Battery management system ieee. A Review on Battery Management System for Electric Vehicles

Battery management system ieee

In Smart cities and Smart industry, a Battery Management System (BMS) focuses on the intelligent supervision of the status (e.g., state of charge, temperature) of batteries (e.g., lithium battery, lead battery). Internet of Things (IoT) integration enhances the system’s intelligence and convenience, making it a Smart BMS (SBMS). However, this also raises concerns regarding evaluating the SBMS in the wireless context in which these systems are installed. Considering the battery application, in particular, the SBMS will depend on several wireless communication characteristics, such as mobility, latency, fading, etc., necessitating a tailored evaluation strategy. An IEEE P2668-Compatible SBMS Evaluation Strategy (SBMS-ES) was proposed to overcome this issue. The SBMS-ES is based on the IEEE P2668 worldwide standard, which aims to assess IoT solutions’ maturity.

Contributors MDPI registered users’ name will be linked to their SciProfiles pages. To register with us, please refer to :


Battery sales are growing in the global market. As predicted by Grand View Research, the global battery market size achieved USD 108.4 billion in 2019, and it is expected that growth with a Compound Annual Growth Rate (CAGR) of 14.1% from 2020 to 2027 will occur [1]. The growth of the battery market, including lithium and lead-acid batteries, is mainly attributed to the demand for automotive and renewable energy applications [2] [3]. Besides, batteries are employed in other areas, such as solar power plant energy storage, data centers, offshore drilling platforms, north and south poles, and airplane and vehicle cranking [4].

The usage of batteries requires extra attention, particularly in the critical applications. Otherwise, an inappropriate installation or use may cause additional costs or even accidents such as fires or explosions. The main risk of the battery usage is from the operating temperature, influenced by the internal chemical processes of the battery [5]. The reasons are twofold: on the one hand, the life of batteries decreases substantially when the temperature rises. As a result, the battery’s maintenance expenses will increase. On the other hand, the effects can be devastating when batteries are exposed to severe temperatures. The expansion of frozen electrolytes at low temperatures and State-of-Charge (SOC) can cause battery ruptures. In addition, uncontrolled reactions, thermal runaway, may occur due to a high operating temperature. A thermal runaway is a self-heating process that leads the battery to shut down or explode [6] [7]. Due to the positive net heat energy in processes, the exothermic reaction in batteries is self-sustaining. Hence, the safe employment of batteries raises many concerns considering these mentioned risk features. Furthermore, extreme caution should be particularly paid when batteries are deployed in critical applications, to prevent severe consequences.

Hence, monitoring battery status during the operation is essential to prevent risks [8]. Smart Battery Management Systems (SBMSs) are proposed to complete such tasks by implementing the supervision of various critical features, such as operating temperature, State of Health (SOH), SOC, etc. Traditional BMSs employ Controller Area Network (CAN)-bus and I2C/SPI communication protocols. However, traditional BMSs are believed to have unreliability, high cost, and complexity as negatives, which have resulted in the emergence of new types of BMS [9]. Compared to conventional BMS, the SBMS applies wireless communication methods to report the monitoring results, providing improved reliability, lower cost, and sensor deployment feasibility [9]. With wireless communication technologies employed, the SBMSs is recognized as one of the many Internet-of-Things (IoT)-based Smart applications. The IoT refers to the connection of physical items (“things”) equipped with various elements and technologies, including sensors, software, etc., to exchange data with other devices and systems through the internet [10]. These physical objects can share and gather data with minimum human interaction, using state-of-art technologies, such as big data analytics, Cloud computing, and mobile communications.

Various categories of SBMS, including experimental or model-based methods, have been presented to contribute to battery status such as temperature, etc. However, the SBMS also brings new challenges. A failure may occur if the wireless communication techniques are inappropriately applied. For example, wireless communication techniques with high latency are unsuitable for time-critical SBMSs. However, a general SBMS evaluation strategy is lacking to address this challenge. As a result, developers are not able to evaluate the performance of their SBMS, and it is hard to determine the best configuration for their applications.

