Electric vehicle battery life prediction method and extension method

Abstract

The invention provides a predication method for service life of an electric vehicle lithium battery. The service life of the battery is predicated according to obtained working temperature of the battery, charging frequency, discharging frequency and the ratio between battery energy required for a driver's driving in one day and energy of the fully charged battery just out of a factory. The predication method for the service life of the electric vehicle lithium battery comprises predicating the service life range of the battery according to the obtained driver's character parameters, charging strategies, environmental temperature, driving distances on a freeway and an urban road in workdays and at weekends. The invention further provides a prolonging method for the service life of electric vehicle lithium battery according to the formula. The method provides the basis for accurate predication of the service life of the lithium battery, the service life of the lithium battery of an electric vehicle is predicted by considering behavior characteristics of the driver, predication results are true, the service life of the lithium battery can be predicted without needing hardware, and the cost is extremely low.

Description

technical field [0001] The invention relates to the field of electric vehicles, in particular to a method for predicting battery life of an electric vehicle and a method for extending it. Background technique [0002] Current environmental concerns and possible oil production issues have spurred the development of various electric vehicles. Compared with traditional cars, electric vehicles can play an important role in reducing pollutant emissions and energy consumption. In electric vehicles, the life and cost of on-board batteries will directly affect the performance, life and cost of electric vehicles, and predicting battery life has become an important issue today. [0003] First of all, in existing studies, researchers only use experiments to prove the qualitative relationship between battery life and related factors, and do not provide mathematical quantitative predictions for battery life. [0004] In addition, most of the existing research on battery life focuses on...

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Problems solved by technology

[0003] First of all, in the existing research, the researchers only proved the qualitative relationship between battery life and related factors by experiments, and did not provide mathematical quantitative prediction for battery life

[0004] In addition, most of the existing research on battery life focuses on the physical and chemical processes of the battery, such as state of charge (SOC), operating temperature, charge and discharge times, but ignores the influence of the driver

As the driver is the operator of the electric vehicle, his behavior directly affects the life of the battery, so it will not be possible to fully and truly predict the battery life of the electric vehicle without considering or not fully considering the behavior characteristics of the driver.

[0005] In summary, the prior art lacks a mathematical quantitative prediction method for battery life of electric vehicles that comprehensively considers factors related to battery life. In addition, there is still a lack of battery life prediction methods for electric vehicles that comprehensively consider driver behavior characteristics in the prior art.

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