Optimal Management of Thermal Comfort and Driving Range in Electric Vehicles

Lahlou, Anas; Ossart, Florence; Boudard, Emmanuel; Roy, Francis; Bakhouya, Mohamed

doi:10.3390/en13174471

Cited by 24 publications

(14 citation statements)

References 36 publications

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“…The target function should not only focus on the total electric energy expenditure, but would also involve a balance with a passenger comfort parameter and remaining driving range. As a further improvement with respect to an optimal control approach explored by Lahlou et al in [35], a connected BEV, having set a defined road trip in the GPS navigation aid system and knowing the traffic level and the environmental conditions, could estimate in real time the energy needed for traction and the energy available for regulating the thermal comfort. This possibility was simulated by the author in [36] for different traffic and weather condition scenarios, and different initial battery states of charge.…”

Section: • Advanced Thermal Storagementioning

confidence: 99%

Assessing the Energy Consumption and Driving Range of the QUIET Project Demonstrator Vehicle

et al. 2022

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This article summarises the experimental testing campaign performed at the Joint Research Centre (JRC) on the demonstrator battery electric vehicle (BEV) of the European Union Horizon 2020 research project QUIET. The project, launched in October 2017, aimed at developing an improved and energy-efficient electric vehicle with increased driving range under real-world driving conditions, focusing on three areas: improved energy management, lightweight materials with enhanced thermal insulation properties, and improved safety and comfort. A heating, venting, and air conditioning (HVAC) system based on the refrigerant R290 (propane), a phase change material (PCM) thermal storage system, infrared heating panels in the near field of the passengers, lightweight materials for seat internal structures, and composite vehicle doors with a novel atomically precise manufacturing (APM) aluminium foam are all the breakthrough technologies installed on the QUIET demonstrator vehicle. All these innovative technologies allow the energetic request for cooling and heating the cabin of the demonstrator vehicle under different driving conditions and the weight of the vehicle components (e.g., doors, windshields, seats, heating, and air conditioning) to be reduced by about 28%, leading to an approximately 26% driving range increase under both hot (40 °C) and cold (−10 °C) weather conditions.

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Section: • Advanced Thermal Storagementioning

confidence: 99%

Assessing the Energy Consumption and Driving Range of the QUIET Project Demonstrator Vehicle

et al. 2022

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“…These latter are generated to achieve efficient BEV energy management. Decisions could be rerouting suggestions (i.e., best driving routes) to the driver, charging schedule, or even control actions (e.g., heating, ventilation, and air-conditioning control) [6]. Compliance with these suggestions enables a less restrictive BEV exploitation and extends its remaining driving range.…”

Section: Decision Support Subsystemmentioning

confidence: 99%

“…For instance, sensor data could be used in Intelligent Transportation System (ITS) applications to improve traffic efficiency and road safety [5]. Moreover, control strategies can be deployed in BEVs in order to maximize their driving range by, for example, controlling the speed and other in-vehicle systems (e.g., HVAC) [6]. All these technologies could be combined in a holistic way to develop an intelligent in-vehicle battery energy management system (BEMS).…”

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confidence: 99%

Embedded Real-Time Speed Forecasting for Electric Vehicles: A Case Study on RSK Urban Roads

