Module-Level Modelling Approach for a Cloudbased Digital Twin Platform for Li-Ion Batteries

The pursue of the new increasingly intelligent, and heavier state estimation algorithms requires a significant amount of data and computing power, which may challenge their deployment in current BMS solutions. To address that issue, this paper proposes a cloud-based Digital Twin Platform to extend computing power and data storage capacity. This tool aims to contain the integration of models to analyse thermoelectricand ageing aspects of a LIB, based on experimental operation data by comparative analysis. Based on well-known cell-level modelling techniques, a module-level modelling approach is proposed and an experimental validation platform is suggested

Open circuit voltage and state of charge relationship functional optimization for the working state monitoring of the aerial lithium-ion battery pack.

The aerial lithium-ion battery pack works differently from the usual battery packs, the working characteristic of which is intermittent supplement charge and instantaneous large current discharge. An adaptive state of charge estimation method combined with the output voltage tracking strategy is proposed by using the reduced particle - unscented Kalman filter, which is based on the reaction mechanism and experimental characteristic analysis. The improved splice equivalent circuit model is constructed together with its state-space description, in which the operating characteristics can be obtained. The relationship function between the open circuit voltage and the state of charge is analyzed and especially optimized. The feasibility and accuracy characteristics are tested by using the aerial lithium-ion battery pack experimental samples with seven series-connected battery cells. Experimental results show that the state of charge estimation error is less than 2.00%. The proposed method achieves the state of charge estimation accurately for the aerial lithium-ion battery pack, which provides a core avenue for its high-power supply security

A novel safety anticipation estimation method for the aerial lithium-ion battery pack based on the real-time detection and filtering.

Lithium-ion battery packs have become increasingly important for power supply applications, in which the state of charge estimation and output voltage tracking should be very critical for the safety protection. A novel real-time estimation method is proposed by using the improved extended Kalman filtering algorithm together with the two-order resistance and capacitance circuit network battery model, aiming to solve its security protection issues. Experimental results show that this method can track the voltage signals effectively along with the real-time state estimation in the discharging and charging maintenance operation processes. The battery cell voltage detection accuracy is found to be 1.00mV and the pack voltage measurement error is less than 20.00mV. Meanwhile, the state of charge value can be estimated with a great accuracy of 2.00%, in which the state of balance parameter is considered for the internal connected battery cells. The developed experimental associated battery management system can be used for the working state monitoring in the aerial power supply application of the lithium-ion battery pack

Aportaciones al dimensionamiento y gestión de energía de un tren de potencia eléctrico híbrido para vehículos industriales con ciclos de conducción repetitivos y agresivos

