| Title | (Invited Paper) Thermal Modeling for Energy-Efficient Smart Building With Advanced Overfitting Mitigation Technique |
| Author | Wandi Liu, Hai Wang (University of Electronic Science and Technology of China, China), Hengyang Zhao, Shujuan Wang (University of California at Riverside, U.S.A.), Haibao Chen, Yuzhuo Fu (Shanghai Jiaotong University, China), Jian Ma (University of Electronic Science and Technology of China, China), Xin Li (Carnegie Mellon University, U.S.A.), *Sheldon X.-D. Tan (University of California at Riverside, U.S.A.) |
| Page | pp. 417 - 422 |
| Keyword | Thermal Modeling, Smart Building |
| Abstract | Building energy accounts large amount of the total energy consumption, and smart building energy control leads to high energy efficiency and significant energy savings. A compact and accurate building thermal model is important for designing the efficient energy control system. In this paper, we propose an accurate thermal behavior modeling technique for general and complicated buildings. This new modeling technique builds compact thermal model by system identification using temperature and power data obtained from EnergyPlus software, which can provide realistic temperature, weather and power data for buildings. In order to make the best use of data from EnergyPlus and avoid the overfitting problem associated with the system identificatoin method, a cross-validation technique is employed to generate multiple thermal models to find the optimal model order. The final model is then generated by performing a regular system identification using the previously selected order. Experimental results from a case study of a 5-zone building have shown that the proposed method is able to find the optimal model order, and the building models built by the proposed method can achieve 1-3% average errors and less than 10-18% maximum errors for the estimation of zone temperatures for about a one year period. |
| Title | (Invited Paper) Modeling, Analysis, and Optimization of Electric Vehicle HVAC Systems |
| Author | *Mohammad Abdullah Al Faruque, Korosh Vatanparvar (UC Irvine, U.S.A.) |
| Page | pp. 423 - 428 |
| Keyword | Electric Vehicle, Battery, HVAC, Climate Control |
| Abstract | Major challenges of driving range and battery lifetime in Electric Vehicles (EV) have been addressed by designing more efficient power electronics, advanced embedded hardware, and sophisticated embedded software. Besides the electric motor in EVs, Heating, Ventilation, and Air Conditioning (HVAC) has been seen as a significant contributor to the EV power consumption. The main responsibility of automotive climate controls has been to control the HVAC system in order to maintain the passengers’ thermal comfort. However, the HVAC power consumption and its dynamic behavior may influence the battery lifetime and driving range significantly. Therefore, modeling and analyzing the HVAC system and its thermodynamic behavior may benefit the control designers to integrate the HVAC control and optimization into Battery Management Systems (BMS) for better battery lifetime and driving range. In this paper, the EV architecture, HVAC system dynamic behavior, and battery characteristics are explained and modeled. Automotive climate controls (e.g. battery lifetime-aware automotive climate control) and the benefits gained by system modeling and estimation for different conditions in terms of battery lifetime and driving range are illustrated. Moreover, present and future challenges regarding the HVAC system and control design are explained. |
| Title | (Invited Paper) Distributed Reconfigurable Battery System Management Architectures |
| Author | *Sebastian Steinhorst (TUM CREATE Ltd., Singapore), Zili Shao (The Hong Kong Polytechnic University, Hong Kong), Samarjit Chakraborty (TU Munich, Germany), Matthias Kauer (TUM CREATE Ltd., Singapore), Shuai Li (The Hong Kong Polytechnic University, Hong Kong), Martin Lukasiewycz, Swaminathan Narayanaswamy (TUM CREATE Ltd., Singapore), Muhammad Usman Rafique, Qixin Wang (The Hong Kong Polytechnic University, Hong Kong) |
| Page | pp. 429 - 434 |
| Keyword | Battery System Management Architectures (BSMAs), Lithium-Ion Batteries, Smart Cells, Battery Management, Reconfigurability |
| Abstract | This paper presents an overview of recent trends
in Battery System Management Architectures (BSMAs). After
introducing the main characteristics of large battery packs, the
state of the art in BSMAs is discussed. Two emerging concepts
are in the focus of this contribution. On the one hand, there is a
development from centralized battery management architectures
with a single control entity towards decentralized management
where the computational resources are distributed across the
battery pack and, hence, move closer to the individual battery
cells. This enables a more scalable and modular battery system
architecture, while, at the same time, posing challenges regarding
hardware and management algorithm design. On the other hand,
the static setup of the series- and parallel-connected cells forming
the battery pack may be developed towards a reconfigurable
architecture such that the electrical topology of the pack can
be adaptively changed. Such reconfigurability could increase the
reliability of battery packs and reduce management efforts such
as cell balancing. At the same time, limited energy efficiency
of the additional hardware poses a challenge. We give an
outlook how these two trends could be combined into distributed
reconfigurable BSMAs. This introduces a set of challenges which
have to be solved in order to benefit from the increased scalability,
reliability and safety such designs could offer. |
| Title | (Invited Paper) Minimum-Energy Driving Speed Profiles for Low-Speed Electric Vehicles |
| Author | Donkyu Baek, Joonki Hong, *Naehyuck Chang (KAIST, Republic of Korea) |
| Page | p. 435 |
| Keyword | Driving optimization, Electric vehicles, Speed profile |
| Abstract | Electric vehicles (EV) are rapidly invading the previous internal combustion engine vehicle (ICEV) market introducing not only environmental friendliness and a higher efficiency but a better ride quality, comfortness and performance. However, there still remain factors that the EV cannot reach the territory of ICEV such as a limited fully charged driving range per vehicle cost due to a low energy density of batteries compared with petroleum fuel. We formulate an optimization problem that minimizes the total energy consumption for a given route that consists of arbitrary slope variations. |