Due to increasing concerns about global warming, greenhouse gas emissions, and the depletion of fossil fuels, the electric vehicles (EVs) receive massive popularity due to their performances and efficiencies in recent decades. EVs have already been widely accepted in the automotive industries considering the most promising replacements in reducing CO2 emissions and global environmental issues. Lithium-ion batteries have attained huge attention in EVs application due to their lucrative features such as lightweight, fast charging, high energy density, low self-discharge and long lifespan. This paper comprehensively reviews the lithium-ion battery state of charge (SOC) estimation and its management system towards the sustainable future EV applications. The significance of battery management system (BMS) employing lithium-ion batteries is presented, which can guarantee a reliable and safe operation and assess the battery SOC. The review identifies that the SOC is a crucial parameter as it signifies the remaining available energy in a battery that provides an idea about charging/discharging strategies and protect the battery from overcharging/over discharging. It is also observed that the SOC of the existing lithium-ion batteries have a good contribution to run the EVs safely and efficiently with their charging/discharging capabilities. However, they still have some challenges due to their complex electro-chemical reactions, performance degradation and lack of accuracy towards the enhancement of battery performance and life. The classification of the estimation methodologies to estimate SOC focusing with the estimation model/algorithm, benefits, drawbacks and estimation error are extensively reviewed. The review highlights many factors and challenges with possible recommendations for the development of BMS and estimation of SOC in next-generation EV applications. All the highlighted insights of this review will widen the increasing efforts towards the development of the advanced SOC estimation method and energy management system of lithium-ion battery for the future high-tech EV applications.

A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations

With the rapidly evolving technology of the smart grid and electric vehicles (EVs), the battery has emerged as the most prominent energy storage device, attracting a significant amount of attention. The very recent discussions about the performance of lithium-ion (Li-ion) batteries in the Boeing 787 have confirmed so far that, while battery technology is growing very quickly, developing cells with higher power and energy densities, it is equally important to improve the performance of the battery management system (BMS) to make the battery a safe, reliable, and cost-efficient solution. The specific characteristics and needs of the smart grid and EVs, such as deep charge/discharge protection and accurate state-of-charge (SOC) and state-of-health (SOH) estimation, intensify the need for a more efficient BMS. The BMS should contain accurate algorithms to measure and estimate the functional status of the battery and, at the same time, be equipped with state-of-the-art mechanisms to protect the battery from hazardous and inefficient operating conditions.

Battery Management System: An Overview of Its Application in the Smart Grid and Electric Vehicles

An overview of new and current developments in state of charge (SOC) estimating methods for battery is given where the focus lies upon mathematical principles and practical implementations. As the battery SOC is an important parameter, which reflects the battery performance, so accurate estimation of SOC cannot only protect battery, prevent overcharge or discharge, and improve the battery life, but also let the application make rationally control strategies to achieve the purpose of saving energy. This paper gives a literature survey on the categories and mathematical methods of SOC estimation. Based on the assessment of SOC estimation methods, the future development direction of SOC estimation is proposed.

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The State of Charge Estimating Methods for Battery: A Review

Real-time estimation of the state of charge (SOC) of the battery is a crucial need in the growing fields of plug-in hybrid electric vehicles and smart grid applications. The accuracy of the estimation algorithm directly depends on the accuracy of the model used to describe the characteristics of the battery. Considering a resistance-capacitance (RC)-equivalent circuit to model the battery dynamics, we use a piecewise linear approximation with varying coefficients to describe the inherently nonlinear relationship between the open-circuit voltage (VOC) and the SOC of the battery. Several experimental test results on lithium (Li)-polymer batteries show that not only do the VOC-SOC relationship coefficients vary with the SOC and charging/discharging rates but also the RC parameters vary with them as well. The moving window least squares parameter-identification technique was validated by both data obtained from a simulated battery model and experimental data. The necessity of updating the parameters is evaluated using observers with updating and nonupdating parameters. Finally, the SOC coestimation method is compared with the existing well-known SOC estimation approaches in terms of performance and accuracy of estimation.

Online Adaptive Parameter Identification and State-of-Charge Coestimation for Lithium-Polymer Battery Cells

In this study, a probabilistic neural network (PNN) is used to estimate the state of health (SOH) of Li-ion batteries. The accurate prediction of SOH can help avoid inconveniences or fatal accidents from the sudden malfunction of the battery. A total of 110 pieces of Li-Co batteries are used. Constant current/voltage recharging and constant current discharging are performed for the life-cycle test of the battery. The data obtained from the recharging and discharging electric characteristics as well as the life-cycle test of the battery are used to estimate the SOH of the battery. The test data show that the constant current charging time, the instantaneous voltage drop at the start of discharging, and the open circuit voltage are the most important characteristics for estimating the SOH of the battery. The PNN is trained using 100 pieces of batteries. The remaining 10 pieces are used to verify the feasibility of the proposed method. The effectiveness of the PNN training using a number of samples is discussed and analyzed. The results show that the average error of the prediction is 0.28% and the standard deviation is 1.15%. The computation time of the PNN is 62.5 ms. Thus, the proposed method can accurately estimate the SOH of the battery in a short period.

