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| Peak power estimation of power battery discharge based on SA+BP hybrid algorithm |
| College of Mechanical and Vehicle Engineering, Hunan University, Changsha, Hunan 410082, China |
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Abstract To solve the problems that only single factor was considered for battery peak power estimation and the battery model was simple, a new estimation method was proposed. Taking the ternary lithium power lithium batteries as research object, considering the comprehensive effects of battery temperature, charging state and ohm resistance on the power state, a neural network battery model on simulated annealing and back propagation（SA+BP）hybrid algorithm was eatablished based on data statistics and machine learning by neural network toolbox and Matlab programming. Hybrid pulse power characteristic (HPPC) method was used to conduct the experiments. 245 groups valid experimental data were obtained with selected 200 groups experimental data as training samples, and the rest experimental data were used as test samples. The simulation results of the training model by single BP algorithm were compared with those by SA+BP hybrid algorithm. The results show that the trained model based on SA+BP hybrid algorithm has better estimation accuracy, which can more accurately describe the peak power of battery.
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Received: 27 March 2019
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2020, Vol. 41 