To solve this problem, the SBMS evaluation strategy (SBMS-ES) is proposed in this entry. The SBMS-ES is a comprehensive evaluation strategy which considers the impact of the IoT on the SBMS. Integrating the IEEE P2668 global standard makes the SBMS-ES a general strategy that could be widely applied. The SBMS-ES identifies the essential features of the SBMS and designs a scoring guideline for each of them. A weighted average value is calculated as the final score, based on the evaluated sub-scores of attributes, to indicate the overall performance of the SBMS. The weighting is obtained through implementing an Analytic Hierarchy Process (AHP), a broadly utilized decision-making procedure. The final score is applied to rank the candidate SBMS solutions, to find the most desired one regarding the scenario demands.

Works on SBMS​

Cloud computing and IoT have been widely utilized by researchers, based on traditional BMS, to design SBMS solutions. Kim et al. [11] introduced IoT-enabled battery conditional monitoring and fault diagnosis for Li-ion large-scale applications. over, a digital battery twin was developed, by combining battery monitoring and data-driven modeling approaches. In [12]. an SOC estimate method for lithium-ion and lead-acid batteries was based on an adaptive extended H-infinity filter. In addition, a state-of-health estimate system with particle swarm optimization was designed to monitor the battery’s capacity and power degradation as it ages. With Cloud computing and IoT, a digital twin was built to implement monitoring simultaneously. Xinrong et al. [13] proposed a Wireless Smart Battery Management System (WSBMS) to manage battery cells in electric vehicles (EVs). The developed system aimed to improve performance in fault tolerance and scalability. A balancing algorithm was presented to balance battery cells with various features, such as numbers. Friansa et al. [14] suggested an IoT-based battery monitoring system for microgrid batteries. A human–machine interface was designed using an ExtJS/HTML5 framework to store information, which can be accessed on a desktop. Tetsu et al. [15] developed a Cloud-connected battery management system that monitors shared batteries’ status. The designed system continually connects to the batteries, managing their SOC and monitoring changes in their attributes via a location data Cloud. It supports e-mobility and can be applied to Electric Vehicles (EV). The authors of [16] presented a Smart battery management system to prolong battery life. Authors of [17] proposed a control strategy to minimize the side reaction-induced capacity loss, by changing the cell series-parallel configuration dynamically inside the battery pack.

In addition to the aforementioned SBMS, researchers also have proposed works aiming to study the performance of the wireless communication protocol in the BMS. Alonso et al. [18] researched wireless channel parameters and data rates in a BMS. The main work was to estimate the transmission capacities of different antenna types in various frequency bands. The study also concentrated on Planar Inverted-F- Antenna and CAN-bus communication. Kumtachi et al. [19] improved the reliability of a multi-hop wireless communication protocol for BMS electric vehicles. Specifically, the approach achieves successful communication within 20 ms for over 99% of packets by overhearing those incoming packets without optimal routes.

The mentioned works have made remarkable contributions to the study of SBMS. However, these works solely focused on battery or communication performance monitoring in BMS. The field of SBMS lacks a systematic strategy for evaluating the overall performance of solutions. As a result, designers cannot decide the best configuration for their SBMS. A comprehensive evaluation of SBMS is necessary.

battery, management, system, ieee

P2668 Interoperable Standardized Management Framework

3.1. IEEE P2668 Global Standard

The IEEE P2668 standard defines methods and criteria for evaluating the performance of IoT objects, the evaluation outcome of which is expressed as a quantitative indicator, namely the IDex [20]. The IDex categorizes the maturity of (IoT) objects into five levels, ranging from one (the lowest maturity) to five (the most excellent maturity) [21]. The final IDex value is expected to satisfy the requirement of IoT stakeholders for a clear indication. IDex can also be used to forecast performance changes under various operating circumstances and to present recommendations for increasing the performance of IoT objects. The main objective of IDex is to evaluate the performance of IoT solutions and provide advice on corresponding improvements.