et al. 2022

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During the past ten years, worldwide efforts have been pursuing an ambitious policy of sustainable development, particularly in the energy sector. This ambition was revealed by noticeable progress in the deployment and development of infrastructures for the production of renewable electrical energy. These infrastructures combined with the deployment of wired and wireless communications could support research actions in the field of connected electro-mobility. Also, this progress was manifested by the development of electric vehicles (EV), penetrating our transportation roads more and more. They are considered among the potential solutions, which are envisaged to further reduce road transport's greenhouse gas emissions, relying on low-carbon energy production. However, the uncertainty caused by both external road disturbances and drivers' behavior could influence the prediction of upcoming power demands. These latter are mainly affected by the unpredictability of the electric vehicles' speed on transportation roads. In this work, we introduce an energy management platform, which interfaces with in-vehicle components, using a developed embedded system, and external services, using IoT and big data technologies, for efficient battery power use. The platform was deployed in real-setting scenarios and tested for EV speed prediction. In fact, we have used driving data, which have been collected on Rabat-Salé-Kénitra (RSK) urban roads by our Twizy EV. A multivariate Long Short Term Memory (LSTM) algorithm was developed and deployed for speed forecasting. The effectiveness of LSTM was evaluated against well-known algorithms: Auto Regressive Integrated Moving Average (ARIMA), Convolutional Neural Network (CNN) and Convolutional LSTM (ConvLSTM). Experiments have been conducted using two approaches; the whole trajectory dataset and segmented trajectory datasets to train the models. The experimentation results show that LSTM outperforms the other used algorithms in terms of forecasting the speed, especially when using the trajectory segmentation approach.INDEX TERMS Electro-mobility, Intelligent Transportation Systems, Speed forecasting, Time-series forecasting, Deep learning I. INTRODUCTIONN OWADAYS, electric vehicles (EVs) are penetrating more and more transportation roads. There are three main categories of EVs: Hybrid Electric Vehicles (HEV), Plugin Hybrid Electric Vehicles (PHEV), and Batterypowered Electric Vehicles (BEV). BEVs are fully-electric vehicles with rechargeable batteries. HEV and PHEV are, however, powered by both gasoline and electricity. Unlike PHEV, which can be recharged through both regenerative braking and external electricity sources, HEV is charged from the electricity generated by the car's braking system. However, both of them are still using gasoline, which is one of the sources of greenhouse gas.BEVs have been getting more attention in the last years, as they are environment-friendly while having an acceptable driving range, particularly in urban areas. BEV driving range

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“…Supply control of variable air volumes helps to meet different thermal demand objectives in cabin [ 16 ]. Thermal condition strategies based on thermal loads and thermal comfort can effectively reduce the energy consumption of electric vehicles [ 17 – 19 ]. Related strategies focus on thermal comfort and energy management when the vehicle environment stabilizes, while effective management strategies for the initial thermal environment is lacking.…”

Section: Introductionmentioning

confidence: 99%

Environmental conditions driven method for automobile cabin pre-conditioning with multi-satisfaction objectives

Chen

Lan

2022

PLoS ONE

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The optimal initial pre-conditioning parameter is essential to properly adjust the temperature within the cabin in an effective and accurate way, especially while passengers’ thermal comfort and energy-saving properties are both considered. Under the various environmental thermal loads, the pre-conditioning solutions resulting from those pre-fixed cooling parameters are unfeasible for achieving accurately passengers’ comfort temperature. In addition, it is also difficult in such a narrow car space to identify a lot of local attributes due to the different material properties and sizes of a variety of structural parts that have various thermal responses to environmental conditions. This paper presents a data-driven decision model to numerically identify the degrees of the cabin thermal characteristic to determine satisfactory pre-conditioning parameter schemes. Initially, based on the thermal data within a vehicle recorded through the whole year at a selected hot climate region of the Middle East, the study levels multiple climate scenes corresponding to change in the cabin air temperature. Then three classification algorithms (Support Vector Machines, Decision Tree, and K-nearest neighbor model) are used to comparatively identify climate levels according to the input conditions. Based on the identified climate level, an appropriate parameters scheme for this level is applied. A comprehensive evaluation index (CEI) is proposed to characterize the passengers’ satisfaction in numerical computation, on considering multi-satisfaction objectives including Predicted Mean Vote (PMV), local temperature, air quality, and energy efficiency; and it formulates the pre-conditioning parameter scheme for each climate scene with CEI. Several scene cases are carried out to verify the effectiveness of the proposed models. The result shows that the pre-conditioning schemes of the model can effectively satisfy passengers in multi-satisfaction objectives.

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Optimal Management of Thermal Comfort and Driving Range in Electric Vehicles

Cited by 24 publications

References 36 publications

Assessing the Energy Consumption and Driving Range of the QUIET Project Demonstrator Vehicle

Assessing the Energy Consumption and Driving Range of the QUIET Project Demonstrator Vehicle

Embedded Real-Time Speed Forecasting for Electric Vehicles: A Case Study on RSK Urban Roads

Environmental conditions driven method for automobile cabin pre-conditioning with multi-satisfaction objectives

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