Offline data driving based storage battery combined state estimation method

Abstract

The invention discloses an offline data driving based storage battery combined state estimation method. The method is based on offline test data driving, characteristic points extracted from offline data serve as an online identification knowledge base, and the battery SOC and capacity are estimated in a combined way online; and stored voltage change data after charging and discharging is used tocalculate an equivalent OCV value, and SOC of the battery is obtained according to the obtained offline data SOC=s(OCV, T). The SOC of a characteristic point is obtained by carrying out characteristicidentification on the battery voltage data during current-constant charging. The method is low in computational complexity, high in estimation precision, and suitable for state estimation of each battery among a lot of batteries in serial connection.

Description

technical field [0001] The present invention designs a battery joint state estimation method driven by offline data, which is applicable to but not limited to lithium iron phosphate batteries. The state estimation specifically involves battery capacity estimation and battery state of charge (SOC) estimation. The present invention tests the battery offline, Calculate and extract feature data, and combine online feature identification method for state estimation. Background technique [0002] In the application field of domestic electric vehicle power lithium-ion batteries, lithium iron phosphate accounts for about 50%. In actual use, the battery management system has a large error in estimating the capacity and SOC of lithium iron phosphate batteries, resulting in the expected battery life of electric vehicles. SOC estimation error will lead to misoperation of battery management and affect battery life. [0003] Lithium iron phosphate batteries have a wide voltage platform, ...

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Problems solved by technology

[0002] In the application field of domestic electric vehicle power lithium-ion batteries, lithium iron phosphate accounts for about 50%. In actual use, the battery management system has a large error in estimating the capacity and SOC of lithium iron phosphate batteries, resulting in the expected battery life of electric vehicles. Inaccurate estimation, poor driving experience, SOC estimation error will lead to misoperation of battery management and affect battery life

[0003] Lithium iron phosphate batteries have a wide voltage platform, and the voltage platform in the SOC range of 20% to 85% is very flat. When the SOC changes by 1%, the terminal voltage changes even less than 1mV, so it is very difficult to estimate the SOC based on the terminal voltage

[0004] The capacity of lithium iron phosphate batteries gradually decays with the increase in the number of cycles, but the standard measurement of capacity cannot be performed during use, so it needs to be passed during use to identify the battery capacity online. The current online capacity estimation algorithm in the industry has low accuracy

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