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It is difficult to directly measure the SOC value (battery state of charge) of a lithium ion battery, and a large estimation error is caused due to high nonlinearity. In order to reduce the battery SOC estimation error, improve the SOC estimation accuracy. After analyzing the effect of voltage, temperature, current and discharge electricity of lithium-ion battery on battery SOC, a novel Immune Genetic Algorithm (IGA) combined with BP neural network was proposed for SOC value of lithium-ion battery. This method is used for the first time in the estimation of SOC value of lithium-ion battery, using a novel immune genetic algorithm to optimize parameters of BP neural network, optimize the network model, and effectively improve the network learning efficiency and battery SOC value. Finally, through simulations and experiments under the actual conditions of the power battery, the results show that the use of a novel joint estimation algorithm improves the network operating efficiency and the battery SOC value estimation accuracy, estimates the root mean square error control within 2%, and validates this joint estimation algorithm. The feasibility and effectiveness of the solution to the problem of large error in battery SOC estimation.