3.2. Overview of SBMS-ES

The SBMS can be evaluated by IDex since it utilizes IoT technology. The specialized scheme for the employment of IDex in the SBMS is called SBMS-ES (evaluation scheme). This section introduces the general construction of SBMS-ES, step by step. The quantitative score of each SBMS solution can be obtained to its comprehensive performance, applying SBMS-ES. By comparing the final scores of SBMS with various configurations (e.g., different communication protocols), the best solution among the candidates can be decided.

battery, management, system, ieee

A flowchart of SBMS-ES is illustrated in Figure 1. It is divided into three subsections, i.e., the attributes evaluation, the weighting allocation, and the final score calculation. The attribute evaluation introduces the identified essential attributes in SBMS-ES and the justifications. Furthermore, the evaluation principles of the attributes are illustrated. The weighting allocation describes how the weighting for each attribute is determined. The final score calculation depicts the way to calculate the final score and select the most desired SBMS solution. The details of these subsections are specified in the following Section 3.3, Section 3.4 and Section 3.5.

3.3. Evaluation Attributes in SBMS

Five key attributes are typically identified in SBMS-ES for evaluation, i.e., sensor installation, monitoring performance, mobility, latency, and fading. The attribute descriptions and sub-scores evaluation principles for each attribute are given in this section.

3.3.1. Senor Installation

As mentioned, the SBMS implements battery status monitoring based on the relative sensors’ feature measurements. To be specific, a straightforward method directly measures the battery status of concern. On the opposite, the indirect method measures the other features to implement data modeling, i.e., estimating the status of concern based on the measured features. Both methods will need sensors for the measurement. Hence, the installation of sensors is part of the SBMS evaluation.

Sensor installation is evaluated in two aspects, i.e., the location of sensors, and the number of sensors. As discussed previously, installing a sensor inside a battery will change the original structure, which entails risks and extra costs. On the contrary, the influence is limited if the sensors are installed outside the battery, e.g., fixed on the battery surface. Hence, it is encouraged to install sensors outside.

over, the number of sensors utilized to obtain the concentrated battery status will be considered when evaluating SBMS. The monitoring scheme that needs more measurements will require more sensors to be deployed, which will bring an increase of installation costs. over, a larger packet size is requested by such schemes for data transmission. The monitoring scheme with fewer sensors is more recommended for the SBMS when the monitoring performance is consistent.

3.3.2. Monitoring Performance

The monitoring performance represents the estimation accuracy of the battery status, which can be measured by the Mean Absolute Error (MAE) [22]. The value of the MAE of the SBMS needs to be as low as possible to improve its evaluation score of this aspect.

3.3.3. Mobility

The mobility of a communication network is the technology that enables nodes to make communications with a moving status. A moving node that employs a communication technique without the function of mobility will suffer from poor communication quality.

The SBMS application scenario can be stationary or mobile [12]. Considering this, the mobility of the applied IoT technology in SBMS needs to be considered. If the battery (such as a lead-acid battery) is utilized in a moving vehicle, the capacity for mobility of the network is essential. Otherwise, mobility is not important in the SBMS if the batteries are fixed in the application scenario.

Ijraset Journal For Research in Applied Science and Engineering Technology

Worldwide, development on the batteries used in electric vehicles is making significant strides in solving the problems of carbon emissions and climate change issues. The efficiency of electric vehicles depends on accurate testing of key parameters and proper operation and functionality of battery management system. On the other hand, inadequate battery energy storage system monitoring and safety measures can lead to serious problems such as battery overheating, overcharging, cell unbalancing, thermal management, and fire threats. An efficient battery management system, which includes charging-discharging control, precise monitoring, temperature management, battery safety, and protection, is essential for enhancing battery performance in order to alleviate these worries. This study aims to give a comprehensive analysis of different intelligent techniques and control strategies for the battery management system in electric vehicle applications. The evaluation evaluates battery state estimate intelligent algorithms in terms of their features, structure, configuration, accuracy, advantages, and disadvantages. The paper also examines the numerous controllers employed in battery warming, cooling, balancing, and protection emphasizing sections, traits, objectives, results, benefits, and limitations.


Nowadays, the automotive industry has made significant strides toward improving the safety of both passengers and pedestrians as a result of various technological advancements. However, the increased number of vehicles on the road is responsible for significantly increasing pollution levels in urban areas.

According to the European Union, the transportation sector accounts for approximately 27 percent of total carbon dioxide (CO2) emissions, with vehicle transportation accounting for more than 70 percent of emissions. To address these issues, electric vehicles (EVs) have gained widespread attention and popularity due to their ability to reduce environmental pollution, conserve fossil fuels, and reduce carbon emissions and global warming concerns.

