摘要
对某锂离子动力电池进行了试验研究,以分析充电倍率、初始荷电状态和环境温度对锂离子动力电池充电温升的影响,并根据获得的试验数据,建立了基于自适应神经模糊推理系统的电池充电温升预测模型。该模型以充电倍率、初始荷电状态和环境温度作为输入,以充电温升作为输出,对试验数据进行训练后,即可准确预估电池在不同充电条件下的充电温升情况。该方案无须用数学模型准确描述各影响因素与充电温升之间的复杂关系,易于实现,可移植到电池管理系统平台上,以实现充电过程中温度的有效预测和管理。
An Experimental study is conducted on a lithiumion power battery to analyze the effects of charging current, initial state of charge （ SOC） and ambient temperature on the temperature rise of battery, and based on the experiment data obtained, a prediction model for battery temperature rise is established based on adaptive neuro-fuzzy inference system （ ANFIS） . After the model is trained by experiment data with the charging current, initial SOC and ambient temperature as input and the temperature rise during charging as output, temperature rise of battery in different charging conditions can be accurately predicted. No math model describing the complex relation-ship between battery temperature rise and various influencing factors is required so the scheme is easy to implement, i. e. the model can be transferred to battery management system platform to achieve effective temperature prediction and management during charging process.
出处
《汽车工程》
EI
CSCD
北大核心
2014年第7期799-803,共5页
Automotive Engineering
基金
国家自然科学基金(51277010)
北京高等学校青年英才计划项目(YETP0570)资助
