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Due to the strong nonlinearity and complex time-variant property of batteries, the existing state of charge (SOC) estimation approaches based on a single equivalent circuit model (ECM) cannot provide the accurate SOC for the entire discharging period. This paper aims to present a novel SOC estimation approach based on a multiple ECMs fusion method for improving the practical application performance. In the proposed approach, three battery ECMs, namely the Thevenin model, the double polarization model and the 3 rd order RC model, are selected to describe the dynamic voltage of lithium-ion batteries and the genetic algorithm is then used to determine the model parameters. The linear matrix inequality-based H-infinity technique is employed to estimate the SOC from the three models and the Bayes theorem-based probability method is employed to determine the optimal weights for synthesizing the SOCs estimated from the three models. Two types of lithium-ion batteries are used to verify the feasibility and robustness of the proposed approach. The results indicate that the proposed approach can improve the accuracy and reliability of the SOC estimation against uncertain battery materials and inaccurate initial states.

Electrode material aging leads to a decrease in capacity and/or a rise in resistance of the whole cell and thus can dramatically affect the performance of lithium-ion batteries. Furthermore, the aging phenomena are extremely complicated to describe due to the coupling of various factors. In this review, we give an interpretation of capacity/power fading of electrode-oriented aging mechanisms under cycling and various storage conditions for metallic oxide-based cathodes and carbon-based anodes. For the cathode of lithium-ion batteries, the mechanical stress and strain resulting from the lithium ions insertion and extraction predominantly lead to structural disordering. Another important aging mechanism is the metal dissolution from the cathode and the subsequent deposition on the anode. For the anode, the main aging mechanisms are the loss of recyclable lithium ions caused by the formation and increasing growth of a solid electrolyte interphase (SEI) and the mechanical fatigue caused by the diffusion-induced stress on the carbon anode particles. Additionally, electrode aging largely depends on the electrochemical behaviour under cycling and storage conditions and results from both structural/morphological changes and side reactions aggravated by decomposition products and protic impurities in the electrolyte.

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Abstract:The wheel driving torque on four-wheel-drive electric vehicles (4WDEVs) can be modulated precisely and continuously, therefore maneuverability and energy-saving control can be carried out at the same time. In this paper, a wheel torque distribution strategy is developed based on multi-objective optimization to improve vehicle maneuverability and reduce energy consumption. In the high-layer of the presented method, sliding mode control is used to calculate the desired yaw moment due to the model inaccuracy and parameter error. In the low-layer, mathematical programming with the penalty function consisting of the yaw moment control offset, the drive system energy loss and the slip ratio constraint is used for wheel torque control allocation. The programming is solved with the combination of off-line and on-line optimization to reduce the calculation cost, and the optimization results are sent to motor controllers as torque commands. Co-simulation based on MATLAB ® and Carsim ® proves that the developed strategy can both improve the vehicle maneuverability and reduce energy consumption.

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