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

The content of the speech is as follows:

Jiang Jiuchun:

I'm talking about battery energy storage. We at Jiaotong University have been doing energy storage, from electric power system, electric vehicle to rail transit. Today we are talking about some things we are doing in the application of electric power system.

Our important research directions: one is the microgrid, the other is the battery application. In the battery application, our earliest electric vehicles used power system energy storage.

The most important problem about battery energy storage is safety; The second is longevity, and then efficiency.

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

The use of the energy storage system, node controller and intelligent distribution box that everyone is using at present improves the overall economy and stability of the system, enhances the core value of the system integrator, and can be friendly connected to the back-end cloud platform.

This is a centralized energy dispatching system. This hierarchical structure has been made clear in the morning. We can achieve long-term optimal scheduling of multi energy storage power plants and microgrids through multi node controllers.

Now it is made into a standard intelligent power distribution cabinet, which is the basic feature of the power distribution cabinet, including various functions, charging and discharging functions, automatic protection and interface functions, which are standard equipment.

The node controller realizes the core equipment of local energy management, important data acquisition functions, monitoring, storage, implementation of management strategies and upload. There is a problem in this. We need to seriously and deeply study the data sampling rate and time when data is uploaded. In this way, we can analyze the battery data in the background of the battery and turn the battery maintenance into intelligent maintenance. We are also doing some work. How many samples are taken at the end, or how fast the storage speed is, to fully describe the current state of the battery.

If I open an electric vehicle, you will find that many electric vehicle states often change and jump. In fact, energy storage applications in the power system face the same problem. We hope to solve it through data. We have a BMS sample size here.

Let me talk about flexible energy storage. It is said that I can use a single battery 6000 times, and it can be used 1000 times when it is installed in a car. It is hard to say. Now you can help it to achieve an energy storage system, which is claimed to be 5000 times. In fact, how much is the utilization rate, because there is a big problem with the battery itself. The decline of the battery during the decline process is random. Each battery has a different decline, leading to a growing difference in the single battery, The change of battery recession varies from manufacturer to manufacturer. How much the battery can be used and how much energy is available is a question to be carefully analyzed. For example, when electric vehicles come into use, from 10% to 90% of them are in use, and when they decline to a certain extent, they can only use 60% to 70%, which poses a big challenge to energy storage.

Can we make a compromise by grouping them according to the law of recession? We hope to group them according to the law of battery recession. Whether 20 cells are more appropriate as a node or 40 cells are more appropriate, which will make a balance optimization between efficiency and power electronics. So we do something about flexible energy storage, which is also a project for us to do. Of course, there is also a better place, which can be used in echelon. I think echelon utilization has a certain value in the past two years, but whether it is worth using in the future, and we should also consider that once the charging and discharging efficiency and battery price are reduced, there are some problems in echelon utilization. Flexible grouping can solve large problems. The other is highly modular, reducing the cost of the whole system. The largest one can improve utilization.

For example, the battery used in the car after three years has declined by less than 8%, and the utilization rate is only 60%, which is caused by its difference. You can make five groups of batteries with a utilization rate of 70%, which can improve the utilization rate. The battery modules are connected together to improve the battery utilization. After maintenance, the energy storage capacity increased by 33%.

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

This is a method adopted. The module is fully power independent current control, which is not suitable for high-power applications.

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

In addition, the battery status analysis is involved. I have always said that the battery capacity is inconsistent, and the decline is random. The battery aging is inconsistent, and the capacity and internal resistance are greatly reduced. This parameter is used to characterize the capacity and internal resistance. You need to find a way to maintain consistency. You need to evaluate the SOC difference of each battery and how to evaluate the SOC of this cell. Then you can tell how the battery is inconsistent and how much the maximum power difference is. How does a single SOC come about when the battery is maintained 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. We analyze the battery SOC and SOH through the background data, and optimize the battery on this basis. Therefore, we hope that the vehicle battery data, not big data, is a data platform. Through machine learning and mining, we can expand the estimation model of SOH, and give the management strategy of full charge and discharge of the battery system based on the estimation results.

After the data comes up, there is another advantage. I can give an early warning of battery health. The battery ignition still happens frequently, and the energy storage system must be regarded as a safety system. We hope to establish real-time information and medium and long-term early warning through background data analysis, find online early warning methods for short-term and long-term security risks, and finally improve the security and reliability of the entire system.

In this way, I can greatly improve the energy utilization rate of the system, extend the battery life, and ensure safety, so that the energy storage system can work reliably.

How much data must be transmitted to meet my requirements? I need to find the smallest pole to meet the battery operation status. These data can support the following analysis, and the data cannot be too large. In fact, a large amount of data is also a great load on the entire network. For tens of milliseconds, you can collect the voltage and current of each battery. It is impossible for you to transmit them to the background. Now we have found a way to tell you what the sampling frequency should be and what characteristic data you want to transmit. We compress these data simply and then transmit them to the network. One millisecond of battery curve parameters is enough to meet the needs of battery evaluation. Our data records are very few.

Finally, we say that the cost of energy storage is more important than the cost of batteries for BMS. If you add all the functions to the BMS, you cannot reduce the cost of the BMS. Since we can send the data and have a powerful analysis platform at the back, I can simplify it in the front. In the front, there is only data sampling or simple protection, and a very simple SOC calculation is done. Other data are sent from the background. This is what we are doing now. The whole state estimation and the sampling of the underlying BMS, After passing through the energy storage node controller, we finally transmit it to the network. The energy storage node controller will have a certain algorithm, which is basically detection and equalization. The final operation is performed in the background network. This is the whole system architecture.

Let's take a look at the effectiveness and simplicity of the lowest level transformer, that is, equalization, low-voltage acquisition and equalization acquisition to current acquisition. The controller of the energy storage node tells the lower level how to deal with it, including the SOC is performed here once, and the background is performed again. This is the smart sensor, battery management unit and smart node controller that we have been working on, which has greatly reduced the cost of energy storage.

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