Precise state of charge (SOC) of traction battery is a key factor for power distribution of HEV power system, total control of vehicle and cost reduction.
精确的动力电池剩余电量(SOC)是混合动力系统进行动力分配的重要依据，也是整车控制和降低使用成本的关键。
Under the condition of given parameter, the two common driving cycles were simulated with the instantaneous optimization strategy based on SOC (state of charge) balance.
在设定参数条件下，以基于电量平衡的瞬时优化策略对两种常见行驶工况进行了整车仿真。
It was very importance for the battery management system in electric vehicles to measure and estimate the state of charge (SOC) of lead-acid batteries.
铅酸电池的荷电状态(SOC)的测量和估计，对电动汽车的电池管理系统极为重要。
According to feature of individual battery and equalization method for state of charge (SOC), the number of measuring points is determined.
根据所选用的单体镍氢电池的特点，以及荷电状态SOC均衡方法来确定电池包参数测量点的数量。
For predicting the state of charge (SOC) of lithium-ion rechargeable battery accurately, a method based on electrochemical impedance spectroscopy was presented.
为了准确预测使用过程中的电池荷电状态，提出了电化学阻抗谱预测法。
The weakened battery with its limited storage capacity and ability to give and receive charge adversely affects the battery's State of charge (SOC).
虚弱的电池以其有限的存储容量和能力去给予和接受电荷产生不良影响的电池的状态(SOC)负责。
This article dedicated to the research on the design of battery management system and battery state of charge (SOC) estimation.
本文致力于电池管理系统设计及电池荷电状态(SOC)估算的研究。
The state-of-charge(SOC) is an important parameter for the electrical vehicle.
电动车电池管理系统的核心任务是对电池荷电状态（SOC）进行预测。
Taking the state of charge (SOC) as fuzzy input and the parameters of the battery module as fuzzy output, the parameters of the model are determined by fuzzy inference.
的状态的充电量(SOC)，以作为模糊输入和模糊输出的参数的电池模块，模型的参数，通过模糊推理确定。
Existing battery State of Charge (SOC) estimation methods are time consuming for the training and learning process, and it restricts the application in electrical vehicles.
现有电池荷电状态(SOC)估计方法所需训练和学习时间较长，很难满足动力电池的实时性要求。
Existing battery State of Charge (SOC) estimation methods are time consuming for the training and learning process, and it restricts the application in electrical vehicles.
现有电池荷电状态(SOC)估计方法所需训练和学习时间较长，很难满足动力电池的实时性要求。
应用推荐