Research On Key Technologies Of Vehicle Lithium-ion Power Battery Management System Based On Embedded MCU

The emission of pollutants from conventional fuel vehicles has increasingly become the focus of people’s attention.Electric vehicles have great advantages in energy conservation and emission reduction.As governments of the countries increase policy support for electric vehicles,the output of electric vehicles shows a rapid growth momentum.As an on-board energy system of electric vehicles,lithium-ion battery has become a key battery type adopted by most automobile manufacturers.Lithium-ion power batteries have the risk of deflagration in the case of high voltage electric shock and overcharge and overdischarge,state of charge(SOC),state of health(SOH)and state of power(SOP)cannot be directly measured when the electric vehicle is working.Therefore,a battery management system(BMS)is needed to manage power lithiumion batteries.In battery management system,high-precision SOC estimation is the key to the whole battery management system.Meanwhile,high-precision estimation of SOP and SOH can also improve the battery life.The main control chip of the vehicle battery management system generally adopts an embedded microprocessor unit(MCU).Therefore,the vehicle-based battery management system must consider the estimation of battery SOC,SOH and SOP with high precision and high robustness in the case of limited computing resources.In addition,for the battery pack,the consistency of the battery will affect the overall service life of the battery pack.It is also important to study the balance technology of the battery pack to improve the overall service life of the battery pack.This paper mainly studies the key technologies of SOC,SOH,SOP estimation,battery pack equalization and BMS software and hardware design of power battery management system based on vehicle embedded MCU.The following is the main work of this paper:(1)The square root cubature Kalman filter(SRCKF)algorithm has been developed to estimate the SOC of batteries.SRCKF calculates 2n points that have the same weights according to cubature transform to approximate the mean of state variables.After these points are propagated by nonlinear functions,the mean and the variance of the capture can achieve third-order precision of the real values of the nonlinear functions.SRCKF directly propagates and updates the square root of the state covariance matrix in the form of Cholesky decomposition,guarantees the nonnegative quality of the covariance matrix,and avoids the divergence of the filter.Simulink models and the test bench of extended Kalman filter(EKF),Unscented Kalman filter(UKF),cubature Kalman filter(CKF)and SRCKF are built.Three experiments have been carried out to evaluate the performances of the proposed methods.The results of the comparison of accuracy,robustness,and convergence rate with EKF,UKF,CKF and SRCKF are presented.Compared with the traditional EKF,UKF and CKF algorithms,the SRCKF algorithm is found to yield better SOC estimation accuracy,higher robustness and better convergence rate.(2)The development of a novel method to estimate the state of charge(SOC)with low read-only memory(ROM)occupancy,high stability and high anti-interference capability is very important for the BMS in actual electric vehicles.This paper proposes the SRCKF with a temperature correction rule,based on the BMS of a common on-board MCU,to achieve smooth estimation of SOC.The temperature correction rule is able to reduce the testing effort and ROM space used for data table storage(189.3 kilobytes is much smaller than the storage of the MPC5604 B,with 1000 kilobytes),while the SRCKF is adopted to achieve highly robust real-time SOC estimation with high resistance to interference and moderate computing cost(68.3% of the load rate of the MPC5604B).The results of multiple experiments show that the proposed method with less computational complexity converges rapidly(in approximately 2.5 s)and estimates the SOC of the battery accurately under dynamic temperature condition.Moreover,the SRCKF algorithm is not sensitive to the high measuring interference and highly nonlinear working conditions(even with 1% current and voltage measurement disturbances,the root mean square error of the proposed method can be as high as 0.679%)(3)Although much progress has been made in estimating SOC,SOH and SOP,there are still some problems suitable for embedded MCU in the joint estimation of SOC,SOH and SOP.In order to meet the above requirements,a new method based on vector-type recursive least squares(VRLS)+SRCKF fusion algorithm(VRSRCKF)and combined application of first-order and second-order battery models for joint estimation of SOC,SOH and SOP under multi-parameter constraints is proposed.Specifically,based on the first-order battery model,VRLS algorithm is adopted to identify the battery parameters online,and then the parameters identified online and offline are fused according to the working conditions.In SRCKF algorithm,the fused battery parameters are used to estimate the battery SOC.With the passage of time,the estimated battery capacity based on RLS algorithm tends to converge.According to the measured temperature to look up the temperature-battery rated capacity table,the battery rated capacity can be got at the current temperature.The battery SOH can be obtained by comparing the estimated capacity with the rated capacity.The multi-parameter constrained SOP estimation algorithm based on the battery DP model can improve the accuracy of SOP estimation without increasing the computational complexity.The experimental results show that the proposed joint estimation algorithm has a moderate computational complexity and can estimate SOC,SOH and SOP very well.It has a broad application prospect in real vehicle BMS based on vehicle-mounted embedded MCU.(4)Lithium-ion battery packs for electric vehicles are usually composed of multiple battery cells.There will inevitably be inconsistencies in the production and use of battery cells.If the inconsistency of battery packs is ignored,the overall service life of battery packs will be shortened.In order to solve the inconsistency of monomers in battery packs,a new method of active-passive equalization based on working conditions is proposed.This method opens different equilibrium modes according to different working conditions.With the charging condition,the current is stable,and the driver’s driving feeling can be ignored,so the non-destructive active equalization method can be used to balance the battery cell quickly.Under the driving and static conditions,the passive equalization method with small rate current can satisfy the driving feeling and vehicle safety very well.(5)Based on freescale’s vehicle-mounted embedded MCU(MPC5604B)widely adopted in the industry,the main purpose of this research subject is to design a BMS software and hardware system adopting the above key technologies and apply it in the real vehicle.The software development method based on model based design(MBD)is proposed in the paper.The BMS with master-slave architecture is composed of 1 battery managenent unit(BMU),4 Local electric control units(LECU),8 active balance control units(BCU)and controller area network(CAN).The methods of SIMULINK modeling,simulation,transcoding and code integration and debugging in codewarrior are introduced in detail.The products of the BMS have been applied in a pure electric vehicle,and the results of bench test and real vehicle test are well.The functions and performances meet the design objectives of this project.