Self-adaptive online detection method for state of charge of battery

Abstract

The invention discloses a battery state-of-charge adaptive online detection method. The method comprises the following steps: 1, acquiring battery temperature, terminal voltage and working current data in real time; step 2, establishing a battery state space model based on the sampling data by taking a battery working current as an input variable, a battery charge state as a state variable and a battery end voltage as an output variable, and determining a capacity threshold and an end voltage error threshold; 3, if the self-adaptive switch module judges to be true, calculating a battery charge state initial predicted value and a battery charge state optimal predicted value by adopting an extended Kalman filtering algorithm, and if the self-adaptive switch module judges to be false, calculating the battery charge state initial value and the battery charge state optimal predicted value by adopting the extended Kalman filtering algorithm at the same time; and introducing a nonlinear proportional-integral observer based on a BP neural network to calculate an estimated compensation value of the state of charge of the battery and an optimal estimated value of the state of charge of the battery.

Description

Technical field [0001] The present invention belongs to the field of automotive battery detection technology, specifically a battery charge state adaptive online detection method. Background [0002] As one of the main means of transportation in the future, electric vehicles have certain requirements for their performance and mileage such as starting, acceleration, and climbing. The battery pack is the core component of the electric vehicle, providing driving power for the whole vehicle, and the performance of the power battery largely determines the performance of the electric vehicle. Battery charge state is an important parameter reflecting the battery energy state, only accurate estimation of battery charge state can effectively improve the utilization rate of the power battery, extend the service life of the battery and optimize the driving scheme. However, the battery state of charge is the amount of state implied in the power battery, which is difficult to directly measure...

AI Technical Summary

Problems solved by technology

However, the dynamic characteristics of the power battery are obviously nonlinear, so if the given initial value of the iteration deviates significantly from the actual state, the estimation results of the Kalman filter will be inaccurate or even divergent.

Images