EVs are a promising alternative to IC engine-powered vehicles, not only in terms of emissions, but also in terms of simplicity, dependability, comfort, and efficiency. For proper battery management system (BMS) functionality and diagnosis in terms of charge-discharge control, battery cell monitoring, cell balancing, power management, and thermal management control, EVs must, however, be widely adopted.

Lithium-ion batteries now rule the EV battery industry due to their high energy and power density, prolonged life cycles, high voltage and poor self-discharge rates. Nonetheless, lithium batteries are susceptible to ageing and temperature, requiring special attention to their working environments in order to avoid physical damage, ageing, and thermal runaways. The battery management system (BMS) is essential for electric vehicle (EV) functioning because it regulates temperature, helps to control voltage across cells, and checks battery charge, health and energy. The following are the main responsibilities of an effective BMS:

  • Data acquisition.
  • Should communicate with all the battery components.
  • Battery status and authentication should be delivered to a user interface.
  • Estimates and evaluates battery states accurately such as state of charge (SoC), state of Health (SoH) and remaining useful life (RUL).
  • Keeps the battery temperatures within a safe range.
  • Performs fault diagnosis, prognosis, and fault handling.
  • Balances the voltage, charge, and capacity of the battery cells.
  • Should ensure the prolonged battery life.


The software in the BMS that analyses and builds a database for system modelling relies heavily on data acquisition (DAQ) and data storage. Oversight tasks include continuous monitoring of all battery cells and collects different parameters using the sensors deployed, where data tracking can be used for diagnostics on its own but is often used in conjunction with the task of calculation to estimate the SOC of all cells in the assembly. This information is utilized in balancing algorithms, but it can also be sent to external devices and displays to reflect the driver the available energy, estimate expected range or range/lifetime depending on current usage and provide information about the battery pack’s condition.

State of charge (SoC) is the ratio of the battery’s remaining charge to its rated capacity or maximum capacity. Soc is computed to make sure that the battery is not ever undercharged or overcharged. SOC also serves as an electric vehicle’s fuel gauge because it shows how much battery power is still available. With the help of new algorithms, it is possible to calculate how far an electric vehicle can travel before its battery needs to be recharged.

Valentina Bianchi

University of Parma, Department of Engineering and Architecture

Batteries are among the most important elements of an electric vehicle and the management becomes a key factor to improve their performance and a safe and reliable operation over time. The Battery Management System (BMS), whose design is of paramount importance, oversees this task, by predicting battery life and keeping the battery in working condition by controlling its charge, detecting the state of charge, the state of health and determining its remaining useful life. over, it oversees the balancing of battery cells. Some of these parameters cannot be determined directly, but depend on the battery current, voltage, and temperature, then the accurate measurement of them is of utmost importance.


This special session focuses on the implementation and validation of strategies for the implementation of efficient BMSs. Submissions are encouraged on a wide spectrum of topics including (but not limited to):

battery, management, system, ieee
  • Innovative battery management systems (BMS);
  • Advanced algorithms data-driven and/or model-based for battery control and monitoring of the state of charge (SOC), state of health (SOH), battery remaining useful life (RUL), etc. and their metrological characterization;
  • Battery diagnostic;
  • Sensing methods to improve battery performance and BMS’s operation;
  • Battery aging;
  • battery balancing techniques;
  • Innovative methods to measure the key battery parameters.


Ilaria De Munari received the M.Sc. degree in electronic engineering and the Ph.D. degree in information technologies from the Parma University, Italy, in 1991 and 1995. She joined the Dept. of Engineering and Architecture, of Parma University as a Research Assistant in 1997, and she has been an Associate Professor since 2004. Her past research interests include the reliability of electronic devices and the design of electronic systems. In this framework, she was involved in several European projects. Her current research is focused on the design of digital systems based on microcontrollers and FPGA. In particular, she is dealing with the design of sen sors for human activity recognition, electrochemical applications, and for the evaluation of battery state of charge. She has authored or co-authored more than 100 papers in technical journals or proceedings of international conferences.

