BP neural network approach for heat generation rate estimation of power battery for electric vehicles
The heat generation of battery is one of the critical indicators of battery thermal management.Accurate estimation of the heat generation rate of the battery is crucial to building an efficient battery thermal management system and thereby facilitating the safe driving of electric vehicles.However,most researchers currently rely on model-based simulations to estimate the heat generation rate of electric vehicle batteries.There are some disadvantages in this method,such as time-consuming and only application to some specific battery condition,which impede its wide application in addressing real-time heat generation rate of battery for electric vehicles.This paper proposed a precise battery heat production rate estimation strategy based on BO-Adam-BP neural network approach,that was,an electric vehicle power battery heat production estimation model based on BP neural network.The model used the Bayesian optimization algorithm(Bayesian optimization,BO)to select hyperparameters of BP(back propagation,BP)neural network,and used Adam(adaptive momentum estimation)optimization algorithm to speed up the convergence speed and improve the accuracy and stability of the model.Comparing to the battery heat generation power of constant current discharge experiments under different discharge rates and various ambient temperatures,the results showed that the estimated average error of the model was 5.01%,and the maximum error was only 5.53W.The R2 fitting index could reach up to 99.98%,proving that the proposed battery heat production estimation model had achieved high accuracy and strong robustness,providing a paradigm structure for real-time heat production estimation of electric vehicle batteries.
electric vehiclepower batteryBP neural networkheat generation rate estimationoptimization algorithm
万方数据
维普
