Abstract
The accurate peak power estimation of a battery pack is essential to the power-train control of electric vehicles (EVs). It helps to evaluate the maximum charge and discharge capability of the battery system, and thus to optimally control the power-train system to meet the requirement of acceleration, gradient climbing and regenerative braking while achieving a high energy efficiency. A novel online peak power estimation method for series-connected lithium-ion battery packs is proposed, which considers the influence of cell difference on the peak power of the battery packs. A new parameter identification algorithm based on adaptive ratio vectors is designed to online identify the parameters of each individual cell in a series-connected battery pack. The ratio vectors reflecting cell difference are deduced strictly based on the analysis of battery characteristics. Based on the online parameter identification, the peak power estimation considering cell difference is further developed. Some validation experiments in different battery aging conditions and with different current profiles have been implemented to verify the proposed method. The results indicate that the ratio vector-based identification algorithm can achieve the same accuracy as the repetitive RLS (recursive least squares) based identification while evidently reducing the computation cost, and the proposed peak power estimation method is more effective and reliable for series-connected battery packs due to the consideration of cell difference.
Highlights
With the global issues of energy shortage and environmental degradation, the lithium-ion battery, because of its high energy and power density and long service lifetime, has become one of the most readily available and low-cost energy storage components in electric vehicles (EVs)
We find that the parameters obtained by the proposed method are very close to the reference values identified by the repetitive recursive least squares (RLS) algorithm, especially after the convergence of the algorithm
This is because the ratio vector-based algorithm should first use the results identified by traditional RLS as the mean value of the parameters, and determine the parameters for each individual cell with the ratio vectors
Summary
With the global issues of energy shortage and environmental degradation, the lithium-ion battery, because of its high energy and power density and long service lifetime, has become one of the most readily available and low-cost energy storage components in electric vehicles (EVs). This technique suffers from the drawbacks that only static battery characteristics are considered, massive experiments should be implemented to obtain the characteristic map, and a significant amount of non-volatile memory is required which increases the cost of the BMS Another technique is the model-based estimation [4,5,6,7,8,9,10,11,12]. The results indicate that with the online parameter identification considering obtained, the limitation, imposed by the weakest cell, on peak power is taken into cell difference, the proposed power estimation method is adaptive to different aging states, working consideration in power estimation. The forecast period of power prediction is less than tens of seconds, the influence of temperature and SOC changes canBattery be neglected
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