Valentina Bianchi received the B.Sc. and M.Sc. degrees in electronic engineering and the Ph.D. degree in 2003, 2006, and 2010, respectively from the Department of Engineering and Architecture, University of Parma, Parma, Italy, where she is currently a Research Associate. She participated in several national and international projects. She has authored or co-authored over 50 papers in international journals or proceedings of conferences. Her current research interests include the design and validation of sensors for human activity recognition, sensors for electrochemical applications, and digital systems implemented on FPGAs, with a special FOCUS on the design of hardware for machine learning algorithms and arithmetic circuits. She is an Associate Editor of the “IEEE Transactions on Instrumentation and Measurement” journal.


Electric vehicles run on high voltage Lithium-ion battery packs. Lithium-ion batteries have higher energy density (i.e. 100-265 Wh/kg) than other battery chemistries. These batteries come with a risk of catching fire under unusual circumstances. It is imperative to operate the EV batteries in pre-defined safe limits to ensure the safety of the user as well as the vehicle.

The Battery Management System continuously monitors parameters such as temperature, voltage and current in and out of the pack to ensure it is being operated in safe conditions the entire time. BMS is responsible for thermal management of the battery and monitors its temperature continuously. If required, BMS can adjust cooling and trigger other safety mechanisms to cease operations and minimize the risk. e.g. in Hyundai Kona Electric, if overheating of the battery pack is detected by the BMS, the vehicle’s power output is automatically limited and the car is put in fail-safe mode.

battery, management, system, ieee

Overcharging of lithium-ion cells can also lead to thermal runaway and potentially an explosion. BMS continuously monitors the voltage of the pack as well as individual battery cells and controls the supply of the current to avoid overcharging. BMS can enforce the limits of maximum charge or discharge current according to temperature.

Sensing electrical isolation – The BMS also checks that the vehicle chassis is completely isolated from the high voltage battery pack at all times to prevent the user from getting an electric shock.

Performance Optimization

BMS is responsible for optimising the performance of the battery pack.

Lithium-ion batteries perform best when their State of Charge (SoC) is maintained between the minimum and maximum charge limits defined in the battery profile. Overcharging as well as deep discharging degrades the capacity of the battery, thereby shortening its life. At the time of charging, BMS determines how much current can safely go in and communicates the same to the EVSE (Electric Vehicle Supply Equipment or the Charger). During discharge of the battery, BMS would communicate with the motor controller to avoid the cell voltages reaching too low. The vehicles can show a corresponding alert to the user to charge the battery pack. The BMS also controls the recharging of the battery pack by energy generated through regenerative braking.

Individual cells in the battery pack can develop differences in capacity with time, which amplify with each charge/discharge cycle. This imbalance limits the amount of energy that can be derived from the battery, and also how much the battery pack can be charged. Cell Balancing is needed to maintain the cells at equal voltage levels and maximise the capacity utilization of the battery pack. Measurement of individual cell voltages by BMS indicates their relative balance and acts as a pointer to how much charge equalization is required. The BMS performs cell balancing by draining excess energy from cells that are more charged than others, through active or passive balancing techniques.

Health Monitoring and Diagnostics

The BMS uses the collected data points (temperature, voltage, current etc.) to estimate the State of Charge and State of Health (SoH) of the battery pack. The SoC refers to available energy in the battery and determines how far the vehicle can go before needing to recharge. The SoH measures the current condition of the battery as compared to its original capacity and indicates the battery’s suitability for the application. Both SoC and SoH are presented as percentages.

BMS also checks for anomalies in the parameters and behaviour of the cells and the battery pack. It stores the error codes and logs diagnostic information that helps fix any issues with the battery. The BMS can either take necessary corrective actions or trigger failsafe mechanisms to preserve the health of the pack.


The BMS is responsible for communicating with other ECUs (Electronic Control Units) in the vehicle. It relays the necessary data about the battery parameters to the motor controller to ensure the smooth running of the vehicle. In case of AC charging, BMS communicates with the onboard charger to monitor and control the charging of the battery pack.

For DC charging, a communication link is established directly between the EVSE and the BMS. BMS communicates the required output voltage and current levels to the EVSE, and sends instructions to start and stop the charging process.

Leave a Comment