A novel non-isolated active charge balancing architecture for lithium-ion batteries

Active charge balancing is an approved technique to implement high performance lithium-ion battery systems. Enhanced balancing speeds and reduced balancing losses are feasible compared to passive balancing. The new architecture proposed in this paper overcomes several drawbacks of other active balancing methods. It consists of only 2 non-isolated DC/DC converters. In combination with a MOSFET switch matrix it is able to balance arbitrary cells of a battery system at high currents. Adjacent cells can be balanced simultaneously. For the given setting, numerical simulations show an overall balancing efficiency of approx. 92.5%, compared to 89.4% for a stack-to-cell-to-stack method (St2C2St, bidirectional fly-back) at similar balancing times. The usable capacity increases from 97.1% in a passively balanced system to 99.5% for the new method

Analysis of an active charge balancing method based on a single non-isolated DC/DC converter

​© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.For lithium-ion batteries, active balancing can bring advantages compared to passive balancing in terms of lifetime and available capacity. Most known balancing techniques suffer from a low efficiency or high complexity and cost. This paper proposes a new technical solution based on a non-isolated DC/DC converter and a low-speed switching matrix to overcome efficiency and power limitations of present balancing methods. The proposed circuit allows balancing current paths which are not possible with previous methods and results in balancing efficiencies of over 90%. The performance comparison is based on batch numerical simulations using calculated and measured efficiency values. The simulation results are compared to the approved active balancing method cell-to-cell and to passive balancing. A study case with eight randomly distributed battery cells shows an improvement in overall balancing efficiency of up to 47.4% and a reduction in balancing time of up to 36.9%. The available capacity increases from 97.2% in a passively balanced system to 99.7% for the new method. A hardware prototype was set up to demonstrate the working principle and to verify the numerical simulations

Techniques neuromimétiques pour la commande dans les systèmes électriques : application au filtrage actif parallèle dans les réseaux électriques basse tension

Le travail présenté dans ce mémoire concerne l'élaboration d'une stratégie complète d'identification et de commande neuronale d'un filtre actif parallèle (FAP). L'objectif visé est l'amélioration des performances par rapport aux systèmes classiques de dépollution des installations électriques basse tension. Basée sur l'utilisation des techniques neuromimétiques, notre approche de compensation des harmoniques se fait en trois étapes. Les deux premières étapes identifient respectivement les composantes de la tension et les courants harmoniques à l'aide de réseaux de neurones du type Adaline. La troisième étape injecte les courants harmoniques dans le réseau électrique par un module de commande à base de réseaux de neurones multicouches. Plusieurs architectures neuronales ont été développées et comparées pour chacune des étapes. La structure proposée s'adapte automatiquement aux variations de la charge du réseau et donc aux fluctuations du contenu harmonique des perturbations. Elle permet également la compensation sélective des harmoniques et la correction du facteur de charge. Finalement, ces stratégies ont été validées sur un banc expérimental et leur aptitude à l'intégration matérielle a été testée en simulation

Microgrid Cyber-Security: Review and Challenges toward Resilience

The importance of looking into microgrid security is getting more crucial due to the cyber vulnerabilities introduced by digitalization and the increasing dependency on information and communication technology (ICT) systems. Especially with a current academic unanimity on the incremental significance of the microgrid’s role in building the future smart grid, this article addresses the existing approaches attending to cyber-physical security in power systems from a microgrid-oriented perspective. First, we start with a brief descriptive review of the most commonly used terms in the latest relevant literature, followed by a comprehensive presentation of the recent efforts explored in a manner that helps the reader to choose the appropriate future research direction among several fields

A Self-Learning Solution for Torque Ripple Reduction for Non-Sinusoidal Permanent Magnet Motor Drives Based on Artificial Neural Networks

This paper presents an original method, based on artificial neural networks, to reduce the torque ripple in a permanent-magnet non-sinusoidal synchronous motor. Solutions for calculating optimal currents are deduced from geometrical considerations and without a calculation step which is generally based on the Lagrange optimization. These optimal currents are obtained from two hyperplanes. The study takes into account the presence of harmonics in the back-EMF and the cogging torque. New control schemes are thus proposed to derive the optimal stator currents giving exactly the desired electromagnetic torque (or speed) and minimizing the ohmic losses. Either the torque or the speed control scheme, both integrate two neural blocks, one dedicated for optimal currents calculation and the other to ensure the generation of these currents via a voltage source inverter. Simulation and experimental results from a laboratory prototype are shown to confirm the validity of the proposed neural approach.CPER Région Alsace 2007-201

Adaptive Online State of Charge Estimation of EVs Lithium-Ion Batteries with Deep Recurrent Neural Networks

The State of Charge (SOC) estimation is a significant issue for safe performance and the lifespan of Lithium-ion (Li-ion) batteries. In this paper, a Robust Adaptive Online Long Short-Term Memory (RoLSTM) method is proposed to extract SOC estimation for Li-ion Batteries in Electric Vehicles (EVs). This real-time, as its name suggests, method is based on a Recurrent Neural Network (RNN) containing Long Short-Term Memory (LSTM) units and using the Robust and Adaptive online gradient learning method (RoAdam) for optimization. In the proposed architecture, one sequential model is defined for each of the three inputs: voltage, current, and temperature of the battery. Therefore, the three networks work in parallel. With this approach, the number of LSTM units are reduced. Using this suggested method, one is not dependent on precise battery models and can avoid complicated mathematical methods. In addition, unlike the traditional recursive neural network where content is re-written at any time, the LSTM network can decide on preserving the current memory through the proposed gateways. In that case, it can easily transfer this information over long paths to receive and maintain long-term dependencies. Using real databases, the experiment results illustrate the better performance of RoLSTM applied to SOC estimation of Li-Ion batteries in comparison with a neural network modeling and unscented Kalman filter method that have been used thus far