Overview: The article explains the advancements and challenges in the growth of electric vehicles and the difficulties in modeling batteries. It also highlights the state of charge estimation of batteries and their potential to revolutionize the transportation industry.

The development of smart grid technology, the quick rise of renewable energy-based power generation, the expansion of electric vehicle (EV) production, and the decrease in carbon dioxide emissions have all contributed to the rapid advancement of the battery energy storage system (BESS) over the past few decades. 

What are the Challenges Faced by Battery Management Systems?

As battery energy storage systems advance, electric vehicles are becoming a more popular technology to replace fossil fuels and reduce carbon emissions. However, the limited lifespan and inefficient battery energy storage system charging methods pose barriers to electric vehicle growth. 

So, studies have been done to come up with a fast charging method for a battery energy storage system using linear-quadratic strategies, hierarchical navigation, and advanced control theory. Moreover, a great deal of research is being done to improve the energy capacity and prolong the life cycles of battery energy storage systems. 

‘Lithium-ion Batteries’ the Game Changer

Based on their composition and construction, battery energy storage systems are divided into different categories. Since lithium-ion batteries have higher power and energy capacities than other energy storage systems, electric vehicles frequently use lithium-ion batteries.

However, lithium-ion batteries are expensive, and in order to prevent an explosion, they require an appropriate thermal management system. To meet the need for electric vehicles in the future, research and development on lithium-ion battery technology has begun.

Additionally, a great deal of research and development is being done to improve the longevity of lithium-ion batteries and lower the cost of production. The state-of-charge estimation of lithium-ion batteries plays an important role in increasing the reliability of lithium-ion batteries.

The State-of-Charge

The state-of-charge (SOC) describes the remaining energy in battery energy storage systems. State-of-charge cannot be measured directly because it is not a physical quantity. The state-of-charge is typically stated as a percentage with respect to the rated capacity and can only be determined by measuring strongly associated proxy parameters like voltage, current, and temperature. 

According to the research, state-of-charge is the ratio of the battery's available charge to its highest charge. The mathematical definition of state-of-charge is given in equation (1).

Challenges in Determining the State-of-Charge

Although the definition of state-of-charge is simple and can be found in (1), it is quite difficult to accurately estimate the state-of-charge for the lithium-ion battery. This is due to the fact that, contrary to what battery manufacturers recommend, the battery's rated capacity (Qrated) does not accurately represent the battery's actual capacity.

Rated capacity varies during the course of the battery's life, adding to the complexity of the problem. These variables include the battery's age, the surrounding temperature, and the intricate chemical reactions occurring within the battery. 

Also, only a few types of sensors—including temperature, conductometric, potentiometric, and amperometric sensors—can directly measure the electrochemical processes that happen in batteries. 

In addition, mechanical elements, including physical harm sustained during assembly line operations and manufacturing flaws, are well-known contributors. Because of these different unknowns, high-accuracy state-of-charge estimation is still a difficult problem to solve. 

Essentially, precise battery state-of-charge estimation would give manufacturers and researchers a clear notion of how to proceed with the development of electric vehicles in the future.

Advantages

  • Increases the battery pack's lifetime. Using a battery management system with an accurate state-of-charge calculation to initiate a cutoff under particular circumstances can prevent battery pack damage.

  • Improves the battery pack's performance. A battery management system with an accurate state-of-charge estimate can realize the full potential of the battery pack capacity.

  • It increases the dependability of any battery-powered device's power system. Reduces costs by using smaller battery packs.

  • Increases the battery packs' power density. Battery packs can be constructed more precisely and without over-engineering, which can lead to smaller and denser battery packs because of the exact state-of-charge estimation.

Proposed State-of-Charge Estimation Models

A great deal of research has been done in the past to help improve the accuracy of state-of-charge estimation. To increase estimation accuracy, a number of models have been developed, including the 

  • Electrochemical model (EM)

  • Equivalent circuit model (ECM)

  • Electrochemical impedance model (EIM) 

In an effort to produce an accurate state-of-charge estimation, these techniques try to mimic the behavior of batteries by taking the aforementioned elements into account. Even still, the issue hasn't been fixed. 

The non-linearity and time-variability of the system may make it impossible to model a battery. Authors have even claimed that it is physically difficult to observe the intricate internal electrochemical processes in batteries by any kind of direct measurement. 

Because of this, a predictive battery model is made by looking at the important parts of an experiment cycle, like the operating time (t), voltage (V), current (I), and temperature (T). This method can provide precise battery health status information, but it has issues when used online. 

In addition, it is believed that an excessive number of external uncertainties in the surrounding environment (temperature, pressure, etc.) could modify the battery's internal electrochemical characteristics. To overcome these challenges, data-driven state-of-charge estimate algorithms have become very popular because they are so good at handling complex nonlinear functions.

Battery Management System

Any battery management system (BMS) is the perfect example to show the advantages of having a precise state-of-charge estimate. An electronic system called a battery management system keeps track of the conditions and characteristics of a rechargeable battery pack in order to manage it. 

Cell voltage, current, temperature, state of charge, state of health (SOH), state of power (SOP), and other variables are examples of battery states. 

By maintaining these parameters, the battery management system is able to decide when to charge the battery and when to cut off battery utilization in order to prevent dangerous operating conditions. 

The battery management system makes sure that the battery and the user are effectively protected from any risks in this way. The function of state-of-charge estimation in a battery management system is shown in Fig. 1.

Fig. 1: State-of-charge estimation in battery management system Source: IEEE Access

A key element of a battery management system that affects a variety of other operations is the state-of-charge estimate. Other computations, like state of health, cell balancing, and power calculations, use the state-of-charge value as an input. 

Summarizing the Key Points

  • The development of smart grid technology, renewable energy-based power generation, and electric vehicle production has contributed to the rapid advancement of battery energy storage systems.

  • Researchers are working on improving the safety and performance of lithium-ion batteries through the development of new materials and manufacturing processes.

  • Accurate state-of-charge estimation is crucial for the proper functioning of battery management systems and can prevent battery pack damage.

  • Data-driven state-of-charge estimate algorithms have become popular due to their ability to handle complex nonlinear functions

Reference

How, Dickson N. T., M. A. Hannan, M. S. Hossain Lipu, and Pin Jern Ker. “State of Charge Estimation for Lithium-Ion Batteries Using Model-Based and Data-Driven Methods: A Review.” IEEE Access 7 (2019): 136116–36. https://doi.org/10.1109/access.2019.2942213.