Design of automotive 18650 lithium ion battery management system based on DSP

　　Fierce competition in the automotive market requires designers to shorten product development cycles. In the design and development of traditional automotive electronic controllers, the overall design of the controller, overall performance analysis and control strategy optimization usually require a lot of time, manpower and material resources, resulting in large investment and low efficiency. In addition, this development method is error-prone and does not enter real-time online testing until final calibration. If errors are made during the initial design and are not discovered in time, most of the work will have to be redone and the development cycle will become longer. It can be seen that traditional research and development methods cannot meet the needs of the market, and a new design concept is necessary to adapt to the needs of the market.

　　1V mode design method and automatic code generation

　　1.1V mode design method

　　As shown in Figure 1, compared with traditional design methods, the V-mode design method applies the principles of system engineering to the development of modern automotive electronic systems. It is a cyclic design mode. Its characteristic is that whether you are developing, programming or testing, you always work in the same environment, and every step of the development process can be verified. It is based on powerful computing simulation tools, and the entire design process is completed on the same platform, achieving seamless connection from the proposal of design concepts, to rapid prototyping, and then to ECU products. The most direct effect of adopting this method is to accelerate and simplify the development process, eliminate errors in time, and greatly reduce the workload of engineers.

　　1.2 Use Simulink to achieve automatic code generation

　　Automatic code generation is at the bottom of the V model and is the most critical step in the entire development process. Its purpose is to achieve rapid iteration in the development process to improve development efficiency. The quality of code generation directly affects the reliability and stability of the system.

　　Simulink is a platform for multi-domain simulation and model-based design of dynamic systems. It provides an interactive graphical environment and a rich module library. According to the functional requirements of the system, first build the system model in the MATLAB/Simulink environment and conduct simulation analysis. Use the Simulink debugger to inspect simulation results and locate and diagnose unexpected behavior in your model. Once the results are verified, the C language project file for TI compiler can be automatically generated through RTW (Real-timeworkshop), and further compilation, connection and downloading can be completed, and finally run on the hardware platform.

　　RTW is a tool used together with MATLAB and Simulink. It can be used to generate code directly from Simulink models and automatically create programs that can run in real time. By default, RTW generates highly optimized and fully commented C code. In addition to the MATLAB function module and the module that calls the M-file S function, code can be generated for any Simulink model, including linear, nonlinear, continuous, discrete, and mixed models.

　　From the perspective of the entire process, engineers only need to build the model and verify the correctness of the model in Simulink, without writing any code, to obtain reliable and accurate code.

　　2Embedded TargetforTIC2000 toolbox

　　TargetforTIC2000 integrates TI's eXpressDSP tool into Simulink, which is a connection tool between MATLAB and TICCS. It can make MATLAB, MATLAB toolbox, TICodeComposerStudio integrated development environment (CCSIDE) and RTDX (Real-TimeDataExchange) work together.

　　The TargetforTIC2000 toolbox consists of three parts [3]: common tools, chip peripheral module library, and optimization library. Commonly used tools include real-time data exchange channel module, target controller basic parameter setting module and CAN communication setting module. The toolbox supports C281x series, C280x series and C2400 series DSPs. The optimization library includes a fixed-point arithmetic library and a digital motor control library.

　　Simulink can support four types of C280xDSP peripheral module libraries: memory read and write modules, interrupt management modules, control modules and communication modules. Except for the IIC communication module, this module library provides good support for all modules on the C280xDSP board. When users call these modules of DSP, they only need to set parameters and select the corresponding modules. They do not need to care about how the underlying layer is implemented. The entire model building process is as simple as stacking blocks.

