Accurate state estimation is essential for the safe and reliable operation of lithium-ion batteries. However, the accuracy of the battery state estimation depends on the accuracy of the battery parameters. Because the state of charge (SOC) cannot be directly measured, estimation methods based on the Kalman filter are widely used. However, it is difficult to estimate SOC online and get high accuracy results. This article proposes a method for parameter identification and SOC estimation for lithium-ion batteries. Because the lithium-ion battery has slow-varying parameters (such as internal resistance, and polarization resistance), and the SOC has fast-varying characteristics, so a multi-scale multi-innovation unscented Kalman filter and extended Kalman filter (MIUKF-EKF) are used to perform online measurement of battery parameters and SOC estimation in this method. The battery parameters are estimated with a macro-scale, and the SOC is estimated with a micro-scale. This method can improve the estimation accuracy of the SOC in real-time. Results of experiments indicate that the algorithm has higher accuracy in online parameter identification and SOC estimation than in the dual extended Kalman filter (DEKF) algorithm.
A low-power Colpitts VCO with high output efficiency that uses base inductive feedback and Q-factor enhancement techniques is proposed in this paper. The base inductive feedback technique employs an inductor at the base of the bipolar transistor to generate a feedback enhancement effect and reduce the start-up current. The Q-factor enhancement technique adopts a capacitor voltage divider at the output of the Colpitts VCO to improve the DC-to-RF efficiency. The mechanisms are theoretically analyzed, and then a 433 MHz Colpitts VCO is designed to verify the scheme. Its DC consumption is as low as 270 µW while the operating frequency is 433 MHz. Finally, the Colpitts VCO is applied in a wireless neural signal recording system and works well. Thus, the presented VCO is suitable for analog signal acquisition system in the extremely power-constrained wireless scenarios. key words: Colpitts VCO, Inductive feedback, Q-factor enhancement, DCto-RF efficiency, Low power Classification: Devices, circuits and hardware for IoT and biomedical applications
To deal with the uncertainties of wind power and load residing in the power supply reliability model, an interval reliability evaluation method is proposed by combining the wind power generation and energy storage system (ESS). Firstly, the interval power supply reliability evaluation model, which belongs to an interval mixed integer program (IMIP), is established based on the interval variables. Secondly, the IMIP model is transformed into the deterministic optimization model under two extreme circumstances by utilizing the possibility degree theory of interval numbers. The maximum power supply probability, considering the wind power interval to meet the load demand interval, is sought by optimizing outputs of the ESS and generators, i.e., the upper boundary of the load shedding is the smallest. Finally, the states of wind turbines and generators are generated based on sequential Monte Carlo simulation, and the reliability of the hybrid energy generation system is evaluated by calculating the loss of load expectation, expected energy not supplied, and maximum power supply probability, which provides a basis for establishing interval optimal allocation model of energy storage. IEEE RTS-24 test system is utilized to verify the performance of the proposed method, and the model is solved by the CPLEX 12.7 solver. The simulation results demonstrate the effectiveness and applicability of the proposed method.
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