Abstract
PurposeThe operation state of lithium-ion battery for vehicle is unknown and the remaining life is uncertain. In order to improve the performance of battery state prediction, in this paper, a joint estimation method of state of charge (SOC) and state of health (SOH) for lithium-ion batteries based on multi-scale theory is designed.Design/methodology/approachIn this paper, a joint estimation method of SOC and SOH for lithium-ion batteries based on multi-scale theory is designed. The venin equivalent circuit model and fast static calibration method are used to fit the relationship between open-circuit voltage and SOC, and the resistance and capacitance parameters in the model are identified based on exponential fitting method. A battery capacity model for SOH estimation is established. A multi-time scale EKF filtering algorithm is used to estimate the SOC and SOH of lithium-ion batteries.FindingsThe SOC and SOH changes in dynamic operation of lithium-ion batteries are accurately predicted so that batteries can be recycled more effectively in the whole vehicle process.Originality/valueA joint estimation method of SOC and SOH for lithium-ion batteries based on multi-scale theory is accurately predicted and can be recycled more effectively in the whole vehicle process.
Highlights
The on-board battery is the main energy supply unit to drive the electric vehicle
Method based on analysis model 2.1 Establishment of aging model of lithium-ion battery Li-ion battery packs will suffer from self-discharge and leakage due to prolonged outage and the actual state of charge (SOC) value will be inaccurate
In order to obtain the values of the variables in the model parameters, an improved fitting method is adopted and used in this paper to fit the relationship between SOC and OCV, while the use of the end-voltage exponential fit will further yield the values of capacitance and resistance in the equivalent model
Summary
The on-board battery is the main energy supply unit to drive the electric vehicle. The battery can provide the energy required for vehicle driving power and can provide energy support for the electronic system of the whole vehicle. The performance indexes related to the energy, power and multiplying power characteristics of the battery significantly affect the performance of the vehicle. The battery management system (BMS) and its key technology of real-time control module become the research focus of.
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