Nonlinear model predictive control for thermal management in plug-in hybrid electric vehicles

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.A nonlinear model predictive control (NMPC) for the thermal management (TM) of Plug-in Hybrid Electric Vehicles (PHEVs) is presented. TM in PHEVs is crucial to ensure good components performance and durability in all possible climate scenarios. A drawback of accurate TM solutions is the higher electrical consumption due to the increasing number of low voltage (LV) actuators used in the cooling circuits. Hence, more complex control strategies are needed for minimizing components thermal stress and at the same time electrical consumption. In this context, NMPC arises as a powerful method for achieving multiple objectives in Multiple input- Multiple output systems. This paper proposes an NMPC for the TM of the High Voltage (HV) battery and the power electronics (PE) cooling circuit in a PHEV. It distinguishes itself from the previously NMPC reported methods in the automotive sector by the complexity of its controlled plant which is highly nonlinear and controlled by numerous variables. The implemented model of the plant, which is based on experimental data and multi- domain physical equations, has been validated using six different driving cycles logged in a real vehicle, obtaining a maximum error, in comparison with the real temperatures, of 2C. For one of the six cycles, an NMPC software-in-the loop (SIL) is presented, where the models inside the controller and for the controlled plant are the same. This simulation is compared to the finite-state machine-based strategy performed in the real vehicle. The results show that NMPC keeps the battery at healthier temperatures and in addition reduces the cooling electrical consumption by more than 5%. In terms of the objective function, an accumulated and weighted sum of the two goals, this improvement amounts 30%. Finally, the online SIL presented in this paper, suggests that the used optimizer is fast enough for a future implementation in the vehicle.Accepted versio

Comparative analysis of battery electric vehicle thermal management systems under long-range drive cycles

Due to increasing regulation on emissions and shifting consumer preferences, the wide adoption of battery electric vehicles (BEV) hinges on research and development of technologies that can extend system range. This can be accomplished either by increasing the battery size or via more efficient operation of the electrical and thermal systems. This study endeavours to accomplish the latter through comparative investigation of BEV integrated thermal management system (ITMS) performance across a range of ambient conditions (-20 °C to 40 °C), cabin setpoints (18 °C to 24 °C), and six different ITMS architectures. A dynamic ITMS modelling framework for a long-range electric vehicle is established with comprehensive sub models for the operation of the drive train, power electronics, battery, vapor compression cycle components, and cabin conditioning in a comprehensive transient thermal system modelling environment. A baseline thermal management system is studied using this modelling framework, as well as four common thermal management systems found in literature. This study is novel for its combination of comprehensive BEV characterization, broad parametric analysis, and the long range BEV that is studied. Additionally, a novel low-temperature waste heat recovery (LT WHR) system is proposed and has shown achieve up to a 15% range increase at low temperatures compared to the baseline system, through the reduction of the necessary cabin ventilation loading. While this system shows performance improvements, the regular WHR system offers the greatest benefit, a 13.5% increase in cold climate range, for long-range BEV drive cycles in terms of system range and transient response without the need for additional thermal system equipment

Evaluation of Heat Pumping and Waste Heat Recovery for Battery Electric Vehicle Thermal Management

Due to increasing regulation on emissions and shifting consumer preferences, the wide adoption of battery electric vehicles (BEV) hinges on research and development of technologies that can extend system range. This can be accomplished either by increasing the battery size or via more efficient operation of the electrical and thermal systems. This study evaluates the range performance of a BEV integrated thermal management system (ITMS) with heat pumping and waste heat recovery across a range of ambient conditions (-20 °C to 40 °C) and cabin setpoints (18 °C to 24 °C). A dynamic ITMS modelling framework for a long-range electric vehicle is established with comprehensive sub models for the operation of the drive train, power electronics, battery, vapor compression cycle components, and cabin conditioning. This modelling framework is used to construct a baseline thermal management system. The waste heat recovery (WHR) system is compared to the baseline and shown to offers significant benefit in terms of driving range for long-range BEV drive cycles in terms of system range and transient response

Review of Cabin Thermal Management for Electrified Passenger Vehicles

Peer reviewe

MODELING OF THERMAL DYNAMICS IN CHEVROLET VOLT GEN II HYBRID ELECTRIC VEHICLE FOR INTEGRATED POWERTRAIN AND HVAC OPTIMAL OPERATION THROUGH CONNECTIVITY

