Wladislaw Waag

Adaptive On-line State-of-available-power Prediction of Lithium-ion Batteries

This paper presents a new overall system for state-of-available-power (SoAP) prediction for a lithium-ion battery pack. The essential part of this method is based on an adaptive network architecture which utilizes both fuzzy model (FIS) and artificial neural network (ANN) into the framework of adaptive neuro-fuzzy inference system (ANFIS). While battery aging proceeds, the system is capable of delivering accurate power prediction not only for room temperature, but also at lower temperatures at which power prediction is most challenging. Due to design property of ANN, the network parameters are adapted on-line to the current battery states (state-of-charge (SoC), state-of-health (SoH), temperature). SoC is required as an input parameter to SoAP module and high accuracy is crucial for a reliable on-line adaptation. Therefore, a reasonable way to determine the battery state variables is proposed applying a combination of several partly different algorithms. Among other SoC boundary estimation methods, robust extended Kalman filter (REKF) for recalibration of amp hour counters was implemented. ANFIS then achieves the SoAP estimation by means of time forward voltage prognosis (TFVP) before a power pulse occurs. The trade-off between computational cost of batch-learning and accuracy during on-line adaptation was optimized resulting in a real-time system with TFVP absolute error less than 1%. The verification was performed on a software-in-the-loop test bench setup using a 53 Ah lithium-ion cell.

Specialized battery emulator for automotive electrical systems

Complexity of the automotive electrical power system is continuously increasing. Greater amount of higher loads is added and active energy management is being introduced. The whole design of such power systems depends on the batteries being used. Since safety-related systems also need power to function, special care has to be taken considering voltage quality. However, there are a lot of parameters that can be modified, and all configurations need to be tested. Since testing is time consuming, the right tools must be used during different phases of the design process. Specialized battery emulator is a combination of a power electronics device with an advanced battery model. It is used for testing and verification of the vehicle electrical system at various battery behavior or ambient conditions. Design requirements for the hardware were identified by observing occurring current and voltage ranges as well as considering special needs in the automotive environment.

Self-learning state-of-available-power prediction for lithium-ion batteries in electrical vehicles

This paper describes an overall system for state-of-available-power (SoAP) prediction for a lithium-ion battery pack. The essential part of this method is based on an adaptive network architecture which utilizes both fuzzy model (FIS) and artificial neural network (ANN) into the framework of adaptive neuro-fuzzy inference system (ANFIS). While battery aging proceeds, the system is capable of delivering accurate power prediction for the typical temperature range. Due to design property of ANN, the network parameters are adapted on-line to the current states (state of charge (SoC), state of health (SoH), temperature). SoC is required as an input parameter to SoAP module and high accuracy is crucial for a reliable self-learning capability. Therefore, a reasonable way to determine the battery state variables is proposed applying a combination of several partly different algorithms. Among others SoC boundary estimation methods, robust extended Kalman filter (REKF) for recalibration of amp hour counters was implemented. ANFIS then achieves the SoAP estimation by means of time forward voltage prognosis (TFVP) before a power pulse. The tradeoff between computational cost of batch-learning and accuracy during on-line adaptation was optimized resulting in a real-time system. The verification was performed on a software-in-the-loop test bench setup using a 53 Ah lithium-ion cell.