Jiang Jiuchun: Key technologies of lithium battery energy storage system

Recently, Jiang Jiuchun, director of the National Energy Active Distribution Network Technology Research and Development Center, delivered a speech on the key technologies of 6F22 battery energy storage system.

The content of the speech is as follows:

Jiang Jiuchun:

I am talking about battery energy storage. We at Jiaotong University have been working on energy storage, from power systems, electric vehicles to rail transit. Today we are talking about some of the things we are doing in power system applications.

Our important research directions: one is microgrid, and the other is battery application. In battery application, the earliest electric cars we rode used power system energy storage.

The most important issues about battery energy storage are safety, life, and efficiency.

For energy storage systems, the first thing to consider is safety, and then efficiency. We insist on efficiency, transformer rate and life, as well as the energy utilization rate after battery degradation. This problem may not have a quantitative indicator to describe it in many cases, but it should be very important for energy storage. We hope to solve the problems of safety, life and efficiency through several aspects, a standardized energy storage system, a battery status analysis system, and energy storage systems are widely used in electric vehicles and public transportation systems.

At present, everyone is using energy storage systems, node controllers and intelligent distribution boxes to improve the overall economy and stability of the system, enhance the core value of system integrators, and can be connected to the back-end cloud platform in a friendly manner.

This is a centralized energy dispatching system. This hierarchical structure has been explained very clearly this morning. We can achieve long-term multi-energy storage power stations and microgrids coordinated and optimized dispatching through multi-node controllers.

Now it is made into a standard intelligent distribution cabinet, which is the basic feature of the distribution cabinet. It contains a variety of functions, including charging and discharging functions, automatic protection and interface functions. This is a standard configuration.

Node controller realizes the core equipment of local energy management, important data collection functions, monitoring, storage, execution of management strategies and uploading. There is a problem here, which needs to be studied in depth, about the data sampling rate and the time of data sampling when uploading data, so as to implement the battery data analysis in the battery background and turn the battery maintenance into intelligent maintenance. We are also doing some work, how large the sampling number is, or how fast the storage speed is, to fully describe the current state of the battery.

If I drive an electric car, you will find that many electric car states often change and jump. In fact, energy storage, energy storage applications in power systems face the same problem, and we hope to solve it through data. Here we have a BMS sampling number, how large is the appropriate number.

Next, I will talk about flexible energy storage. For single cells, everyone says that I can do 6,000 times, and it can be used 1,000 times when installed in a car, but it is difficult to say. Now you help it to make an energy storage system, claiming to do 5,000 times, what is the actual utilization rate, because the battery itself has a big problem. The battery decays randomly during the decay process, and the decay of each battery is different, which leads to greater and greater differences in single cells. The inconsistency of the decay of batteries from different manufacturers is also different. How much can this group of batteries be used, and the energy is available, this is a problem that needs to be carefully analyzed. For example, when electric vehicles are used, 10 to 90% of them are used. When they decay to a certain extent, only 60% to 70% can be used, which poses a big challenge to energy storage.

Can we group them according to the decay law and make a compromise? What size is appropriate to get better performance and better efficiency? We hope to group them according to the decay law of batteries. Is it more appropriate to have 20 or 40 batteries as a node? Here, we need to make a balance between efficiency and power electronics. So we are doing some flexible energy storage, which is also a project for us to do this. Of course, there is another good place, which is cascade utilization. I think cascade utilization has certain value in the past two years, but whether it is worth using in the future also needs to be considered. Once the efficiency of charging and discharging and the price of batteries are reduced, there will be some problems with cascade utilization. Flexible grouping can solve a big problem. Another highly modularized method reduces the cost of the entire system. The biggest one is to improve the utilization rate.

For example, the battery used in cars after three years decays less than 8% and the utilization rate is only 60%, which is caused by its difference. If you make 5 groups, the utilization rate can reach 70%, which can improve the utilization rate. Connecting battery modules in series can also improve battery utilization. After maintenance, energy storage can be increased by 33%.

Look at this example. After balancing, it can be increased by 7%. After flexible grouping, I increased it by 3.5%. Balancing can increase it by 7%. Flexible grouping can bring a benefit. In fact, the battery decay trajectory of different manufacturers is different. You need to know what this group of batteries will become or what the parameter distribution is in advance, and then you can make a targeted optimization.

This is a method used. The full power of the module is independently controlled by the current, which is not suitable for high-power applications.

Part of the power of the module is independently controlled by the current. This circuit is suitable for medium and high voltage and repeated use. This is the method of MMC battery energy storage suitable for high voltage and high power.

In addition, there is battery status analysis. I have always said that the battery capacity is inconsistent, the decay is random, the battery aging is inconsistent, the capacity and internal resistance are greatly reduced, and this parameter is used for characterization. People use more one capacity and one internal resistance. You have to find a way to maintain consistency. You have to evaluate the SOC difference of each battery. How to evaluate the SOC of this single cell, then you can say why this battery is inconsistent and how much the maximum power can differ. How is a single SOC obtained by maintaining the battery through SOC? The current practice is to put the BMS on the battery system and estimate the SOC online in real time. We want to describe it in another way. We hope to run the sampled data to the background, analyze the battery SOC and SOH through the background data, and optimize the battery on this basis. So we hope to use the car battery data, which cannot be called big data, as a data platform, to expand the SOH estimation model through machine learning and mining, and give the management strategy of the battery system's full charge and discharge based on the estimation results.

After the data comes up, there is another benefit. I can give an early warning of the battery health status. Battery fires still happen frequently, and the energy storage system must be safe. We hope to establish real-time information and medium- and long-term early warnings through background data analysis, find online early warning methods for safety hazards in short and long time scales, and finally improve the safety and reliability of the entire system.

In this way, I can achieve several aspects to a large extent. One is to improve the energy utilization rate of the system, the second is to extend the battery life, and the third is to ensure safety, so that the energy storage system can work reliably.

How much data should be transmitted to meet my requirement? I want to find the smallest data that satisfies the battery operation status. These data can support the subsequent analysis. The data cannot be too large. Sending a large amount of data actually puts a heavy load on the entire network. It is impossible to sample the voltage and current of each battery in dozens of milliseconds and transmit it to the background. We have now found a way. We can tell you what the sampling frequency should be and what characteristic data you want to transmit. We simply compress these data and transmit them to the network. The battery curve parameters in one millisecond are enough to meet the requirements of battery evaluation. We record very little data.

The last one, we said BMS, the cost of energy storage has become more important than the cost of the battery. If you add all the functions to the BMS, you can't reduce the cost of the BMS. Since we can send the data, we can have a powerful analysis platform at the back. I can simplify the front part. There is only data sampling or simple protection at the front part. A very simple SOC calculation is done. The other data are sent from the backend. This is what we are doing now. The whole state estimation and the sampling of the BMS below, we pass through the energy storage node controller and finally transmit it to the network. The energy storage node controller will have a certain algorithm. The following is basically detection and balancing. The final calculation is calculated in the background network. This is the entire system architecture.

Let's take a look at the most basic changes, which are effective and simple, that is, balancing, low-voltage acquisition and balancing acquisition to current acquisition. The energy storage node controller tells the bottom how to process, including SOC here once, and the background works again. This is the smart sensor, battery management unit, and smart node controller we are already doing, which greatly reduces the cost of energy storage.

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