Application On Energy Estimation And Equilibrium Conversion Control Of Lithium-ion Battery

Lithium-ion battery has become the first choice for electric vehicle(EV) batteries because of its various advantages, such as higher energy density, higher voltage level, no memory effects and no pollution to the enviorment. Power batteries as the energy storage elements of EVs, their state of charge(SOC) in the driving process real-timely is needed to know for determining endurance mileage. It is very difficult to estimate the SOC of batteries online and improve estimation accuracy, because the SOC of batteries is affected by the change of various factors. The dissertation focuses on improving the battery SOC estimation accuracy by using progressive intelligent estimation method. High power applications of EV require packs that may have dozen or even a hundred cells connected in series. In fact, the overall life and safty of the battery pack is affected by the inconsistency of any single cell. The dissertation focuses on prolonging the life and ensuring safety of the battery pack by use of a single cell monitoring and energy conversion control strategy. The specific contents are as followings:1. The factors influencing the SOC of lithium-ion battery have been analyzed, and the performance weight has been measured in the case of different change factors. The different characteristics of the lithium-ion battery SOC estimation methods are compared respectively on the basis of the measurement. Progressive intelligent SOC estimation method is identified in this paper.2. Back propagation(BP) neural network algorithm has been used to estimate the SOC. Estimate error is analyzed and find that a larger edge error exist in this algorithm. To solve this problem, genetic algorithm has been utilized to optimize the BP network. The effects have been compared before and after optimization. The final estimate error after optimization has been controlled in 2.3% or less. Kalman filter estimation method and genetic algorithms are combined in order to estimate the SOC more accurate. The state space equation is set up in the genetic algorithm Kalman filter according to the battery dynamic model. The SOC is estimated according to the preceding sample data. The SOC estimation value obtained is closer to the real value and its estimate errors are less than 2%. The extended Kalman filter is studied to further estimate the SOC. Improved battery Thevenin equivalent circuit model is set up to add a Resistance-Capacitance Circuits on the basis of the original model. State equation of Kalman filtering spreads as the first order Taylor series and an extended Kalman filter is obtained to estimate the nonlinear system. The battery health status is joined in the process of operation as an influential factor. The estimation accuracy is improved greatly for its less than 1.5% estimate errors in the conditions of constant current and constant resistance discharging.3. The monitoring and management of single cell of 13 lithium-ion batteries(12 in series and 1 spare) are controlled by software of virtual instrument platform. When the power of a single cell is low, the single cell is switched out of the system and is replaced by the spare battery. The battery pack still supplies power normally. The data are collected by PCI-6251 data acquisition card. It has a friendly artistic interface and estimate SOC of batteries online by programming on LabVIEW and backup battery switchover system is achieved to ensure the normal power supply when a single individual battery power is low. Energy equalization module is added to the system when the battery in low power is switch out. The single cell in electric vehicles can be charged online through electric vehicle energy storage unit which is supplied power by electric vehicle braking energy recovery system and the control of energy balance module. The eaqualization experiments of 13 lithium-ion batteries(12 in series and 1 spare) show that single battery switch operated normally and the device has advantages of well balanced results and improves usage efficiency of lithium-ion battery pack. A control system of electric car is designed after the experiment of the control of single cell and equalization. Protective control in constant current of 5 battery packs(13 in series) solves problem of the electric vehicle battery discharge current surge leading to thermal runaway. The cicuit combined super capacitors and inverter device makes the electric vehicles to start acceleration smoothly and fit to drive in different road conditions.In conclusion, the application on energy estimation and equilibrium conversion control of lithium-ion battery is carried out in-depth step by step. The experimental testing has been done, it provids a foundation for further pre-industrialization.