Currently, the interest for helping mitigate the emission of greenhouse gases caused by high fuel consumption in industrial vehicles has increased. In order to the reduction of fuel consumption in an industrial vehicle, it has been proposed to incorporate into the powertrain a system capable of storing and supplying electrical energy. Consequently, the design of a hybrid electric powertrain is required, based on the interconnection of the elements (topology), the sizing of the elements and/or the energy management strategy of the powertrain. This paper presents a methodology for the design of a hybrid electric vehicle for refuse collection, which presents a repetitive and aggressive drive cycle as a result of work activity. The proposed methodology consists in modeling the behavior of a hybrid electric powertrain, considering the electrical behavior of various energy accumulation elements (batteries and supercapacitors). An embedded system is used to perform the experimental characterization of a cell and a commercial supercapacitor, in order to approximate the behavior through an electric model. In accordance with a real drive cycle of a refuse collection vehicle, the energy demand for a hybrid electric refuse collection vehicle is determined. On the other hand, the fuel consumption is calculated from a hybrid electric powertrain that integrates an energy storage system or a hybrid energy storage system. A bio-inspired metaheuristic based on a stochastic population (particle swarm optimization and genetic algorithm) is developed, in order to determine an optimal solutions space. Subsequently, the optimal sizing of an energy storage system (batteries) and a hybrid energy storage system (batteries and supercapacitors) is performed, considering different mono-objective and multi-objective optimization problems. Based on the results of each optimization problem, a comparative analysis is carried out with an element of commercial accumulation. Considering a hybrid electric powertrain that integrates a hybrid energy storage system (batteries and supercapacitors), an energy management strategy based on fuzzy logic is developed. This includes the identification of the vehicle status from a real drive cycle. Finally, the validation of the energy management strategy is carried out through the model of a hybrid electric vehicle for refuse collection.Actualmente, se ha incrementado el interés por mitigar la emisión de gases de efecto invernadero que se produce por un elevado consumo de combustible en vehículos industriales. Con la intención de contribuir en la reducción del consumo de combustible de un vehículo industrial, se ha propuesto incorporar al tren de potencia un sistema capaz de almacenar y suministrar energía eléctrica. En consecuencia, surge la necesidad de realizar el diseño de un tren de potencia eléctrico híbrido, a partir de la interconexión de los elementos (topología), el dimensionamiento de los elementos y/o la estrategia de gestión de energía del tren de potencia. En el presente trabajo se presenta una metodología para realizar el diseño de un vehículo eléctrico híbrido de recolección de basura, que presenta un ciclo de conducción repetitivo y agresivo como resultado de la actividad laboral. La metodología propuesta consiste en modelar el comportamiento de un tren de potencia eléctrico híbrido, considerando el comportamiento eléctrico de diversos elementos de acumulación de energía híbrido (baterías y supercapacitores). Se emplea un sistema embebido para realizar la caracterización experimental de una celda y un supercapacitor comercial, con el propósito de aproximar el comportamiento a través de un modelo eléctrico. En función de un ciclo de conducción real de un vehículo de recolección de basura se determina la demanda de energía para un vehículo eléctrico híbrido de recolección de basura. Por otra parte, se calcula el consumo de combustible a partir de un tren de potencia eléctrico híbrido que integra un sistema de almacenamiento de energía o un sistema de almacenamiento de energía híbrido. Se desarrolla una metaheurística bio-inspirada basada en una población estocástica) para determinar un espacio de soluciones óptimas. Posteriormente, se realiza el dimensionamiento óptimo de un sistema de almacenamiento de energía (baterías) y un sistema de almacenamiento de energía híbrido (baterías y supercapacitores), considerando diferentes problemas de optimización mono-objetivo y multi-objetivo. Con base en los resultados de cada problema de optimización, se procede a realizar un análisis comparativo con un elemento de acumulación comercial. Considerando un tren de potencia eléctrico híbrido que integra un sistema de almacenamiento de energía híbrido (baterías y supercapacitores), se desarrolla una estrategia de gestión de energía basada en lógica difusa, que incluye la identificación del estado del vehículo a partir de un ciclo de conducción real. Finalmente, se realiza la validación de la estrategia de gestión de energía a través del modelo de un vehículo eléctrico híbrido de recolección de basura.Postprint (published version

Aportaciones al dimensionamiento y gestión de energía de un tren de potencia eléctrico híbrido para vehículos industriales con ciclos de conducción repetitivos y agresivos

