The requirement for including the air-conditioning and the battery-cooling loads within the energy efficiency analyses of a hybrid electric vehicle is widely recognized and has promoted system-level simulations and integrated modelling, escalating the challenge of balancing the accuracy and the speed of simulations. In this paper, a hybrid electric vehicle model is created through co-simulation of the passenger cabin, the air conditioning, the battery cooling, and the powertrai. Calibration and verification of the submodels help determine their accuracy in representing the target vehicle and achieve a balance between the model fidelity and the simulation speed. The result is a model which has a higher accuracy and a higher speed than those of similar models developed previously and which provides a reliable tool for a thorough investigation of the cooling loads for different ambient conditions and different duty cycles.Keywords Vehicle simulations, system-level simulations, co-simulations, hybrid electric vehicle model, energy efficiency of a hybrid electric vehicle, cooling load Date
Ambient conditions can have a significant impact on the average and maximum temperature of the battery of electric and plug-in hybrid electric vehicles. Given the sensitivity of the ageing mechanisms of typical battery cells to temperature, a significant variability in battery lifetime has been reported with geographical location. In addition, high battery temperature and the associated cooling requirements can cause poor passenger thermal comfort, while extreme battery temperatures can negatively impact the power output of the battery, limiting the available electric traction torque. Avoiding such issues requires enabling battery cooling even when the vehicle is parked and not plugged in (key-off), but the associated extra energy requirements make applying key-off cooling a non-trivial decision. In this paper, a representative plug-in parallel hybrid electric vehicle model is used to simulate a typical 24-h duty cycle to quantify the impact of hot ambient conditions on three performance attributes of the vehicle: the battery lifetime, passenger thermal comfort and fuel economy. Key-off cooling is defined as an optimal control problem in view of the duty cycle of the vehicle. The problem is then solved using the dynamic programming method. Controlling key-off cooling through this method leads to significant improvements in the battery lifetime, while benefiting the fuel economy and thermal comfort attributes. To further improve the battery lifetime, partial charging of the battery is considered. An algorithm is developed that determines the optimum combination of key-off cooling and the level of battery charge. Simulation results confirm the benefits of the proposed method.
Professor David Greenwood offers insights into the challenges and current and future development trends in the automotive industry. Based on his broad experience in this sector, Professor Greenwood discusses a wide range of topics, such as global and UK automotive industry markets, emerging technologies in energy storage and its impacts on the environment and vehicle performance, and autonomous and future vehicles.
<div class="section abstract"><div class="htmlview paragraph">Modern electric vehicles (EVs) have complex thermal systems due to stringent energy efficiency requirements. The thermal systems of such vehicles have highly nonlinear and strongly coupled dynamics as they operate under various thermal modes. Extracting the maximum performance benefits from such complex systems requires elaborate and modern control strategies since classic and rule-based strategies cannot effectively control them. This is becoming a challenge for electric vehicles. Feedback linearization is a control approach that is designed based on the mathematical model of the system. It has the advantage of requiring low computational resources, specifically, low-computational-time and low-memory usage when compared to control strategies such as Model Predictive Control (MPC).</div><div class="htmlview paragraph">This paper presents a feedback linearization controller that is designed using a nonlinear physics-based model for cabin heating of an electric vehicle. The nonlinear physics-based model is derived from cabin heating governing equations and is correlated with a 1-D model of the thermal systems of the target vehicle. The controller is composed of an Input-Output Feedback Linearization and a Proportional-Integral control. The controller is implemented in an onboard embedded Electronic Control Unit (ECU) and tested on an electrified vehicle. The performance of the classic or Rule-Based controller and the Feedback-Linearization controller are compared.</div></div>
<div class="section abstract"><div class="htmlview paragraph">Stringent requirements for high fuel economy and energy efficiency mandate using increasingly complex vehicle thermal systems in most types of electrified vehicles (xEVs). Enabling the maximum benefits of such complex thermal systems under the full envelope of their operating modes demands designing complex thermal control systems. This is becoming one of the most challenging problems for electrified vehicles. Typically, the thermal systems of such vehicles have several modes of operation, constituting nonlinear multiple-input/multiple-output (MIMO) dynamic systems that cannot be efficiently controlled using classical or rule based strategies. This paper covers the different steps towards the design of a model-based control (MBC) strategy that can improve the overall performance of xEV thermal control systems. To achieve the above objective, the latter MBC strategy is applied to control cooling of the cabin and high voltage battery. First, a plant model representative of a real vehicle thermal dynamics is developed in Amesim®1D Software. In order to design the model-based controller, the plant model is then utilized to obtain a linear mathematical model using system identification methods. In virtue of its suitability for multivariable systems and its low computational cost, the Linear-Quadratic-Gaussian (LQG) controller is utilized to meet energy efficiency and regulation performance objectives. The robustness against the external disturbances as well as structural uncertainties is demonstrated through rigorous simulations for the considered approach.</div></div>
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