摘要
估算动力电池荷电状态是电池管理系统中的一个重点及难点,提出了利用回跳电压与神经网络相结合的方法估算电池的剩余容量。对动力电池离线后所产生的回跳电压进行了研究,并建立回跳电压与电池的剩余容量之间的关系。通过对比分析本算法估测的动力电池SOC值和实际测量值,验证了该方法的可行性。
SOC estimation of dynamic-battery is a difficult point of battery management. Proposed the combination of the rebound voltage method and the neural network method to estimate battery remaining capacity. Studied the rebound voltage of lead-acid battery when off-lined and found the relationship between the rebound voltage and residual capacity of battery. Through the contrast and analysis of the estimated SOC value and the measured one, verifies the feasibility of this method.
出处
《湖南工业大学学报》
2013年第2期59-62,共4页
Journal of Hunan University of Technology
基金
国家自然科学基金资助项目(51077047)
湖南省教育厅科研基金资助项目(12C0067)
关键词
回跳电压
剩余容量
神经网络
放电深度
： rebound voltage
state of charge
neural network
depth of discharge
