2020 · 47 citations · 5 references
EngineeringAutomotive EngineeringElectrical EngineeringAutomotive ElectronicsElectric Vehicle EngineeringElectric Vehicle TechnologyBattery SystemsElectric Vehicle ElectronicsKalman FilteringState EstimationState Of Charge EstimationState Of Charge ManagementAdaptive State Of Charge EstimationBattery ManagementBattery Management System ArchitectureBattery State Of Charge ManagementCoulomb CountingElectrical Power SystemsOnline State EstimationState Of Charge Kalman Filtering
In this paper, an adaptive battery state-of-charge (SOC) estimation method for electric vehicle (EV) battery management system (BMS) is presented. In these days, many parts of EV have been developed with electrical systems, and it makes a growth of energy storage system named battery. Therefore, to make many type of batteries safer and more reliable, BMS is employed and implemented together in EV. The BMS monitors many kinds of battery states and is responsible to manage its charging and discharging. SOC is a key parameter in judging by BMS, and therefore it is certainly important to estimate the SOC accurately. Many SOC estimation methods have been studied, and extended Kalman-filter (EKF) based methods show the best performance. However, they have high computation complexity. In this paper, adaptively combination of EKF and conventional Coulomb counting method is proposed. Finally, the proposed adaptive method shows within 2% error with 70% decreased complexity compared to EKF.
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Algorithms for Advanced Battery-Management Systems
Nalin A. Chaturvedi, Reinhardt Klein, Jake Christensen et al. · IEEE Control Systems · 2010 · 585 citations