Currently, the interest for helping mitigate the emission of greenhouse gases caused by high fuel consumption in industrial vehicles has increased. In order to the reduction of fuel consumption in an industrial vehicle, it has been proposed to incorporate into the powertrain a system capable of storing and supplying electrical energy. Consequently, the design of a hybrid electric powertrain is required, based on the interconnection of the elements (topology), the sizing of the elements and/or the energy management strategy of the powertrain. This paper presents a methodology for the design of a hybrid electric vehicle for refuse collection, which presents a repetitive and aggressive drive cycle as a result of work activity. The proposed methodology consists in modeling the behavior of a hybrid electric powertrain, considering the electrical behavior of various energy accumulation elements (batteries and supercapacitors). An embedded system is used to perform the experimental characterization of a cell and a commercial supercapacitor, in order to approximate the behavior through an electric model. In accordance with a real drive cycle of a refuse collection vehicle, the energy demand for a hybrid electric refuse collection vehicle is determined. On the other hand, the fuel consumption is calculated from a hybrid electric powertrain that integrates an energy storage system or a hybrid energy storage system. A bio-inspired metaheuristic based on a stochastic population (particle swarm optimization and genetic algorithm) is developed, in order to determine an optimal solutions space. Subsequently, the optimal sizing of an energy storage system (batteries) and a hybrid energy storage system (batteries and supercapacitors) is performed, considering different mono-objective and multi-objective optimization problems. Based on the results of each optimization problem, a comparative analysis is carried out with an element of commercial accumulation. Considering a hybrid electric powertrain that integrates a hybrid energy storage system (batteries and supercapacitors), an energy management strategy based on fuzzy logic is developed. This includes the identification of the vehicle status from a real drive cycle. Finally, the validation of the energy management strategy is carried out through the model of a hybrid electric vehicle for refuse collection.Actualmente, se ha incrementado el interés por mitigar la emisión de gases de efecto invernadero que se produce por un elevado consumo de combustible en vehículos industriales. Con la intención de contribuir en la reducción del consumo de combustible de un vehículo industrial, se ha propuesto incorporar al tren de potencia un sistema capaz de almacenar y suministrar energía eléctrica. En consecuencia, surge la necesidad de realizar el diseño de un tren de potencia eléctrico híbrido, a partir de la interconexión de los elementos (topología), el dimensionamiento de los elementos y/o la estrategia de gestión de energía del tren de potencia. En el presente trabajo se presenta una metodología para realizar el diseño de un vehículo eléctrico híbrido de recolección de basura, que presenta un ciclo de conducción repetitivo y agresivo como resultado de la actividad laboral. La metodología propuesta consiste en modelar el comportamiento de un tren de potencia eléctrico híbrido, considerando el comportamiento eléctrico de diversos elementos de acumulación de energía híbrido (baterías y supercapacitores). Se emplea un sistema embebido para realizar la caracterización experimental de una celda y un supercapacitor comercial, con el propósito de aproximar el comportamiento a través de un modelo eléctrico. En función de un ciclo de conducción real de un vehículo de recolección de basura se determina la demanda de energía para un vehículo eléctrico híbrido de recolección de basura. Por otra parte, se calcula el consumo de combustible a partir de un tren de potencia eléctrico híbrido que integra un sistema de almacenamiento de energía o un sistema de almacenamiento de energía híbrido. Se desarrolla una metaheurística bio-inspirada basada en una población estocástica) para determinar un espacio de soluciones óptimas. Posteriormente, se realiza el dimensionamiento óptimo de un sistema de almacenamiento de energía (baterías) y un sistema de almacenamiento de energía híbrido (baterías y supercapacitores), considerando diferentes problemas de optimización mono-objetivo y multi-objetivo. Con base en los resultados de cada problema de optimización, se procede a realizar un análisis comparativo con un elemento de acumulación comercial. Considerando un tren de potencia eléctrico híbrido que integra un sistema de almacenamiento de energía híbrido (baterías y supercapacitores), se desarrolla una estrategia de gestión de energía basada en lógica difusa, que incluye la identificación del estado del vehículo a partir de un ciclo de conducción real. Finalmente, se realiza la validación de la estrategia de gestión de energía a través del modelo de un vehículo eléctrico híbrido de recolección de basura

Simplified electric vehicle powertrain modelling

Rapid development and adoption of electric vehicle technology has driven the requirement for simplified powertrain models. In this thesis, a simplified electric vehicle powertrain (SEVP) model, which calculates energy consumption for a battery electric vehicle (BEV) based on the minimum number of published vehicle parameters, is presented. The SEVP utilises published coast-down coefficients to model the tractive force and simplifies the traction motor model by using a surface-mounted permanent (SPM) motor. The SEVP is benchmarked for energy consumption estimation, with two industry-standard vehicle simulators, ADVISOR and FASTSim. The comparison is enabled by combining all three simulators in a single MATLAB model, which permits the interchange of the individual powertrain component models and establishes their impact on the cumulative energy consumption in a drive cycle. The three simulators are validated for ten BEVs using dynamometer test data from Argonne National Laboratory. Energy consumption estimation deficiencies of the SEVP are addressed by; (i) a simple cabin thermal load model, and (ii) including machine saturation and flux weakening in the SPM model. For electrical circuit simulation, the ideal battery model of the SEVP was expanded to include a Lithium-ion (Li-ion) battery pack model and the SPM motor was replaced with a more complex internal permanent magnet (IPM) design. In the Li-ion model, the output voltage is a function of the depth of discharge and a simple ageing function is included to estimate battery capacity over the lifetime of the vehicle. A comparison of the choice of internal impedance network on the dynamic performance of the battery model is conducted. The IPM motor model parameters are derived based on finite element analysis (FEA) of five traction motor designs, rated from 50 kW to 165 kW. The FEA models are validated based on test data from Oakridge National Laboratory. Finally, an energy management strategy (EMS) for a fuel cell electric vehicle (FCEV) is proposed. The EMS minimises the fuel consumption and the overall operating costs. Prerequisites for achievement of the minimum overall operating costs are minimising the battery and the fuel cell degradation