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In order to improve the accuracy of data-driven prediction by Support Vector Regression (SVR), the traditional Grey Wolf Optimization (GWO) algorithm is improved to optimize the SVR hyperparameters by introducing Tent chaotic mapping for population initialization, improving the convergence method, and combining with simulated annealing algorithm for the parameter optimization problem of SVR. And an IGWO-SVR prediction model is proposed based on the improved GWO algorithm. After applying the proposed IGWO-SVR model to the simulated SOH prediction of NASA lithium battery dataset and the actual production lamp current prediction experiments, the experimental results show that the prediction error of the IGWO-SVR prediction model on the NASA lithium battery dataset is reduced by 23% compared with that of the GWO-SVR model, and there is a significant advantage over both the particle swarm algorithm and the genetic algorithm optimized SVR model. In practice, the IGWO-SVR model has also achieved good results in predicting the lamp current, with a relative error of 2.67% compared to the measured value, which is nearly 7 percentage points lower than that of the GWO-SVR model, proving the value of the model in practical applications.