Battery model plays an important role in the simulation of electric vehicles (EVs) and states estimation of the batteries in the development of the model-based battery management system. To build a battery model with enough precision and suitable complexity, firstly this paper summarizes the seven representative battery models, which belong to the simplified electrochemical models or the equivalent circuit models. Then the model equations are built and the. Battery model plays an important role in the simulation of electric vehicles (EVs) and states estimation of the batteries in the development of the model-based battery management system. To build a battery model with enough precision and suitable complexity, firstly this paper summarizes the seven representative battery models, which belong to the simplified electrochemical models or the equivalent circuit models. Then the model equations are built and the model parameters are identified with an online parameter identification method. The battery test bench is built and the experiment schedule is designed. Finally an evaluation is performed on the seven battery models by an experiment approach from the aspects of the estimation accuracy of the terminal voltages. To evaluate the effect of the number of RC networks on the model's precision, the battery general equivalent circuit models (GECMs) with different RC networks are also discussed further. The results indicate the equivalent circuit model with two RC networks, the DP model, has an optimal performance.••Battery modelsLithium-ion batteryElectric vehiclesExperimentEnergy is the basis of the human survival and development, it's urgent to develop green energy and use the nonrenewable energy rationally. Since transportation consumes a large part of energy, to develop and apply the electric vehicles (EVs) is necessary in the way of green mobility,,,. Power battery is the key component of EVs, which include battery electric vehicles (BEVs), hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs). To ensure the power battery work safely and reliably, which is functioned by the battery management system (BMS),,, the temperature, voltage, current of the batteries should be monitored and the states of the batteries should be estimated precisely in real time. However, it is hard to measure the states of batteries, like state of charge (SoC), state of health (SoH), state of function (SoF) directly for the complicated electrochemical process and various influence factors from the practice application, the estimation method based on battery models is used broadly and the battery model plays an important role,,,,,,,.Many battery models, which are lumped models with relatively few parameters, have been put forward especially for the purpose of vehicle power management control and battery management system development. The most commonly used models can be summarized as two kinds: the electrochemical models an. 2.1. The battery modelsThe equivalent circuits of the Rint model, the Thevenin model and the DP model are shown in Fig. 1. The equations and features of the seven representative battery models mentioned before are summarized and listed in Table 1.2.2. Model parameters' identificationThe models' parameters identified by the traditional offline identification method maybe exit errors inevitably which caused by internal and external factors such as battery operating environment and aging, thus the accuracy will decrease. To solve these problems, we choose an online parameter identification method instead based on the recursive least squares (RLSs) method with an optimal forgetting factor.3.1. Battery test benchThe test bench is shown in Fig. 2, which consists of a Digatron battery test system BNT 400-050, a thermal chamber for environment control, a host computer and BTS-600 interface for programming the BNT 400-050. The host computer is used for the real-time calculation of the model parameters. The BNT 400-050 can charge/discharge a battery according to the designed program with maximum voltage of 50 V and maximum charge/discharge current of 400 A, and its recorded data include current, voltage, temperature, accumulative amp–hours (A h) and watt–hours (W h), etc. The measured data is transmitted to the host computer through TCP/IP driven by BNT 400-050. The host computer has a low-pass filtering function to implement large noise cancellation. Furthermore, in order to improve the sampling precision of cell voltage, the Fluke 8846A multimeter, whose measurement accuracy of DC voltage is up to 0.0024% with a 6.5 digit resolution, has been applied for cell voltage measurement. A LiFePO4 cell with nominal voltage of 3.2 V and nominal capacity of 10 A h is selected as the test object.3.2. Battery test scheduleThe.
