Browsing by Author "Arora, Shashank"
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- Application of Robust Design Methodology to Battery Packs for Electric Vehicles : Identification of Critical Technical Requirements for Modular Architecture
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2018-07-02) Arora, Shashank; Kapoor, Ajay; Shen, WeixiangModularity-in-design of battery packs for electric vehicles (EVs) is crucial to offset their high manufacturing cost. However, inconsistencies in performance of EV battery packs can be introduced by various sources. Sources of variation affect their robustness. In this paper, parameter diagram, a value-based conceptual analysis approach, is applied to analyze these variations. Their interaction with customer requirements, i.e., ideal system output, are examined and critical engineering features for designing modular battery packs for EV applications are determined. Consequently, sources of variability, which have a detrimental effect on mass-producibility of EV battery packs, are identified and differentiated from the set of control factors. Theoretically, appropriate control level settings can minimize sensitivity of EV battery packs to the sources of variability. In view of this, strength of the relationship between ideal system response and various control factors is studied using a “house of quality” diagram. It is found that battery thermal management system and packaging architecture are the two most influential parameters having the largest effect on reliability of EV battery packs. More importantly, it is noted that heat transfer between adjacent battery modules cannot be eliminated. For successful implementation of modular architecture, it is, therefore, essential that mechanical modularity must be enabled via thermal modularity of EV battery packs. - Comparing seven methods for state-of-health time series prediction for the lithium-ion battery packs of forklifts
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2021-11) Huotari, Matti; Arora, Shashank; Malhi, Avleen; Främling, KaryA key aspect for the forklifts is the state-of-health (SoH) assessment to ensure the safety and the reliability of uninterrupted power source. Forecasting the battery SoH well is imperative to enable preventive maintenance and hence to reduce the costs. This paper demonstrates the capabilities of gradient boosting regression for predicting the SoH timeseries under circumstances when there is little prior information available about the batteries. We compared the gradient boosting method with light gradient boosting, extra trees, extreme gradient boosting, random forests, long short-term memory networks and with combined convolutional neural network and long short-term memory networks methods. We used multiple predictors and lagged target signal decomposition results as additional predictors and compared the yielded prediction results with different sets of predictors for each method. For this work, we are in possession of a unique data set of 45 lithium-ion battery packs with large variation in the data. The best model that we derived was validated by a novel walk-forward algorithm that also calculates point-wise confidence intervals for the predictions; we yielded reasonable predictions and confidence intervals for the predictions. Furthermore, we verified this model against five other lithium-ion battery packs; the best model generalised to greater extent to this set of battery packs. The results about the final model suggest that we were able to enhance the results in respect to previously developed models. Moreover, we further validated the model for extracting cycle counts presented in our previous work with data from new forklifts; their battery packs completed around 3000 cycles in a 10-year service period, which corresponds to the cycle life for commercial Nickel-Cobalt-Manganese (NMC) cells. - A Dynamic Battery State-of-Health Forecasting Model for Electric Trucks: Li-Ion Batteries Case-Study
A4 Artikkeli konferenssijulkaisussa(2021-02-16) Huotari, Matti; Arora, Shashank; Malhi, Avleen; Främling, KaryIt is of extreme importance to monitor and manage the battery health to enhance the performance and decrease the maintenance cost of operating electric vehicles. This paper concerns the machine-learning-enabled state-of-health (SoH) prognosis for Li-ion batteries in electric trucks, where they are used as energy sources. The paper proposes methods to calculate SoH and cycle life for the battery packs. We propose autoregressive integrated modeling average (ARIMA) and supervised learning (bagging with decision tree as the base estimator; BAG) for forecasting the battery SoH in order to maximize the battery availability for forklift operations. As the use of data-driven methods for battery prognostics is increasing, we demonstrate the capabilities of ARIMA and under circumstances when there is little prior information available about the batteries. For this work, we had a unique data set of 31 lithium-ion battery packs from forklifts in commercial operations. On the one hand, results indicate that the developed ARIMA model provided relevant tools to analyze the data from several batteries. On the other hand, BAG model results suggest that the developed supervised learning model using decision trees as base estimator yields better forecast accuracy in the presence of large variation in data for one battery. - A HYBRID THERMAL MANAGEMENT SYSTEM WITH NEGATIVE PARASITIC LOSSES FOR ELECTRIC VEHICLE BATTERY PACKS
A4 Artikkeli konferenssijulkaisussa(2018-11-13) Arora, Shashank; Tammi, KariParasitic power requirement is a key criterion in selection of suitable battery thermal management system (TMS) for EV applications. This paper presents a hybrid TMS with negative parasitic requirements, designed by integrating phase change material (PCM) with thermoelectric devices. The proposed system does not require any power consumption to maintain tight control over battery cell temperature during aggressive use and repetitive cycling. In addition, it can recover a portion of waste heat produced during the typical operation of EV battery packs. Commercially available 〖LiFePO〗_4 20 Ah pouch cell has been chosen as a test battery sample for validating the conceptual design presented herein. The commercial battery cells, submerged in a PCM-filled polycarbonate casing, are subjected to a cyclic discharge process to elucidate their heat generation characteristics at 27 °C. Charging and discharging is conducted at 0.5C and 1C, respectively. A thermoelectric circuit is used to recover the heat energy absorbed by the PCM and to convert it to electrical energy. The manuscript further details some of the major findings of this experiment.