Battery charge state estimation method based on AEKF and estimation system

Abstract

The invention relates to the battery electrical testing technology, especially relates to a battery charge state estimation method based on AEKF and an estimation system. Battery SOC can be estimated by adopting the self-adaption expansion Kalman filtering algorithm, and the parameter self-adaption adjusting way of the Kalman filtering algorithm can be changed by additionally providing the weighting coefficient based on the forgetting factor, and then the influence of the parameter initial value setting on the whole algorithm is small, and the phenomena of the inaccurate battery SOC initial value calculated by adopting the original ampere-hour integral method and the accumulated error can be overcome, and in addition, the battery SOC can be estimated accurately and reliable. The battery charge state estimation method and the estimation system are advantageous in that the convergence performance is good, the convergence speed is fast, the algorithm transplantability is good, and the use stable and reliable; the estimation method and the estimation system can be used for the electric vehicle battery management field, and can be used for the SOC estimation of the electric vehicle storage battery, and therefore the endurance mileage of the electric vehicle can be calculated accurately, the control of the driver over the vehicle can be facilitated; the estimation method and the estimation system are more suitable for the electric vehicle environment having the strong current fluctuation.

Description

technical field [0001] The invention relates to battery electrical testing technology, in particular to an AEKF-based battery charge state estimation method and estimation system. Background technique [0002] At present, methods for estimating the state of charge (SOC) of a battery are mainly divided into two categories: a direct method and an indirect method. The direct method refers to directly measuring the remaining power of the battery through experimental equipment; the indirect method mainly uses the internal physical and chemical characteristics of the battery, and requires high-precision equipment in the estimation process, so it is difficult to implement in practice. The ampere-hour integration method, open-circuit voltage method, and internal resistance method are indirect methods. The ampere-hour integration method will produce cumulative errors during the calculation process, which will cause the calculated SOC to increase with the increase of charge and discha...

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Problems solved by technology

The direct method refers to directly measuring the remaining power of the battery through experimental equipment; the indirect method mainly uses the internal physical and chemical characteristics of the battery, and requires high-precision equipment in the estimation process, so it is difficult to achieve in practice

The ampere-hour integration method, open-circuit voltage method, and internal resistance method are indirect methods. The ampere-hour integration method will produce cumulative errors during the calculation process, which will cause the calculated SOC to increase with the increase of charge and discharge time. At the same time, the ampere-hour integration method The accuracy of calculating the initial value of SOC is difficult to determine; the open circuit voltage method requires a long period of standing to stabilize the internal voltage of the battery, which is difficult to achieve when monitoring the remaining battery power during the operation of the car; the internal resistance method has difficulties in estimating the internal resistance , it is also difficult to implement on hardware

In addition, indirect methods such as artificial neural network algorithm and Kalman filter algorithm can also be used to estimate battery SOC, but neural network algorithm and Kalman filter algorithm do not have advantages due to the difficulty in setting up the system and the high cost of application in the battery management system

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