A Fast Prediction of Open-Circuit Voltage and a Capacity Estimation Method of a Lithium-Ion Battery Based on a BP Neural Network - Wenkang Bao; Haidong Liu; Yuedong Sun; Yuejiu Zheng (Batteries, 2022)
The battery is an important part of pure electric vehicles and hybrid electric vehicles, and its state and parameter estimation has always been a big problem. To determine the available energy stored in a battery, it is necessary to know the current state-of-charge (SOC) and the capacity of the battery. For the determination of the battery SOC and capacity, it is generally estimated according to the Electromotive Force (EMF) of the battery, which is the open-circuit-voltage (OCV) of the battery in a stable state. An off-line battery SOC and capacity estimation method for lithium-ion batteries is proposed in this paper. The BP neural network with a high accuracy is trained in the case of sufficient data with the new neural network intelligent algorithm, and the OCV can be accurately predicted in a short time. The model training requires a large amount of data, so different experiments were designed and carried out. Based on the experimental data, the feasibility of this method is verified. The results show that the neural network model can accurately predict the OCV, and the error of capacity estimation is controlled within 3%. The mentioned method was also carried out in a real vehicle by using its cloud data, and the capacity estimation can be easily realized while limiting inaccuracy to less than 5%.