Implementation of machine learning based real time range estimation method without destination knowledge for BEVs

Yavaşoğlu, Hüseyin Ayhan; Tetik, Yusuf Engin; Gokce, Kursad

doi:10.1016/j.energy.2019.02.032

Cited by 38 publications

(17 citation statements)

References 22 publications

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“…The decision tree (DT) is a widely used ML technique for classification and regression purposes. Knowledge gathered by DT is not only understandable by humans but also can be converted into a sequence of rules which can readily be implemented in embedded systems [24], [25]. While random forest (RF) is an integrated learning method that is operated by constructing a multitude of DTs at training time and outputting the classification results that are averaged or voted by every individual tree [26], which can reduce the error that may occur when specific DT is over-fitting and improve the accuracy of the model [27]- [30].…”

Section: A Literature Reviewmentioning

confidence: 99%

A Novel Energy Management Strategy Design Methodology of a PHEV Based on Data-Driven Approach and Online Signal Analysis

et al. 2021

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This paper introduces an energy management strategy design method for a plug-in hybrid electric vehicle based on the data-driven approach and online signal analysis. It includes two parts, mode division strategy design, and power distribution strategy design. Using the random forest in data mining technology to analyze optimization results of dynamic programming can quickly extract key information and establish optimal and understandable mode division strategy with high precision and stability directly. Besides, integrating the classic "Engine optimal operating curve control" strategy with wavelet transform and Markov prediction, which not only enhances the adaptability of the strategy to different driving conditions but also improves the fuel economy by reducing the impact of transient power on the engine operation. At the same time, to improve the prediction accuracy of the algorithm without increasing the computational complexity, this paper adds a prediction result correction function to the first-order Markov prediction model to reduce the impact of slow update of the probability matrix on prediction accuracy. The simulation results show the average prediction error of the improved Markov prediction model is reduced by 5.3% and the new energy management strategy designed reduces fuel consumption by 8.28% at the cost of a small increase in electricity consumption.INDEX TERMS Plug-in hybrid electric vehicle, dynamic programming, random forest, Markov chain, wavelet transform.

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Section: A Literature Reviewmentioning

confidence: 99%

A Novel Energy Management Strategy Design Methodology of a PHEV Based on Data-Driven Approach and Online Signal Analysis

et al. 2021

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“…the reduction of power capability [18], and quickly speed up of battery aging [19]). Among these factors, range anxiety is one of the major issues for EVs, due to their limited all-electric-range (AER) and long battery recharge times [20]. Accurately predicting the remaining driving range of EVs can offer the drivers more precise information about their remaining driving range and reduce the range anxiety [21].…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning-based approaches have been employed for the remaining driving range prediction of EVs [27][28]. In existing studies, the adopted algorithms seem to be relatively traditional; examples include multiple linear regression (MLR) [27,29], neural network [30,20], gradient boost decision tree (GBDT) [31]. In recent years, data scientists have proposed a variety of novel machine learning algorithms such as XGBoost [32] and LightGBM [33], which have been proven to have better performance than traditional methods in a number of application fields [34][35][36][37].…”

Section: Introductionmentioning

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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“…It is possible to evaluate the EMS performance assuming that the vehicle under test will be in this condition. Yavasoglu et al [24] trained a neural network to predict an electric vehicle's actual residual autonomy. The autonomy estimation is based on GPS (itinerary, road gradient profile) and ITS (traffic) information.…”

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confidence: 99%

An Innovative Methodology to Take into Account Traffic Information on WLTP Cycle for Hybrid Vehicles

et al. 2021

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The most efficient energy management strategies for hybrid vehicles are the “Optimization-Based Strategies”. These strategies require a preliminary knowledge of the driving cycle, which is not easy to predict. This paper aims to combine Worldwide Harmonized Light-Duty Vehicles Test Cycle (WLTC) low section short trips with real traffic levels for vehicle energy and fuel consumption prediction. Future research can focus on implementing a new strategy for Hybrid Electric Vehicle (HEV) energy optimization, taking into account WLTC and Google Maps traffic levels. First of all, eight characteristic parameters are extracted from real speed profiles, driven in urban road sections in the city of Messina at different traffic conditions, and WLTC short trips as well. The minimum distance algorithm is used to compare the parameters and assign the three traffic levels (heavy, average, and low traffic level) to the WLTC short trips. In this way, for each route assigned from Google maps, vehicle’s energy and fuel consumption are estimated using WLTC short trips remodulated with distances and traffic levels. Moreover, a vehicle numerical model was implemented and used to test the accuracy of fuel consumption and energy prediction for the proposed methodology. The results are promising since the average of the percentage errors’ absolute value between the experimental driving cycles and forecast ones is 3.89% for fuel consumption, increasing to 6.80% for energy.

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Implementation of machine learning based real time range estimation method without destination knowledge for BEVs

Cited by 38 publications

References 22 publications

A Novel Energy Management Strategy Design Methodology of a PHEV Based on Data-Driven Approach and Online Signal Analysis

A Novel Energy Management Strategy Design Methodology of a PHEV Based on Data-Driven Approach and Online Signal Analysis

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

An Innovative Methodology to Take into Account Traffic Information on WLTP Cycle for Hybrid Vehicles

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