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Battery remaining charging time (RCT) prediction can facilitate charging management and alleviate mileage anxiety for electric vehicles (EVs). Also, it is of great significance to improve EV users' experience. However, the RCT for a lithium-ion battery pack in EVs changes with temperature and other battery parameters. This study proposes an electrothermal model-based method to accurately predict battery RCT. Firstly, a characteristic battery cell is adopted to represent the battery pack, thus an equivalent circuit model (ECM) of the characteristic battery cell is established to describe the electrical behaviors of a battery pack. Secondly, an equivalent thermal model (ETM) of the battery pack is developed by considering the influence of ambient temperature, thermal management, and battery connectors in the battery pack to calculate the temperature which is then fed back to the ECM to realize electrothermal coupling. Finally, the RCT prediction method is proposed based on the electrothermal model and validated in the wide temperature range from − 20 ℃ to 45 ℃. The experimental results show that the prediction error of the RCT in the whole temperature range is less than 1.5%. [ABSTRACT FROM AUTHOR]

Accurate modelling of lithium ion batteries is crucial for battery management in electric vehicles. Recent studies have revealed the fractional order nature of lithium ion batteries, leading to fractional order modelling techniques. In this paper, a comprehensive review of the fractional order battery models and their applications in battery management of electric vehicles is provided from the perspectives of frequency and time domains. In the frequency domain, the fractional order models to fit electrochemical impedance spectroscopy data are investigated, followed by their applications in health diagnosis, battery heating and charging strategies In the time domain, the fractional order models adopted for voltage simulation are discussed, followed by their applications in battery state estimation and fault diagnosis. Finally, from the perspectives of time domain and frequency domain applications, critical challenges and research trends for future work in terms of fractional order modelling are highlighted to advance the development of next-generation battery management. [ABSTRACT FROM AUTHOR]

State of charge (SOC) estimation for lithium ion batteries plays a critical role in battery management systems for electric vehicles. Battery fractional order models (FOMs) which come from frequency-domain modelling have provided a distinct insight into SOC estimation. In this article, we compare five state-of-the-art FOMs in terms of SOC estimation. To this end, firstly, characterisation tests on lithium ion batteries are conducted, and the experimental results are used to identify FOM parameters. Parameter identification results show that increasing the complexity of FOMs cannot always improve accuracy. The model R(RQ)W shows superior identification accuracy than the other four FOMs. Secondly, the SOC estimation based on a fractional order unscented Kalman filter is conducted to compare model accuracy and computational burden under different profiles, memory lengths, ambient temperatures, cells and voltage/current drifts. The evaluation results reveal that the SOC estimation accuracy does not necessarily positively correlate to the complexity of FOMs. Although more complex models can have better robustness against temperature variation, R(RQ), the simplest FOM, can overall provide satisfactory accuracy. Validation results on different cells demonstrate the generalisation ability of FOMs, and R(RQ) outperforms other models. Moreover, R(RQ) shows better robustness against truncation error and can maintain high accuracy even under the occurrence of current or voltage sensor drift. [ABSTRACT FROM AUTHOR]

The simulation plays an important role in understanding of electrochemical behavior and internal process of lithium ion batteries. The existing finite difference method (FDM) to conduct the simulation of electrochemical process is time-consuming and computationally expensive. In this paper, a novel numerical method is proposed to accelerate the solution of the electrochemical model for a lithium ion battery. It is implemented in three steps. In the first step, physical analogy of electrochemical process to an electric circuit is used to solve charge conservation equations. In the second and third step, control volume method is used to solve species conservation equations. The simulation results show that the proposed method is much faster than the FDM by 2.2 times while maintain high accuracy which is verified by simulation and experimental data as well. [ABSTRACT FROM AUTHOR]

With its advantages in wirelength reduction and routing flexibility compared with conventional Manhattan routing, X architecture has been proposed and applied to modern IC design. As a critical part in high-performance integrated circuits, clock network design meets great challenges due to feature size decrease and clock frequency increase. In order to eliminate the delay and attenuation of clock signal introduced by the vias, and to make it more tolerant to process variations, in this paper, we propose an algorithm of a single layer zero skew clock routing in X architecture (called Planar-CRX). Our Planar-CRX method integrates the extended deferred-merge embedding algorithm (DME-X, which extends the DME algorithm to X architecture) with modified Ohtsuki’s line-search algorithm to minimize the total wirelength and the bends. Compared with planar clock routing in the Manhattan plane, our method achieves a reduction of 6.81% in total wirelength on average and gets the resultant clock tree with fewer bends. Experimental results also indicate that our solution can be comparable with previous non-planar zero skew clock routing algorithm. [ABSTRACT FROM AUTHOR]