The battery management system maintains voltage levels within cell modules during charging and discharging. 

However, managing such a system is challenging due to the unique qualities of each cell, including capacity, temperature, state of charge (SoC), and other attributes. Even a small fluctuation can result in low voltage, subpar battery performance, and a shorter lifespan. 

This is where the battery management system steps in to address these issues.

According to the International Journal of Engineering Research & Technology, there has been a demand for clean transportation and a staggering increase of 109% worldwide in electric vehiclе (EV) purchases in 2021 

This clearly underscores the need to implement cell balancing in BMS. 

This article addresses the function of cell balancing and explains why even minor power imbalances in an EV battery can have a significant effect. 

Effects of Cell Imbalances in EV

Any imbalance damages the battery by lowering its capacity, leading to overheating or resulting in other problems that shorten the battery’s lifespan. The factors are mostly hard to detect, but the main ones are listed below:

• The internal resistance of each cell
• Busing resistance between cells

When excess energy is detected, the BMS utilizes Field-Effect Transistors (FETs) and the power transfer mechanism to activate load transfer, safeguarding cells during charging and discharging. Thus, cell balancing in BMS is important for EV.

Current Studies on Cell Balancing in BMS

The following are the main conclusions that underline the value of cell balancing in BMS.

1. “Analysis Of Cell Balancing Techniques in BMS For Electric Vehicle” by Boni Suneelkumar and Dr. R. Srinu Nai 2022

The research elucidates the use of cell balancing procedures by BMS to sustain equilibrium inside the lithium-ion battery pack. It explores the two main methods of cell balance, which are Active and Passive:

• Through the use of a certain resistor, high-voltage cells’ excess energy is discharged to create equilibrium through passive balancing
• On the other hand, direct charge transfer from higher-voltage cells to lower-voltage cells is facilitated by active balancing 

This paper comes to the conclusion that active balancing is more efficient than passive balancing.

2. “Lithium-Ion Battery Management System for Electric Vehicles: Constraints, Challenges, and Recommendations” by A. K. M. Ahasan Habib et al., 2023

This research paper provides a comprehensive overview of the importance of BMS in EVs. It discusses the following: 

• The role of cell balancing in improving cell lifespan and safety 
• It also reviews concerns and challenges in BMS and identifies areas that need additional attention for renewable energy storage systems

3. “Battery Management System in EV Applications: Review” by Shanmugasundaram et al., 2022 

This review includes the development of precise battery models to represent the battery’s static and dynamic behavior. It discusses: 

• The development of an online adaptive algorithm for accurate SOC/SOH estimation 
• Covers the modeling of cell and state estimation for implementing active cell balancing

Patent Review on Cell Balancing in BMS

Here are the patents that discuss cell balancing in BMS:

1. “Cell balancing method and system” by Alfredo Quijano LÒPEZ et al. 2016 (US20190109468A1)

An inventive method for cell balancing in BMS, namely for electric vehicles (EVs), is shown in the patent “Cell balancing method and system” (US20190109468A1) by Alfredo Quijano LÒPEZ et al., 2016. 

The patent describes:

• A method of optimizing cell balancing under various circumstances by combining active and passive balancing strategies. 
• The significance of these techniques in enhancing the efficiency of batteries with a large number of cells, as those seen in medium- and large-scale electrochemical storage systems.

2. Battery cell balancing system (US20190199106A1)

This patent outlines a battery cell balancing system that incorporates a switch-mode circuit. The key features of this system are:

• Current Sensors: These are placed on the balancing legs. They play a crucial role in enabling reliable and efficient cell balancing during the battery charging process.
• Voltage Sensors: These are positioned across the cells. They work in conjunction with the current sensors to facilitate effective cell balancing.
• Controller and Switch Mode Dividers (SMDs): The controller receives signals from the current sensor. It then uses these signals to adjust the SMDs. The purpose of this modulation is to restrict the current flowing through the balancing legs to a specific threshold current magnitude.

This system ensures dependable and effective cell balancing during battery charging by using a combination of current sensors, voltage sensors, and a controller that modulates the SMDs based on the signals it receives. This results in a controlled current flow through the balancing legs.

