Abstract
A battery’s capacity is an important indicator of its state of health and determines the maximum cruising range of electric vehicles. It is also a crucial piece of information for helping improve state of charge (SOC) estimation, health prognosis, and other related tasks in the battery management system (BMS). In this paper, we propose an improved recursive total least squares approach to online capacity estimation, which is based on the constrained Rayleigh quotient in terms of battery capacity. This approach accounts for errors in both the SOC and accumulated current measurements not traditionally considered in the battery capacity model to give an unbiased estimation. Moreover, the forgetting factor, updated by minimizing the Rayleigh quotient of the capacity estimation model, is applied to track the changes in the model and get a more precise estimation of the capacity. Finally, the performance of the proposed algorithm is validated via simulation and experimental studies on lithium-iron phosphate batteries. The estimation results show that the proposed algorithm improves capacity estimation accuracy.
Highlights
To meet the need to reduce fossil fuel dependence and emissions from traditional transportation, electric vehicles (EVs) are being studied and manufactured extensively around the whole world [1]
In order to achieve optimum performance and long life from batteries, their state of health (SOH) is crucial information, in addition to their state of charge (SOC) in the battery management system (BMS) [5, 6]. e battery capacity is considered as one important indicator of SOH, which reflects that maximum electrical charge can be stored into the battery and determines the maximum cruising range for EVs [7]
Based on the above algorithms, we propose an approach employing an improved recursive total least squares with variable forgetting factor (VFF-RTLS) and a coulomb counting model considering errors in both the estimated SOC and the current measurement. e forgetting factor is controlled by minimizing the constrained Rayleigh quotient through the steepest descent method. is approach will improve capacity estimation performance. e rest of this paper is constructed as follows: Section 2 describes the capacity estimation model for the battery
Summary
To meet the need to reduce fossil fuel dependence and emissions from traditional transportation, electric vehicles (EVs) are being studied and manufactured extensively around the whole world [1]. Battery capacity could only be measured offline, by depleting a fully charged battery with a low current rate at a specific temperature This method is not suitable for real-time applications. Some approaches based on coulomb counting have been researched to estimate the capacity in real time [30, 31] In these studies, the SOC estimation error is not considered, which could affect the capacity estimation accuracy. Algorithms with smaller forgetting factor weigh more on tracking the time-varying capacity at the expense of more sensitivity to the current measurement and SOC estimation error; on the contrary, large forgetting factor improves robustness but compromises the tracking capability. Based on the above algorithms, we propose an approach employing an improved recursive total least squares with variable forgetting factor (VFF-RTLS) and a coulomb counting model considering errors in both the estimated SOC and the current measurement.
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