　　3 Application of automatically generated code in 18650 lithium ion battery management system

　　3.1 Functional description of 18650 lithium ion battery management system

　　BMS fuel cell vehicle lithium-ion 18650 lithium ion battery management system BMS (18650 lithium ion battery Management System) is an embedded real-time monitoring system that should have the following functions [4]: 18650 lithium ion battery status monitoring, including measurement and signal processing of 18650 lithium ion battery operating voltage, operating current and operating temperature; Calculation of the maximum charge and discharge power under specific conditions; estimation of the 18650 lithium ion battery pack's state of charge (SoC) (State of Charge) and life state (SoH) (State of Health) under specific operating conditions; high-voltage precharge, overcharge and over-discharge protection, insulation detection and leakage protection ; 18650 lithium ion battery balancing and thermal management; fault diagnosis and communication with the vehicle controller. Figure 3 is the BMS system block diagram.

　　Since the BMS still needs to monitor the 18650 lithium ion battery at certain intervals when the car is parked, the BMS cannot exhaust the 18650 lithium ion battery stored power when parked for a long time, otherwise the car will not be able to start. Therefore, when parking, the BMS must enter a low power consumption mode. When the car starts, the ignition signal from KL15 wakes up the controller from low-power mode and enters normal working mode.

　　3.2 Controller selection

　　It can be seen from the functions of BMS that the control function of the controller only accounts for a small part of the BMS. In real-time parameter estimation and SoC estimation, the algorithm is complex and the amount of calculation is large. The controller needs to complete complex tasks in a short time interval. The recursive operation requires higher computing power and computing speed of the controller. The traditional 18650 lithium ion battery management system uses a single-chip microcomputer as the controller. Since the single-chip microcomputer focuses on control and has limited real-time data computing capabilities, it cannot well meet the requirements of the BMS. TI's TMS320C2000 series DSP combines the characteristics of a microcontroller and a high-performance DSP. It has powerful control and signal processing capabilities and can implement complex control algorithms. This series of DSPs integrates peripherals such as Flash memory, fast and high-precision A/D converters, two enhanced CAN modules, event managers, orthogonal coding circuit interfaces, and multi-channel buffered serial ports. The C2808DSP with 32-bit fixed-point operation can complete 3232-bit multiply-accumulate operations or two 1616-bit multiply-accumulate operations in one cycle. In addition, reading, modifying, and writing operations can be completed on any memory address in one cycle, making the efficiency and program code optimal, fully meeting the requirements of real-time control [5].

　　3.3 18650 lithium ion battery parameter identification and SoC estimation algorithm

　　18650 lithium ion battery monitoring must first carry out modeling, detect the voltage, current and temperature of the 18650 lithium ion battery in real time, and identify the parameters of the model based on these data, thereby indirectly estimating the situation inside the 18650 lithium ion battery. Figure 4 is a lithium-ion power 18650 lithium ion battery model [6]. In the model, C0 is used to describe the capacity of the 18650 lithium ion battery, R0 is used to describe the equivalent ohmic internal resistance of the 18650 lithium ion battery, and the R1 and C1 links with small time constants are used to describe the effects of lithium ions during transmission between electrodes. The impedance, the R2 and C2 links with larger time constants describe the impedance encountered when lithium ions diffuse in the electrode material. The parameters in this model can be obtained through parameter identification.

　　In order to achieve adaptive control and track parameters that change over time, the recursive least squares method is used in the identification process. The internal parameters of the 18650 lithium ion battery are updated according to each sampling value of the voltage and current signals. The basic idea is that this estimated value is equal to the last estimated value plus a correction term. The size of the correction term depends on the output of the model and the actual output. Difference. This method requires that one step of recursive operation must be completed before the next sampling.

　　When loading and running, the vehicle controller requires the BMS to provide a high-precision SoC, and the accuracy is generally estimated to be less than 5%. The SoC of the 18650 lithium ion battery cannot be obtained directly and can only be estimated indirectly by measuring parameters such as 18650 lithium ion battery voltage, current, temperature, and internal resistance. Moreover, these parameters are related to the aging degree of the 18650 lithium ion battery and the unevenness of the 18650 lithium ion battery cells. Common methods currently include open circuit voltage method, current integration method, etc.

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