提出了一种最小二乘支持向量机的电池剩余电量预测新模型。
A novel prediction model for remaining capacity of batteries based on least square support vector machine (LS-SVM) was proposed.
该方法技术先进，实验结果表明测试精度高，为蓄电池剩余电量的检测提供了有效的途径。
It shows that this residual capacity indicator is advanced in technology and its accuracy is high. It provides a very efficient approach to measure the residual capacity.
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。
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.
电池剩余电量估计系统不仅能够防止电池过放电或过充，而且对于电动汽车行使过程中起到了汽车汽油表的重要作用。
SOC system can not only avoid the overcharge or overdischarge of battery, but also play as a important role as auto gas instrument.
本文在基于前人的基础上进行了进一步研究，对电池管理系统中的难点问题：动力电池剩余电量的估计，提出了一种新的预测方法。
Based on the achievement of predecessors, a further study on the difficult battery management system was given, proposed a new forecasting method for battery power remaining estimated.
多数大电池供电的产品提供电池电量计来指示电池的剩余电量。
Most battery-powered products attempt to provide a fuel gauge of remaining battery capacity.
多数大电池供电的产品提供电池电量计来指示电池的剩余电量。
Most battery-powered products attempt to provide a fuel gauge of remaining battery capacity.
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