Intelligent vehicle drive mode which predicts the driver behavior vector to augment the engine performance in real-time

Kolachalama, Srikanth; Malik, Hafiz

doi:10.1017/dce.2022.15

Cited by 3 publications

(3 citation statements)

References 34 publications

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“…The data retrieval procedure adopted in Kolachalama and Malik (2021) and Kolachalama and Malik (2022) produced the best results for the computational model that predicted the driver behavior vector (DBV). Hence, a similar process was followed to retrieve the 2023 Cadillac Lyriq test vehicle data.…”

Section: Data Retrievalmentioning

confidence: 99%

“…Kolachalama and Malik (2021, 2022) performed multiple experiments using Cadillac test vehicle segments and analyzed the vector EOP. Hence, in this article, a similar experiment was conducted using the electric test vehicle 2023 Cadillac Lyriq to investigate the properties of the performance parameters.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, enhanced battery health could result when IBAT = [71 77] ℉[Balamurugan et al, 2022].The most exciting part of this research is the analysis of the vector [MOP] = [IMOT, IMOS, IMOC, IROT, ISOT]. In combustion engines, the values of [IET] and[IFCR] have fluctuating magnitudes due to the intricate property of the flame impingement[Kolachalama and Malik, 2022]. In contrast, the curves [IMOT] and[IMOC] have smooth behaviour with a direct linear relationship, except for the case Speed = 0 MPH.…”

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confidence: 99%

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Interpretation of the electric vehicle operating point in real time

Kolachalama

Surti²,

Malik³

2023

Exp Results

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In this article, the performance parameters of the electric vehicle were investigated, and its operating point was defined using the core components [Battery, Inverter, Motor]. The test vehicle 2023 Cadillac Lyriq, provided by General Motors Inc., was driven on specified road segments, and the real-time data was retrieved using the integrated controller area network architecture. The neoVI -Fire 2 tool was connected to the vehicle system, which records the dynamic data, and Vehicle Spy software was utilised to convert the data into a readable format. Finally, the vector electric vehicle operating point (EVOP) was proposed, and the corresponding behaviour was interpreted. This methodology could assist researchers in understanding the dynamic behaviour of electric vehicle parameters to develop integrated techniques which augment the performance in real time.

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Section: Data Retrievalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Interpretation of the electric vehicle operating point in real time

Kolachalama

Surti²,

Malik³

2023

Exp Results

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Neural Network Model to Predict the Thermal Operating Point of an Electric Vehicle

Kolachalama¹,

Malik²

2023

SAE Technical Paper Series

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<div class="section abstract"><div class="htmlview paragraph">The automotive industry widely accepted the launch of electric vehicles in the global market, resulting in the emergence of many new areas, including battery health, inverter design, and motor dynamics. Maintaining the desired thermal stress is required to achieve augmented performance along with the optimal design of these components. The HVAC system controls the coolant and refrigerant fluid pressures to maintain the temperatures of [Battery, Inverter, Motor] in a definite range. However, identifying the prominent factors affecting the thermal stress of electric vehicle components and their effect on temperature variation was not investigated in real-time. Therefore, this article defines the vector electric vehicle thermal operating point (EVTHOP) as the first step with three elements [instantaneous battery temperature, instantaneous inverter temperature, instantaneous stator temperature]. As a next step, a novel deep learning model was proposed utilizing the integrated functions of MATLAB, which predicts the vector EVTHOP mapping the elements of [Body module, Driver behavior, Environmental factors], which represent the dynamic state of the system. The trained models were developed using real-time data retrieved by driving the test vehicle 2023 Cadillac Lyriq, provided by General Motors Inc. Since the data retrieved is time-series, the trained functions were developed using the known established method NARX. The Error vector was defined by estimating the conformance of actual and predicted values. The performance of NARX was done by analyzing the Error using the known statistical techniques (RMSE, Area under the curve, Smoothness measure: RSquare). The data snippets for 100 seconds were selected randomly to validate the deep learning model, and it was observed that statistical analysis of the Error resulted [RMSE < 0.2; Area < 632, RSquare > 0.7] in all scenarios. Thus, the developed predictive model was assumed to produce satisfactory results in predicting the vector EVTHOP.</div></div>

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Extended Deep Learning Model to Predict the Electric Vehicle Motor Operating Point

Kolachalama,

Malik

2024

SAE Technical Paper Series

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<div class="section abstract"><div class="htmlview paragraph">The transition from combustion engines to electric propulsion is accelerating in every coordinate of the globe. The engineers had strived hard to augment the engine performance for more than eight decades, and a similar challenge had emerged again for electric vehicles. To analyze the performance of the engine, the vector engine operating point (EOP) is defined, which is common industry practice, and the performance vector electric vehicle motor operating point (EVMOP) is not explored in the existing literature. In an analogous sense, electric vehicles are embedded with three primary components, e.g., <i>Battery</i>, <i>Inverter</i>, <i>Motor</i>, and in this article, the EVMOP is defined using the parameters [motor torque, motor speed, motor current]. As a second aspect of this research, deep learning models are developed to predict the EVMOP by mapping the parameters representing the dynamic state of the system in real-time. The required data is obtained by the testing of a 2023 Cadillac Lyriq electric vehicle (single motor) driven on a specified road segment. The trained functions are developed utilizing the integrated functions of MATLAB, which include the machine learning methods including non-linear autoregressive (NARX), long short-term memory (LSTM), and neural net fit (NNF), suiting vehicle data in the time domain. The performance of the methods is validated by estimating the Error vector representing the conformance of actual and predicted values of randomly selected road snippet data for ten seconds. The RMSE values of the Error, and its first-order derivative (dError) are analyzed as a metric of performance, and thus the best suitable method for modeling the electric vehicle data is identified. It is observed that the NARX method in conjunction with the scaled conjugate gradient (SCG) solver outperformed with the least computational time (< 66.48 s) and lower RMSE, e.g., Error < 3.64 and dError < 4.94, values in all scenarios.</div></div>

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Intelligent vehicle drive mode which predicts the driver behavior vector to augment the engine performance in real-time

Cited by 3 publications

References 34 publications

Interpretation of the electric vehicle operating point in real time

Interpretation of the electric vehicle operating point in real time

Neural Network Model to Predict the Thermal Operating Point of an Electric Vehicle

Extended Deep Learning Model to Predict the Electric Vehicle Motor Operating Point

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