Integrated thermal energy management across system level components in electric vehicles (EVs) and hybrid electric vehicles (HEVs) is currently an under explored space. The proliferation of connected vehicles and accompanying infrastructure in recent years provides additional motivation to explore opportunities in optimizing thermal energy management in EVs and HEVs with the help of this newly available connected vehicle data. This thesis aims to examine and analyze the potential to mitigate vehicle energy consumption and extend usable driving range through optimal control strategies which exploit physical system dynamics via controls integration of vehicle subsystems. In this study, data-driven and physics-based models for heating, ventilation and air-conditioning (HVAC) are developed and utilized along with the vehicle dynamics and powertrain (VD\&PT) models for a GM Chevrolet Volt hybrid electric vehicle to enable co-optimization of HVAC and VD\&PT systems of HEVs. The information available in connected vehicles like driver schedules, trip duration and ambient conditions is leveraged along with the vehicle system dynamics to predict operating conditions of the vehicle under study. All this information is utilized to optimize the operation of an integrated HVAC and VD\&PT system in a hybrid electric vehicle to achieve the goal of reduced energy consumption. For achieving the goals outlined for this thesis, an integrated HVAC and VD\&PT model is developed and the various components, sub-systems and systems are validated against real world test data. Then, integrated relationships relevant to the thermal dynamics of an HEV are established. These relationships comprise the combined operational characteristics of the internal combustion (IC) engine coolant and the cabin electric heater for cabin heating, coordinated controls of IC engine using engine coolant and catalyst temperatures for cabin thermal conditioning in cold ambient conditions and the combined operational characteristics of the air-conditioning compressor for conditioning both cabin and high-voltage battery in an HEV. Next, these sub-system and system relationships are used to evaluate potential energy savings in cabin heating and cooling when vehicle\u27s operating schedule is known. Finally, an optimization study is conducted to establish an energy efficient control strategy which maximizes the HVAC energy efficiency whilst maintaining occupant comfort levels according to ASHRAE standards, all while improving the usable range of the vehicle relative to its baseline calibration. The mean energy savings in overall vehicle energy consumption using an integrated HVAC - Powertrain control strategy and a coordinated thermal management strategy proposed in this work are $3$\% and $14$\% respectively

Optimal battery thermal management for electric vehicles with battery degradation minimization

The control of a battery thermal management system (BTMS) is essential for the thermal safety, energy efficiency, and durability of electric vehicles (EVs) in hot weather. To address the battery cooling optimization problem, this paper utilizes dynamic programming (DP) to develop an online rule-based control strategy. Firstly, an electrical–thermal-aging model of the LiFePO4 battery pack is established. A control-oriented onboard BTMS model is proposed and verified under different speed profiles and temperatures. Then in the DP framework, a cost function consisting of battery aging cost and cooling-induced electricity cost is minimized to obtain the optimal compressor power. By exacting three rules ”fast cooling, slow cooling, and temperature-maintaining” from the DP result, a near-optimal rule-based cooling strategy, which uses as much regenerative energy as possible to cool the battery pack, is proposed for online execution. Simulation results show that the proposed online strategy can dramatically improve the driving economy and reduce battery degradation under diverse operation conditions, achieving less than a 2.18% difference in battery loss compared to the offline DP. Recommendations regarding battery cooling under different real-world cases are finally provided

Surrogate Model of the Optimum Global Battery Pack Thermal Management System Control

_The control of the battery-thermal-management-system (BTMS) is key to prevent catastrophic events and to ensure long lifespans of the batteries. Nonetheless, to achieve a high-quality control of BTMS, several technical challenges must be faced: safe and homogeneous control in a multi element system with just one actuator, limited computational resources, and energy consumption restrictions. To address those challenges and restrictions, we propose a surrogate BTMS control model consisting of a classification machine-learning model that defines the optimum cooling-heating power of the actuator according to several temperature measurements. The la-belled-data required to build the control model is generated from a simulation environment that integrates model-predictivecontrol and linear optimization concepts. As a result, a controller that optimally controls the actuator with multi-input temperature signals in a multi-objective optimization problem is constructed. This paper benchmarks the response of the proposal using different classification machine-learning models and compares them with the responses of a state diagram controller and a PID controller. The results show that the proposed surrogate model has 35% less energy consumption than the evaluated state diagram, and 60% less energy consumption than a traditional PID controller, while dealing with multi-input and multi-objective systems.European Commissio: Grant Agreement No. 824300