Abstract

Resumen: En el presente documento se describe el desarrollo, la implementación y los resultados obtenidos al aplicar una estrategia de estimación en línea del estado de carga de baterías de litio. La estrategia de estimación presentada involucra técnicas algebraicas para realizar la estimación de la tensión de circuito abierto, y se apoya en el clásico método de Newton para estimar el estado de carga. En este trabajo se desarrolla un modelo matemático para predecir el comportamiento de la batería. También se diseña y construye una plataforma de pruebas en donde se validan las estrategias desarrolladas. Adicionalmente, se presentan los resultados obtenidos y se realiza una comparación con otras técnicas de estimación del estado de carga. Abstract. This document describes the development, testing and results of applying an state of charge online estimation strategy for lithium batteries. The estimation strategy involves algebraic techniques in order to estimate the open circuit voltage, and make use of the classic Newton’s method to estimate the state of charge. In this work, a mathematical battery model are developed. Moreover, the design and construction of a testbench to validate the strategies are presented. In addition, the test results and the comparison between other state of charge estimation strategies are shown.

Abstract

This paper deals with a green energy highway in the Netherlands. Here, the development of electric mobility and self- driving cars is introduced. The ideas of wireless power integration with green energy technologies - solar and wind is considered. In case of wind energy, conventional turbines and bladeless vortex are considered as options. Solaroads along the emergency lanes are also investigated. A Dutch highway A12 is considered as a case study and sizing of these energy sources for electric mobility is considered. A grid power demand profile is considered and number of EVs that can be charged hourly is calculated. A preliminary investigation of the combination of IPT and Self-Healing roads is considered in this study.

Abstract

Electric vehicle battery performance near end-of-life is limited by mismatched cell degradation, leading to an estimated 5-10% cell capacity variation across the pack. Active cell balancing hardware architectures incorporating a Low-Voltage (LV) bus supply have been introduced to unlock lost capacity due to cell imbalance at reduced cost, through elimination of the vehicle's 400-to-12V dc-dc converter. In this work, a hierarchical model-predictive control scheme is applied to a time-shared isolated converter active balancing architecture that incorporates LV bus supply. The proposed controller efficiently divides computation among the battery management system hardware components. The energy-buffering capability of the lead-acid battery, which is connected to the LV bus, is used to trade-off balancing and bus regulation objectives, reducing peak power and improving the system cost-effectiveness. Simultaneous state-of-charge balancing and LV bus regulation is verified in simulation and experiment using real-world drive and LV load data collected from a GM Bolt electric vehicle. Similar controller performance compared to a central scheme is achieved in simulation. The experimental setup includes a custom 12S2P, 3.9kWh, liquid-cooled Lithium Nickel Manganese Cobalt battery module with embedded battery management system. The controller performance is evaluated with an initial maximum state-of-charge imbalance of 6.8%.

Abstract

This paper proposes a method for the combined estimation of the state of charge (SOC) and state of health (SOH) of batteries in hybrid and full electric vehicles. The technique is based on a set of five artificial neural networks that are used to tackle a regression and a classification task. In the method, the estimation of the SOC relies on the identification of the ageing of the battery and the estimation of the SOH depends on the behavior of the SOC in a recursive closed-loop. The networks are designed by means of training datasets collected during the experimental characterizations conducted in a laboratory environment. The lithium battery pack adopted during the study is designed to supply and store energy in a mild hybrid electric vehicle. The validation of the estimation method is performed by using real driving profiles acquired on-board of a vehicle. The obtained accuracy of the combined SOC and SOH estimator is around 97%, in line with the industrial requirements in the automotive sector. The promising results in terms of accuracy encourage to deepen the experimental validation with a deployment on a vehicle battery management system. Document type: Article