Estimation of Battery State of Health Using Probabilistic Neural Network

For the purpose of predicting the state of charge of Lithium-ion rechargeable battery accurately, we introduced the method of electrochemical impedance spectroscopy to solve the problem. The experiment data of impedance spectroscopy is comprised of an inductive arc in the high-frequency region and two capacitive arcs in the low-frequency region, and by which the reasonable equivalent circuit of battery was established. The component parameters obtained at several state of charge values of the battery had been analyzed by a non-linear least-squares fitting procedure and some electrochemical knowledge. Through researching the changing regulation of parameters with the different States of charge, the frequency of maximum of the semicircle (f max ), the phase angle o, the equivalent series capacitance (C s ) had been substantiated to be the suitable parameters for analyzing and predicting the state of charge values of the lithium-ion battery.

Prediction of state of charge of Lithium-ion rechargeable battery with electrochemical impedance spectroscopy theory

According to the communication structure of CAN BUS network on EV, this paper works out a SAE J1939 application layer protocol meet the system functional requirements, and designs the software and hardware for the system. First, design the CAN BUS work nod for EV, including the master node, the light node, air conditioning node, doors and instrument node, etc, and the draw the CAN BUS topology diagram. Meanwhile, according to the concrete situation of EV, work out an application layer protocol that consistent with the SAE J1939 protocol, and the information allocation table and message structure chart of CAN BUS network node is also presented. Secondly, design the hardware and software for the CAN BUS communication network. Hardware interface circuit mainly consist of CAN communication controller SJA1000, high-speed opt coupler 6N137 and CAN BUS driver 82C250, and design schematic circuit diagram for CAN bus system hardware. The software designs for CAN BUS network are mainly the design of CAN BUS data communication and exchange between nodes, and communication processing for switch-signal, analog signal. The design of software communication module includes CAN initialization unit, message sending unit, message receiving unit and the interrupt service unit. Finally, the CAN BUS network communication system the paper designed is applied to the new energy bus produced by CENS Energy-Tech Co., Ltd. which is dedicated in the Shanghai World Expo. From the battery voltage data collected when EV are running, we can see that the system is accurate, stable without number lost, frame dropping and transfer error in data communication. The design has practical value promoting the application of new energy vehicles.

Design method of CAN BUS network communication structure for electric vehicle

Aiming at the uncertain problem of dynamic performance evaluation of power battery for electrical vehicle, this paper researched deep on the establishment of simulation system of the performance of Li-on power battery for electrical vehicle in different working conditions by comparing and analyzing the general structure of simulation system of the performance of electrical vehicle in and abroad combined with the characteristic of pure electrical vehicle, and proposed an on-line performance simulation method of Li-on power battery for electrical vehicle based on the technology of Internet of things. Design for data acquisition, hardware simulation of charging and discharging of the simulation system and the running state monitoring for simulation platform are also included in this paper. It is of most importance for the development of electrical vehicle and Li-on power battery system for electrical vehicle of our country.

Simulation system of the performance of power battery for electrical vehicle based on Internet of things

For the great need of the high power output and high capacity power battery of electric vehicle, this article presents a method which classify the battery by EIS, and this method can get more information about the dynamics and electrode interface structure than the traditional ones. In order to make it closer to actual production, improvement was made for the disadvantages of the long time consuming and difficulty in sorting. Select a specific spectrum to measure the battery impedance, and use the results to simulate the impedance curve, and then sort the batteries. The actual data have showed that the method is efficient in batteries sorting. Through measuring the EIS of the lithium battery, this article established a reasonable equivalent circuit. By setting the categories range of the equivalent parameters, this is a reasonable sorting method at the moment. However, the long time required makes it impossible to be used in production. To solve the problem, a new method, multi-points EIS is introduced. It just selected some characteristic frequency points in the impedance cure, and it substitutes point for line in the test. The selection of spectrum point plays a vital role in the multi-points EIS sorting, and spectrum point may vary with the internal structure of the battery. EIS curves of the battery are composed of circle and lines, so Hough transform algorithm is use identify the curve and get an approximate subsection equation. After repeated tests and experiments, it won't effectively fit the original state of the battery impedance and calculate the equivalent circuit when the test points are less then five. Test results have shown that multi-points EIS improve the accuracy of battery sorting; therefore it has great practical value. This method can explore the internal structure of the battery, reduce test time and simplify the sorting steps, providing a new ways for the development of battery management system.

Research on the consistency of the power battery based on multi-points impedance spectrum

The cost and security of the LiFePO 4 power battery is a critical factor in the application of the electric vehicles. The paper describes a management system(BMS) use MPC5510 and LF2407 as the core, POWER PC and the DSP as the main body to build the hardware platform of the battery management system. Embed μC / OS-II in POWER PC and DSP respectively as the real-time operating system. And achieve multiple tasks and CAN bus design of the phosphate iron lithium of power battery management system to improve the vehicle system's real-time and stability.

Li Gechen

Papers

Prediction of state of charge of Lithium-ion rechargeable battery with electrochemical impedance spectroscopy theory

Design method of CAN BUS network communication structure for electric vehicle

Simulation system of the performance of power battery for electrical vehicle based on Internet of things

Research on the consistency of the power battery based on multi-points impedance spectrum

Design on battery management system of LiFePO 4 power battery based on μC/OS-II