The design of a high-frequency switching mode charger (HFSMC) was optimized using a PSPICE simulation of the charging system including a valve regulated lead-acid (VRLA) battery pack and the HFSMC. A high-frequency battery charging circuit model was developed to describe the battery dynamics when charging with DC current having high-frequency ripples. An IGBT circuit model, a high-frequency pulsed transformer coupling model and a silicon fast recovery diode model were also developed. Simulations were compared to laboratory measurements to verify the battery and system models. Simulation of the working states of an Fe-based nanocrystalline magnetic core used in the transformer shows that the transformer design can be optimized by adjusting the core gap and by employing an RC network. Further simulations show that the components in the output unit of the charger main circuit can also be optimized. Simulations also show that the battery dynamics including the inductance should be considered for design optimization of the HFSMC.

Since most battery chargers are now switched at high frequencies, new battery models are needed to account for the different battery characteristics during high frequency charging. This paper presents a high frequency transient model for a traction battery during charging to study the battery transient dynamics at high frequencies and its impact on the working state of the charger. An inductance was added to the model in series to describe the inductance characteristic at high frequencies, which differs from most current battery models which emphasize describing the capacitance characteristic of the battery. The model was verified by comparing simulations with laboratory measurements. The system simulations describe the impact on the output characteristics of the high frequency switching mode charger and provide a reliable method for design optimization.

Mobile robot often relies on a battery system as its power supply and such kind of mobile robot is called cableless mobile robot. In the past years, while there are many researches on automation and control techniques, mechanical and sensor designs of mobile robot, very little systematic and comprehensive work has been done in the design of rechargeable battery power system for cableless mobile robot. This paper discusses the above problem from three different aspects: selection of rechargeable battery type, battery management technique and energy renewal. In the section on selection of rechargeable battery, many different types of traction rechargeable battery are introduced and the factors that are crucial for cableless mobile robot applications are compared. The section on battery management technique focuses on battery state-of-charge (SOC) estimation. In the section on energy renewal, two typical charging techniques, conductive charging and inductive charging are discussed. In the final section, one implementation example, high power Ni-MH rechargeable battery system for TH-1 humanoid robot which is being developed by Tsinghua University, is introduced.

A battery is a quite complex and nonlinear system comprising interacting physical and chemical processes although it seems deceptively simple. State-of-charge (SOC), a parameter to describe how much energy battery has, is a key factor in battery management and its estimation is an important and challenging task. We develop an adaptive neuro-fuzzy inference system (ANFIS) to achieve the goal. First in this paper, nonconventional input variables of the ANFIS are selected by three different correlation analysis techniques, linear correlation analysis (LCA), nonparametric correlation analysis (NCA) and partial correlation analysis (PCA). Next, the ANFIS model of five inputs and one output is presented. Takagi and Sugeno's fuzzy if-then rules are used. Then, number determination of training data pairs is discussed. Finally, hybrid learning algorithm combining the gradient method and the least squares estimate (LSE) is adopted to train the ANFIS. Predicted results obtained by the ANFIS are compared with measured results, verifying presented ANFIS. For contrast, a three-layer feedforward back-propagation (BP) artificial neural network (ANN) is presented to estimate SOC. Compared with the BP ANN model, the ANFIS obtains better prediction performance when interpolating. Comparisons of the two approaches have highlighted the potential of ANFIS in modeling and prediction of the behavior of complex nonlinear dynamic systems.

The selection of input variables is important to improve the prediction accuracy of artificial neural networks (ANNs). A three-layer feedforward backpropagation ANN is presented to estimate and predict the battery state-of-charge with nonconventional input variables selected. Initially, a few candidate input variables are derived from three basic input variables: discharging current, discharging time and battery terminal voltage. Then, three techniques of correlation analysis - the linear correlation analysis, nonparametric correlation analysis and partial correlation analysis - are used to select the input variables, and the results obtained are compared. With several nonconventional input variables included in the input sets, high prediction accuracy of the ANN model is obtained.

This paper presents a three-layer feedforward back-propagation (BP) artificial neural network (ANN), whose output is battery state-of-charge (SOC), to estimate and predict SOC of high power Ni-MH rechargeable battery. Five ANN inputs are novelly selected to improve the accuracy of ANN prediction by the proposed method of correlation coefficient ranking based on correlation analysis of different variables and SOC, and they are: batter), discharging current i, accumulated ampere hours Ah, battery terminal voltage v, time-average terminal voltage tav and twice time-average voltage ttav (i.e. time-average of tav). Meanwhile, six training. sets are equally selected from thirteen data sets about constant current discharging (CCD) from 100% to 0% SOC and Levenberg-Marquardt training algorithm is selected. Comparisons between simulation and measurement verify the proposed ANN model. Especially, the ANN can satisfyingly estimate SOC of battery (pack) whose starting SOC (i.e. SOC0) is not originally known after about ten minutes (short time compared with the whole discharging process) constant load discharging (CLD), and most of absolute values of absolute errors are not more than 5%.

Battery is a quite complex and nonlinear system comprised of interacting physical and chemical processes, although it may seem deceptively simple. For this reason, existing battery models are either partially successful or too complicated and inconvenient in modeling and simulation applications. This paper presents a dynamic neural network model with time-delayed system output feedback for modelling and identification of a Ni-MH battery during discharging. Comparisons between the simulation and measurement verify the presented model. Compared with the methods based on the Peukert equation, which is often used for the calculation of the available capacity and simulation of battery discharging curves, the ANN method is more accurate.

This paper initially presents a battery-charging model to describe transient dynamics including inductance characteristic of valve regulated lead acid (VRLA) battery during charging at DC current of high frequency ripple. Next, some formulas are introduced to determine the values of key elements of battery model, which are functions of battery state of charge (SOC). A very simple formula is derived to calculate the value of battery inductance L in the model. Then, the battery-charging model is verified via comparisons between simulation and laboratory measurement of charging transient process. Further simulation study shows that battery inductance characteristic has impact on the working state of high frequency switching mode charger (HFSMC) and parameter selection of the components in output unit of charger power transfer main circuit can be optimized.