Abstract
Battery SOC is affected by many uncertain factors, so it is difficult to predict the exact value. In view of this situation, a convolution neural network prediction method optimized by genetic algorithm is proposed. Taking voltage_measured, current_measured, temperature_measured, current_load and voltage_load as input vectors of the neural network, genetic algorithm is used to generate the initial weights of neural network, and the GA-CNN battery SOC prediction model is constructed. The software and hardware GA-CNN neural network is realized by C language and FPGA programming respectively. The software implementation verifies the correctness of the algorithm, and the hardware implementation achieves the effect of real-time monitoring. The experiment results of C language show that the battery SOC prediction results based on GA-CNN neural network are more accurate. The hardware simulation results are consistent with the software results.
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Guo, W., Li, J. (2019). A Battery SOC Prediction Method Based on GA-CNN Network and Its Implementation on FPGA. In: Xu, W., Xiao, L., Li, J., Zhu, Z. (eds) Computer Engineering and Technology. NCCET 2019. Communications in Computer and Information Science, vol 1146. Springer, Singapore. https://doi.org/10.1007/978-981-15-1850-8_7
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DOI: https://doi.org/10.1007/978-981-15-1850-8_7
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