CAJ | 학술논문

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。
대모리리자동력전지진행료시험연구，이분석충전배솔、초시하전상태화배경온도대리리자동력전지충전온승적영향，병근거획득적시험수거，건립료기우자괄응신경모호추리계통적전지충전온승예측모형。해모형이충전배솔、초시하전상태화배경온도작위수입，이충전온승작위수출，대시험수거진행훈련후，즉가준학예고전지재불동충전조건하적충전온승정황。해방안무수용수학모형준학묘술각영향인소여충전온승지간적복잡관계，역우실현，가이식도전지관리계통평태상，이실현충전과정중온도적유효예측화관리。
An Experimental study is conducted on a lithium-ion power battery to analyze the effects of char-ging 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, in-itial 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.