3. SOC estimation and balance control of an electric vehicle battery management system (CN111337839A)

This patent reveals a system and technique for estimating the State of Charge (SOC) and controlling the balance of an electric vehicle battery management system. The system is composed of several modules:

• Acquisition Module: This module is responsible for gathering necessary data.
• Conversion Module: This module processes the acquired data into a usable format.
• Communication Module: This module ensures effective information exchange within the system.
• SOC Estimation Module: This module estimates the SOC value of the battery.
• Balancing Module: This module maintains the balance of the battery system.

The technology is designed to be safe for use, prolong the battery’s lifespan, and optimize performance. It achieves this by calculating the SOC value of the battery when there is an inconsistency in the battery’s electric quantity. 

4. SOC estimation method of an electric vehicle lithium-ion battery based on GRU-RNN (CN112557907A)

This patent presents a method for estimating the State of Charge (SOC) of an electric vehicle’s lithium-ion battery using a Gated Recurrent Unit – Recurrent Neural Network (GRU-RNN). The process involves several steps:

• Data Collection: The first step involves gathering historical data from the lithium-ion battery during its charging and discharging cycles.
• Preprocessing: The collected data is then preprocessed to prepare it for further analysis.
• Training and Test Set Construction: Finally, a training and test set is constructed from the preprocessed data.

5. “Cell balancing method and cell balancing system” by Subaru Corp (US20240034193A1)

This invention pertains to a cell balancing system and technique specifically designed for lithium-ion battery-powered electric vehicles. 

The system is composed of temperature sensors, state-of-charge determination devices, and processors for data processing and transmission. These components work together to execute the cell balancing technique effectively.

The method involves several steps:

• Adjustment of State of Charge (SoC): The SoC of each battery cell is adjusted based on the projected capacity retention rate, SoC adjustment values, and expected standing duration.
• Prediction of Standing Duration: The expected standing duration is determined by analyzing historical data.
• Calculation of Capacity Retention Rate: The capacity retention rate is calculated when the vehicle is not in use. This calculation uses data from temperature sensors, the state of charge, and deterioration characteristics.
• Establishment of SoC Adjustment Values: SoC adjustment values are set to maintain balance if the variation in capacity retention rates exceeds a certain threshold.

Future of Cell Balancing in BMS

The field of cell balancing in BMS for Electric Vehicles (EVs) is rapidly evolving. Key advancements are being made in the following areas:

Advanced BMS Structures

The shift towards decentralized and wireless BMS designs is improving functionality. 

Centralized BMS architectures, while simpler and cost-effective, may not be well-suited for larger battery packs due to the need for extended wiring, which can elevate the risk of signal interference and voltage drop.

Cell Health Prediction 

Data-driven and model-based cell health prognostic tools are gaining prominence. 

Machine learning algorithms, including Gaussian process regression (GPR), neural networks (NN), relevance vector machine (RVM), autoregressive models (AM), and support vector machines (SVM), have been used for State of Health (SOH) estimation.

Balancing Techniques

There is a need to explore trade-offs in active balancing methods and the push for energy-efficient strategies. 

For instance, a State of Health (SOH)-aware, active cell balancing technique has been proposed, which reduces the load current of cells with low SOH using the active cell balancing architecture.

AI Integration

Machine learning holds great potential for precise State of Charge (SOC) estimation and adaptive balancing. 

For example, a hybrid data-driven method that combines LSTM and an improved particle filter (IPF) has been introduced, enhancing the precision of the SOC estimation.

Renewable Integration

The role of cell balancing in Vehicle-to-Grid (V2G) concepts and sustainable second-life battery applications is being explored. 

V2G technology allows vehicles to feed electricity into the grid, enhancing the efficiency of renewable energy utilization. 

Conclusion

Cell balancing, while undoubtedly complex, is crucial for ensuring the safety and longevity of electric vehicle batteries. As research intensifies in this domain, we can anticipate advancements in battery reliability, efficiency, and cost-effectiveness.  

The potential benefits are immense: cleaner transportation, reduced environmental impact, and a more sustainable energy landscape.  The future of cell balancing in BMS holds the key to unlocking the full promise of electric vehicles.