Ni-MH, with high electric energy density and high stability, is the main backup battery of mine power supply. To improve the estimation accuracy of battery SOC (state of charge) and prolong its service life, this paper takes the single Ni-MH battery as the experimental object, and adopts an improved BP neural network that adds an adaptive rate momentum term to increase the discharged amount of electricity. The input characteristics of the network are the same as temperature, current and terminal voltage. According to the variance of the error between the expected output and the actual output of the training sample, the momentum term is adjusted; The adaptive learning rate is increased according to the change direction of the back propagation error. By comparing the standard BP neural network algorithm, the convergence speed is increased by 70%, and the generalization error is within 4%.

Wireless charging technology is wireless power transmission through electromagnetic wave. Wireless charging technology can make the product design get rid of the shackles of cable and make the product design more compact and miniaturized. This paper introduces the wireless charging technology. in order to overcome the heavy charging cable, realize wireless charging anytime and anywhere; Electronic equipment has the trend of miniaturization, low power consumption and standardization of wireless charging; The application of Internet of things and the sensor of wireless sensor network do not need to connect, power supply and send and upload data through cable. As well as wireless charging, charging efficiency, its impact on the human body, ultra-low power sensors and commercial operation of wireless charging technology are the research focus of wireless charging technology.

No abstract

This paper introduces the definition of SOC estimation and analyzes the common estimation methods, including discharge experiment method, open circuit voltage method, internal resistance method, ampere hour method, linear model method, neural network algorithm and Kalman filter method. Through the comparison and analysis of various methods, it provides a scientific choice for the SOC estimation method of nickel hydrogen battery.

Since Ni MH battery has the characteristics of high energy density and good stability, it is mainly used as a backup power supply for mining at present, but the accuracy of estimation the SOC of the power supply is poor. To enhance the precision of estimation and extend the lifespan of the battery, based on a single nickel-hydrogen battery, this paper applies a new BP neural network to rise the self-adaption and momentum terms. At the same time, the discharged power is included in the Input feature quantity of the estimation network. The temperature, current and terminal voltage are also included in the input characteristic quantity of the estimation network. There is output during training, and then the momentum term is adjusted based on the Variance of variance error by comparing the actual output. The error of the estimated network should be back propagated from the output to the input to correct the weight. The direction of change in the error determines the adaptive learning rate of the improved BP neural network. The speed of the algorithm is increased by 72% and the error rate is less than 5%, compared to the traditional BP neural network algorithm.