On-Line Multi-Time Scale Adaptive Parameter Identification Based on Improved Lithium-Ion Batteries Hysteresis Characteristic-Electrical Equivalent Circuit Modeling

Abstract

Accurate identification of model parameters is a key aspect of lithium battery state estimation. To accurately identify battery model parameters, this paper establishes hysteresis characteristic-electrical equivalent circuit (HC-EEC) modeling by analyzing the influence of the hysteresis effect on battery state of charge. For the high-precision identification of battery model parameters, an online multi-time scale adaptive parameter identification strategy (OM-TSAPIS) is proposed. According to the different dynamic response links in the HC-EEC model, the strategy performs parameter identification through different time scale links, and uses the adaptive step size as the starting identification condition for the multi-time scale links, thereby improving the parameter identification accuracy of the HC-EEC model. The absolute average error of OM-TSAPIS was 0.0437 and 0.298 mV under the urban dynamometer driving schedule and Beijing bus dynamic street test conditions, respectively. Simulation results show that the identification accuracy of the proposed algorithm is high.

A Review of the Technical Challenges and Solutions in Maximising the Potential Use of Second Life Batteries from Electric Vehicles

“…Mathematical models of lithium-ion batteries are utilised to estimate multiple parameters, including the battery voltage, state of charge, state of health, and temperature at various levels (cell, module, and pack) [49][50][51]. In theory, it would be ideal to develop, characterise, and validate the battery models at the cell level.…”

From Atoms to Wheels: The Role of Multi-Scale Modeling in the Future of Transportation Electrification

“…Lower order models also fall under this length scale, and can be divided into two popular categories: ROMs 10,11 (primarily reducing the partial differential equations to ordinary differential equations, differential-algebraic system of equations, and algebraic representations) and equivalent circuit models. 34 With ROMs, the fidelity of the full-order model is maintained under the desired operating conditions with lower operating costs, allowing for module and pack level simulations while maintaining the highest level of performance accuracy, however, an excursion outside of standard operating conditions may yield a significant divergence from the full-order model. Equivalent circuit models are light, and calibrated to a specific cell design (either data, or full-order model output), and provide the flexibility needed to blend accuracy and speed for many pack level simulations, but lack the ability to directly probe the impact of design changes on the performance predictions.…”