1 Battery Management System (BMS) Definition This article refers to the address: http:// The safe working area of ​​the lithium ion battery is shown in Figure 1. The main task of BMS is to ensure the design performance of the battery system: 1) safety; 2) durability; 3) power. Figure 1 Schematic diagram of the safe working area of ​​lithium ion battery The basic framework of BMS software and hardware is shown in Figure 2. It should have the following functions: 1) Battery parameter detection. 2) Battery status estimation. 3) Online fault diagnosis. 4) Battery safety control and alarm. 5) Charge control. 6) Battery equalization. 7) Thermal management. 8) Network communication. 9) Information storage. 10) Electromagnetic compatibility. Figure 2 Basic framework of vehicle BMS software and hardware 2 Battery Management System Key Technologies 2.1 Battery management system requirements for sensor signals 2.1.1 Single chip voltage acquisition accuracy Typically, for safety monitoring, each string of battery voltages in the battery pack needs to be collected. Different systems have different requirements for accuracy. Figure 3 OCV curve of single cell and its voltage acquisition accuracy requirements For LMO/LTO batteries, the cell voltage acquisition accuracy is only 10 mV. For LiFePO4/C batteries, the cell voltage acquisition accuracy needs to be around 1mV. However, the current voltage collection accuracy of single cells can only reach 5 mV. 2.1.2 Sampling frequency and synchronization There are many kinds of battery system signals, and the battery management system is generally distributed. During the signal acquisition process, different control sub-board signals will have synchronization problems, which will affect the real-time monitoring algorithm. When designing a BMS, it is necessary to put forward corresponding requirements for the sampling frequency and synchronization accuracy of the signal. 2.2 Battery Status Estimation The relationship between the various state estimates of the battery is shown in Figure 4. Battery temperature estimation is the basis for other state estimates. Figure 4 battery management system algorithm framework 2.2.1 Battery temperature estimation and management Temperature has a great influence on battery performance. At present, only the surface temperature of the battery can be measured, and the internal temperature of the battery needs to be estimated using a thermal model. The battery is thermally managed according to the estimated structure. Figure 5 Battery internal temperature estimation process 2.2.2 State of charge (SOC) estimation The SOC algorithm is mainly divided into a single SOC algorithm and a fusion algorithm of multiple single SOC algorithms. The single SOC algorithm includes an ampere-time integration method, an open circuit voltage method, an open circuit voltage method based on battery model estimation, and other battery performance based SOC estimation methods. The fusion algorithm includes simple correction, weighting, Kalman filtering, and sliding mode variable structure methods. The battery model based SOC estimation method such as Kalman filtering is accurate and reliable, and is currently the mainstream method. 2.2.3 Health Status (SOH) Estimation SOH refers to the degree to which the current performance of the battery deviates from the normal design specifications. Figure 6 is a simplified schematic diagram of the principle of battery performance degradation. At present, SOH estimation methods are mainly divided into durability empirical model estimation method and battery model based parameter identification method. Figure 6 Lithium-ion battery double tank model 2.2.4 Functional State (SOF) Estimation It is estimated that the battery SOF can be simply considered to be estimating the maximum available power of the battery. Commonly used SOF estimation methods can be divided into two categories: battery-based MAP map-based methods and battery-based dynamic methods. 2.2.5 Residual energy (RE) or energy state (SOE) estimates RE or SOE is the basis for the estimated mileage of electric vehicles. Compared with the percentage of SOE, RE is more intuitive in the actual vehicle mileage estimation. Figure 7 Battery residual energy (RE) FIG. 8 is an EPM (energy prediction method) for a battery accurate discharge energy prediction method suitable for dynamic conditions. Figure 8 Battery residual discharge energy prediction method (EPM) structure 2.2.6 Fault Diagnosis and Safety Status (SOS) Estimation Fault diagnosis is one of the necessary technologies to ensure battery safety. Safety status estimation is one of the important items in battery fault diagnosis. The BMS can give the battery fault level according to the safety status of the battery. 2.2.7 Charge Control Lithium deposition is the main cause of battery life. At present, the mechanism of lithium deposition has been studied. The charging management based on lithium state recognition will be the main research direction in the future. It should be done to ensure that the battery anode does not undergo lithium deposition. It is possible to increase the charging current and shorten the charging time. 2.2.8 Battery Consistency and Balance Management The inconsistency of the single cell will ultimately affect the life of the battery pack, mainly due to the difference in cell battery capacity attenuation (unrecoverable) and the difference in charge. The latter can be compensated by an equalization method. The battery equalization algorithm is divided into a voltage-consistent equalization strategy, a SOC-based equalization strategy, and an equilibrium strategy based on remaining charge. The last kind of equalization algorithm has wider constraints and higher efficiency (Figure 9). Figure 9 Schematic diagram of dissipative equalization based on remaining charge capacity 3 Conclusion The basic research methods of lithium ion battery management system are: 1) Research on the mechanism of lithium-ion batteries to gain an in-depth understanding of the evolution of battery performance; 2) Test and study the performance of lithium-ion batteries to determine the primary and secondary factors and laws affecting battery performance; 3) Establish a battery system model that can be practically applied to the battery management system by using a mechanism-based, semi-empirical or empirical modeling method; 4) During operation, based on the data that can be collected, identify the battery system parameters online (online or offline), estimate the battery status (SOC, SOH, SOF, SOE, and fault), and notify the vehicle controller through the network to ensure vehicle safety. Reliable operation. 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