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Accurate estimation of model parameters and state of charge (SoC) is crucial for the lithium-ion battery management system (BMS). In this paper, the stability of the model parameters and SoC estimation under measurement uncertainty is evaluated by three different factors: (i) sampling periods of 1/0.5/0.1 s; (ii) current sensor precisions of ±5/±50/±500 mA; and (iii) voltage sensor precisions of ±1/±2.5/±5 mV. Firstly, the numerical model stability analysis and parametric sensitivity analysis for battery model parameters are conducted under sampling frequency of 1-50 Hz. The perturbation analysis is theoretically performed of current/voltage measurement uncertainty on model parameter variation. Secondly, the impact of three different factors on the model parameters and SoC estimation was evaluated with the federal urban driving sequence (FUDS) profile. The bias correction recursive least square (CRLS) and adaptive extended Kalman filter (AEKF) algorithm were adopted to estimate the model parameters and SoC jointly. Finally, the simulation results were compared and some insightful findings were concluded. For the given battery model and parameter estimation algorithm, the sampling period, and current/voltage sampling accuracy presented a non-negligible effect on the estimation results of model parameters. This research revealed the influence of the measurement uncertainty on the model parameter estimation, which will provide the guidelines to select a reasonable sampling period and the current/voltage sensor sampling precisions in engineering applications. OPEN ACCESSEnergies 2015, 8 7730

Abstract:In hybrid electric vehicles with power-split configurations, the engine can be decoupled from the wheel and operated with improved fuel economy, while the entire efficiency of the powertrain is affected by the circular electric power flow. Two planetary gear (2-PG) sets with adding brakes/clutches, namely a type of four shaft elelctric continuously variable transmission (ECVT) can provide multi-mode operation for the powertrain and extend the efficient area. First, a conventional 2-PG AT (Automatic Transmission) architecture is investigated. By analyzing and comparing the connection and operating modes based on the kinematic relationship and lever analogy, a feasible four-shaft ECVT architecture with two brakes and two simplified versions are picked. To make a trade-off between fuel economy and configuration complexity, an instantaneous optimal control strategy based on the equivalent consumption minimization strategy (ECMS) concept is then developed and employed as the unified optimization method in the simulations of three different configurations. Finally, the simulation results show that the simplified versions are suboptimal sets and the fuel economy is sacrificed by the limits of different modes. From the viewpoint of concept design, a multi-mode power-split configuration is more suitable for hybrid electric vehicles. This research applied a systematic methodology from concept design to energy management optimization, which can provide the guidelines for researchers to select a suitable multi-mode power-split hybrid powertrain.

A multi-mode power-split (MMPS) hybrid electric vehicle (HEV) has two planetary gearsets and clutches/grounds which results in several operation modes with enhanced electric drive capability and better fuel economy. Basically, the battery storage system is involved in different operation modes to satisfy the power demand and minimize the fuel consumption, whereas the complicated operation modes with frequent charging/discharging will absolutely influence the battery life because of degradation. In this paper, firstly, we introduce the solid electrolyte interface (SEI) film growth model based on the previous study of the battery degradation principles and was verified according to the test data. We consider both the fuel economy and battery degradation as a multi-objective problem for MMPS HEV by normalization with a weighting factor. An instantaneous optimization is implemented based on the equivalent fuel consumption concept. Then the control strategy is implemented on a simulation framework integrating the MMPS powertrain model and the SEI film growth map model over some typical driving cycles, such as New European Driving Cycle (NEDC) and Urban Dynamometer Driving Schedule (UDDS). Finally, the result demonstrates that these two objectives are conflicting and the trade-off reduces the battery degradation with fuel sacrifice. Additionally, the analysis reveals how the mode selection will reflect the battery degradation.

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