An accurate algorithm for lithium polymer battery SOC estimation is proposed based on adaptive unscented Kalman filters (AUKF) and least square support vector machines (LSSVM). A novel approach using the moving window method is applied, with AUKF and LSSVM to accurately establish the battery model with limited initial training samples. The effectiveness of the moving window modeling method is validated by both simulations and lithium polymer battery experimental results. The measurement equation of proposed AUKF method is established by the LSSVM battery model, and AUKF has the advantage of adaptively adjusting noise covariance during the estimation process. In addition, the developed LSSVM model is continuously updated online with new samples during the battery operation, in order to minimize the influence of the changes in battery internal characteristics on modeling accuracy and estimation results after a period of operation. Finally, a comparison of accuracy and performance between AUKF and UKF is made. Simulation and experiment results indicate that the proposed algorithm is capable of predicting lithium battery SOC with a limited number of initial training samples.Index Terms-Lithium polymer battery, moving window method, modeling, least square support vector machine (LSSVM), adaptive unscented Kalman filter (AUKF), state of charge (SOC).  Manuscript

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A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription.For more information, please contact eprints@nottingham.ac.uk

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A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. I. INTRODUCTIONHE more-electric aircraft (MEA) concept is one of the major trends in modern aerospace engineering aiming for reduction of the overall aircraft weight, operation cost and environmental impact. Electrical systems are employed to replace existing hydraulic, pneumatic and mechanical actuators. As a consequence, the onboard installed electrical power increases significantly and this results in challenges in the design of the aircraft electrical power systems (EPS). The tendency is to replace traditional AC distribution with highvoltage DC distribution. This can increase efficiency, reduce weight and remove the need for reactive power compensation devices [1], [2].In literature, the primary power distribution in aircrafts has been traditionally based on the single-generator-per-bus paradigm with switched distribution providing the connectivity and system integrity. Instead, the proposed "single-bus" concept uses the micro-grid approach in which all the generators and loads are connected to a single distribution bus. This single bus configuration has been widely used in other applications such as residential microgrids [3]. Such a system has the potential to considerably reduce the EPS weight since bus mass is reduced and load and generator fault isolation function can be integrated in power converters; in addition the controlled power sharing between generators has the potential to reduce generator capacity and operate at maximum efficiency levels.As the parallel operation of multiple generators is a promising solution for the MEA EPS, appropriate power sharing among the different power sources needs to be carefully considered. From the communication point of view, overall control of DC systems can be divided into three categories: distributed control, centralized control and decentralized control [4].

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