Experimental validation of electric bus powertrain model under city driving cycles

Halmeaho, Teemu; Rahkola, Pekka; Tammi, Kari; Pippuri, Jenni; Pellikka, Ari-Pekka; Manninen, Aino; Ruotsalainen, Sami

doi:10.1049/iet-est.2016.0028

Cited by 31 publications

(15 citation statements)

References 36 publications

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“…The aggressiveness was calculated as follows [30]: The characteristic parameters of the Espoo 11 (E11) route are compared with those of the well-known Braunschweig (BR) bus test cycle in Table 2. Although the BR cycle represents urban driving, it was still considered to be a good comparison point because of its relatively similar characteristics and widespread use in literature, such as in the works of [2,14,27,28]. The parameter list was adapted based on previous studies [2,29].…”

Section: Driving Cycle Synthesis Using Monte Carlo Methodsmentioning

confidence: 99%

City Bus Powertrain Comparison: Driving Cycle Variation and Passenger Load Sensitivity Analysis

et al. 2018

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Alternative powertrains are rapidly increasing in popularity in city buses. Hence, it is vitally important to understand the factors affecting the performance of the powertrains in order to operate them on appropriate routes and as efficiently as possible. To that end, this paper presents an exhaustive driving cycle and passenger load sensitivity analysis for the most common city bus powertrain topologies. Three-thousand synthetic cycles were generated for a typical suburban bus route based on measured cycles and passenger numbers from the route. The cycles were simulated with six bus models: compressed natural gas, diesel, parallel hybrid, series hybrid, hydrogen fuel cell hybrid, and battery electric bus. Twenty reference cycles featuring various types of routes were simulated for comparison. Correlations between energy consumption and the various driving cycle parameters and passenger loads were examined. Further analysis was conducted with variance decomposition. Aggressiveness and stop frequency had the highest correlation with the consumption. The diesel bus was the most sensitive to aggressiveness. The parallel hybrid had a lower statistical dispersion of consumption than the series hybrid on the suburban route. On the varied routes, the opposite was true. The performance of the parallel hybrid powertrain deteriorated significantly on cycles with high aggressiveness and stop frequency. In general, the high correlation between aggressiveness and energy consumption implies that particular attention must be paid to limiting high-speed accelerations of city buses.

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Section: Driving Cycle Synthesis Using Monte Carlo Methodsmentioning

confidence: 99%

City Bus Powertrain Comparison: Driving Cycle Variation and Passenger Load Sensitivity Analysis

et al. 2018

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“…Lajunen [12] also developed a method for optimizing the speed profile of BEBs, which improved energy efficiency by up to 17%. In addition to bus payload, a notable consumption and consumption variation difference between lightweight (0.52-1.42 kWh/km [13,14]) and heavyweight chassis buses (1.24-2.48 kWh/km [15]) has been reported. During the years 2014-2015, Kontou & Miles [16] tested a BEB fleet of eight buses in Milton Keynes, GB.…”

Section: State-of-the-artmentioning

confidence: 99%

Energy Uncertainty Analysis of Electric Buses

et al. 2018

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Uncertainty in operation factors, such as the weather and driving behavior, makes it difficult to accurately predict the energy consumption of electric buses. As the consumption varies, the dimensioning of the battery capacity and charging systems is challenging and requires a dedicated decision-making process. To investigate the impact of uncertainty, six electric buses were measured in three routes with an Internet of Things (IoT) system from February 2016 to December 2017 in southern Finland in real operation conditions. The measurement results were thoroughly analyzed and the operation factors that caused variation in the energy consumption and internal resistance of the battery were studied in detail. The average energy consumption was 0.78 kWh/km and the consumption varied by more than 1 kWh/km between trips. Furthermore, consumption was 15% lower on a suburban route than on city routes. The energy consumption was mostly influenced by the ambient temperature, driving behavior, and route characteristics. The internal resistance varied mainly as a result of changes in the battery temperature and charging current. The energy consumption was predicted with above 75% accuracy with a linear model. The operation factors were correlated and a novel second-order normalization method was introduced to improve the interpretation of the results. The presented models and analyses can be integrated to powertrain and charging system design, as well as schedule planning.

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“…Some researchers focused on accurately predicting the energy consumption rate of EV to reduce the range anxiety. Halmeaho et al [38] established an electric bus simulation model in which a motor efficiency curve model and a resistance model were utilized to predict the energy consumption rate. Fiori et al [39] considered the regenerative braking energy efficiency at different vehicle speeds to predict energy consumption with an average error of 5.9%.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning-Based Method for Remaining Range Prediction of Electric Vehicles

et al. 2020

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Limited driving range is one of the major obstacles to the widespread application of electric vehicles (EVs). Accurately predicting the remaining driving range can effectively reduce the range anxiety of drivers. In this paper, a blended machine learning model was proposed to predict the remaining driving range of EVs based on real-world historical driving data. The blended model fuses two advanced machine learning algorithms of Extreme Gradient Boosting Regression Tree (XGBoost) and Light Gradient Boosting Regression Tree (LightGBM). The proposed model was trained to "learn" the relationship between the driving distance and the proposed features such as cumulative output energy of the motor and the battery, different driving patterns, and temperature of the battery). In addition, an "anchor (baseline) based" strategy was proposed and was seen to be able to effectively eliminate the unbalance distribution of dataset. The results of experiments suggest that our proposed anchor-based blended model has better performances with a smaller prediction error range of [-0.8, 0.8] as compared with previous methods. INDEX TERMS electric vehicle, remaining driving range estimation, machine learning, data mining.

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Experimental validation of electric bus powertrain model under city driving cycles

Cited by 31 publications

References 36 publications

City Bus Powertrain Comparison: Driving Cycle Variation and Passenger Load Sensitivity Analysis

City Bus Powertrain Comparison: Driving Cycle Variation and Passenger Load Sensitivity Analysis

Energy Uncertainty Analysis of Electric Buses

Machine Learning-Based Method for Remaining Range Prediction of Electric Vehicles

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