebesgue Sampling Based Extended Kalman Filter
abstract: The estimation and prediction of state of health (SOH) and state of charge (SOC) of Lithium ion batteries are two main functions of the battery management system (BMS) In order to reduce the computation cost and enable deployment of the BMS on the low cost hardware, a Lebesgue sampling based extended Kalman filter (LS EKF) is developed to estimate the SOH and SOC. An LS EKF is able to eliminate unnecessary computations, especially when the states change slowly. In this paper, the SOH is first estimated and the remaining useful life is predicted by the LS EKF. Then, the estimated SOH is used as the initial battery capacity for SOC estimation and prediction. The SOH and SOC estimation and prediction are calculated repeatedly in the whole battery service life. The proposed method is verified with the application to the capacity degradation of the Lithium ion battery. The results show that the LS EKF based algorithm has a good performance in SOH and SOC estimation and prediction in terms of accuracy and computation cost.

title: Design and implementation of a battery management system with active charge balance based on the SOC and SOH online estimation
abstract: the power delivery performance of series and parallel strings connected battery modules or packs is restricted by the worst cells in the string. Each cell has a slightly different capacity and terminal voltage due to manufacturing tolerances and operating conditions. The cell strings tend to loss balance during charging and discharging process. The motivation of this paper is to develop a battery management system (BMS) to monitor and control the temperature, state of charge (SOC) and state of health (SOH) et al. and to increase the efficiency of rechargeable batteries. An active energy balancing system for Lithium ion battery pack is designed based on the online SOC and SOH estimation. The remainder capacity of the battery is estimated by measuring the terminal voltage for each cell, and the balance system will be triggered when the difference between the SOC of one cell and the average SOC is more or less than a predefined threshold in order to minimize the output voltage ripple. The simulation results indicate that the designed BMS can precisely synchronize the SOC while minimizing the output voltage ripple.

title: Towards a smarter battery management system: A critical review on optimal charging methods of lithium ion batteries
abstract: Automotive electrification is a main source of demand for lithium ion batteries. Performances of battery charging directly affect consumers' recognition and acceptability of electric vehicles. Study on optimized charging methods is vital for future development of a smarter battery management system and an intelligent electric vehicle. This paper starts from introducing the working principles and existing problems of simple charging methods and then elaborating various optimized charging methods along with their characteristics and applications. It demonstrates that the optimized charging methods can reduce charging time, improve charging performance and extend battery life cycle comparing with conventional charging methods. At the end, this paper also provides a four step pathway towards the design of an optimal charging method of Li ion batteries: determine optimization objectives, establish optimization scheme, develop matching design and implement and promote the optimal charging method.

title: Review on battery thermal management system for electric vehicles
abstract: The lithium ion batteries are widely used for electric vehicles due to high energy density and long cycle life. Since the performance and life of lithium ion batteries are very sensitive to temperature, it is important to maintain the proper temperature range. In this context, an effective battery thermal management system solution is discussed in this paper. This paper reviews the heat generation phenomena and critical thermal issues of lithium ion batteries. Then various battery thermal management system studies are comprehensively reviewed and categorized according to thermal cycle options. The battery thermal management system with a vapor compression cycle includes cabin air cooling, second loop liquid cooling and direct refrigerant two phase cooling. The battery thermal management system without vapor compression cycle includes phase change material cooling, heat pipe cooling and thermoelectric element cooling. Each battery thermal management system is reviewed in terms of the maximum temperature and maximum temperature difference of the batteries and an effective BTMS that complements the disadvantages of each system is discussed. Lastly, a novel battery thermal management system is proposed to provide an effective thermal management solution for the high energy density lithium ion batteries.

title: Towards a smarter battery management system: A critical review on battery state of health monitoring methods
abstract: To ensure the driving safety and avoid potential failures for electric vehicles, evaluating the health state of the battery properly is of significant importance. This study aims to serve as a useful support for researchers and practitioners by systematically reviewing the available literature on state of health estimation methods. These methods can be divided into two types: experimental and model based estimation methods. Experimental methods are conducted in a laboratory environment to analyze battery aging process and provide theoretical support for model based methods. Based on a battery model, model based estimation methods identify the parameters, which have certain relationships with battery aging level, to realize state of health estimation. On the basis of reading extensive literature, methods for determining the health state of the battery are explained in a deeper way, while their corresponding strengths and weaknesses of these methods are analyzed in this paper. At the end of the paper, conclusions for these methods and prospects for the development trend of health state estimation are made.

title: Completely Decentralized Active Balancing Battery Management System
abstract: The performance of a string of series connected batteries is typically restricted by the worst cell in the string and a single failure point will render the entire string unusable. To address these issues, we present a decentralized battery management system with no communication requirement based on a modular multilevel converter topology with a distributed inductor and distributed controller running on a local microprocessor. This configuration is referred to as a "smart cell." By sensing the voltage across the local distributed inductor, each smart cell is able to: first, determine its optimal switching pattern in order to minimize the output voltage ripple; and second, adjust its duty cycle to synchronize its state of charge (SOC) with the average SOC of the series string of cells. The decentralized controller is derived using the theory of Kuramoto oscillators, and the stability of a system of smart cells is investigated. We experimentally show that a system of three smart cells with their decentralized controllers can accurately synchronize the SOC while minimizing their output voltage ripple.

title: Sensitivity analysis of factors influencing a heat pipe based thermal management system for a battery module with cylindrical cells
abstract: Structural parameters have a significant effect on the thermal performance of a battery thermal management system (BTMS) In the present study, a thermal management system based on heat pipes is proposed for a battery module with cylindrical cells. A computational fluid dynamics model is built for the BTMS and validated with experimental results. An orthogonal numerical test is conducted to obtain the sensitivity of various parameters, including the battery spacing, the thickness of conduction element, the circumference angle between battery and conduction element, and the height of conduction element on the thermal performance for the proposed BTMS. Results show that the height of conduction element has the most sensitive effect on the maximum temperature and temperature difference of battery module; the circumference angle between battery and conduction element is the second; the battery spacing and the thickness of conduction element has the minimal effect. The best thermal performance is obtained under the condition of 19 mm battery spacing, 4 mm conduction element thickness, 120deg circumference angle and 60 mm conduction element height. Based on the optimal parameters, the maximum temperature and the temperature difference of battery module are 27.62 degC and 1.08 degC, respectively.