16 pubs · 474 cites · h-index 8
In this paper, an effective method based on adaptive extended Kalman filter (AEKF) is proposed for detection of random FDIs against battery state-of-charge algorithms on cloud battery management platforms. First, the battery model is established and used with the AEKF to predict the battery response. Second, a residual signal (RS) is defined as the difference between the AEKF-based estimated battery voltage and the received voltage measurement. The FDIs are then detected based on a hybrid detection criterion mixing the Chi-squared test and Euclidean detector. The proposed mixed strategy improves the detection accuracy in terms of false negatives and false positives caused by noises and changes in battery operation. Regarding the latter point, the AEKF is equipped with a dedicated recursive least squares filter to accommodate real-time model changes. The proposed algorithm is developed and verified based on actual battery data related to high-capacity lithium-ion cells. The method is exposed to different case studies considering normal and attack conditions and a remarkable detection accuracy of about 98% is attained with no false positive in the presence of current and voltage noises up to ±10 mA and ± 3 mV.
Transportation electrification has been fueled by recent advancements in the technology and manufacturing of battery systems, but the industry yet is facing serious challenges that could be addressed using cutting-edge digital technologies. One such novel technology is based on the digital twining of battery systems. Digital twins (DTs) of batteries utilize advanced multi-layer models, artificial intelligence, advanced sensing units, Internet-of-Things technologies, and cloud computing techniques to provide a virtual live representation of the real battery system (the physical twin) to improve the performance, safety, and cost-effectiveness. Furthermore, they orchestrate the operation of the entire battery value chain offering great advantages, such as improving the economy of manufacturing, re-purposing, and recycling processes. In this context, various studies have been carried out discussing the DT applications and use cases from cloud-enabled battery management systems to the digitalization of battery testing. This work provides a comprehensive review of different possible use cases, key enabling technologies, and requirements for battery DTs. The review inclusively discusses the use cases, development/integration platforms, as well as hardware and software requirements for implementation of the battery DTs, including electrical topics related to the modeling and algorithmic approaches, software architectures, and digital platforms for DT development and integration. The existing challenges are identified and circumstances that will create enough value to justify these challenges, such as the added costs, are discussed.
Design of the Electric Vehicle (EV) battery pack involves different requirements related to the driving range, acceleration, fast-charging, lifetime, weight, volume, etc. Therefore, sizing of the EV battery pack necessitates a multi-objective optimization study to achieve the right trade-off considering the aforementioned factors. This “trade-off” can vary depending on the type and size of the EV, as well as use cases. In this regard, a nice solution is to use a hybridized battery pack consisting of both High-Energy (HE) and High-Power (HP) battery cells, which will help to meet a wider range of customer requirements. Hybridization decouples energy and power and thus increases design flexibility to achieve a better trade-off for a wider range of EV applications. This paper proposes an effective framework for optimal sizing of such hybridized battery packs for a typical EV, namely the Mitsubishi MiEV. Lithium-ion cells with Nickel Manganese Cobalt Oxide (NMC) and Lithium Titanate Oxide (LTO) chemistry are chosen as HE and HP cells, respectively. A detailed analysis is fulfilled to determine the best hybridization topology, e.g. voltage profile, interface with DC-link, etc. The genetic algorithm is used to solve the multi-objective optimization problem considering two different driving cycles, namely the New European Driving Cycle (NEDC) and Worldwide Harmonised Light Vehicle Test Procedure (WLTP). The results favor the usefulness of the hybrid battery pack to simultaneously achieve lifetime and charge power requirements compared to mono battery systems. The hybrid pack offers >+40,000 km improvement in the achievable driving when an end-of-life criterion of 70 % for the cell capacity is considered. Remarkably, the optimized hybrid pack can also be fast charged up to 70 % within 6 min.
In this paper we address the windup problem arising from actuator magnitude and rate saturation for the linearized longitudinal short-period dynamics of a tailless aircraft model around any trim flight condition. The proposed anti-windup scheme allows for more aggressive manoeuvres than the standard “command limiting” approach. Moreover, the compensation law guarantees stability of the controlled aircraft for any pilot command and enforces flight quality specifications whenever they are achievable within the given control constraints. “Pilot-in-the-loop” simulations confirm the effectiveness of the control strategy.
For the well known ball-and-beam system, we design a new saturation control law, which employs state-dependent saturation levels and guarantees global asymptotic stability, not achieved with previous designs.