Fuzzy-based Temperature and Humidity Control for HV AC of Electric Vehicle

Ibrahim, Badaruddin; Aziah, M.A.N.; Ahmad, Salmiah; Akmeliawati, Rini; Nizam, Hairul; Muthalif, Asan G. A.; Toha, Siti Fauziah; Hassan, Mohd Khair

doi:10.1016/j.proeng.2012.07.261

Cited by 45 publications

(28 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The HVAC in modern vehicles mainly uses the Variable Air Volume (VAV) system [10]. The advantage of this system is the precise control of the temperature and humidity in multi-zone or single-zone with lower energy consumption [10].…”

Section: Hvac Modelmentioning

confidence: 99%

“…The advantage of this system is the precise control of the temperature and humidity in multi-zone or single-zone with lower energy consumption [10]. The HVAC structure [8] [9] in an EV is depicted in Fig.…”

Section: Hvac Modelmentioning

confidence: 99%

“…We compare our battery lifetime-aware automotive climate control methodology with 1) switching On/Off methodology [8] [9] and 2) fuzzy-based methodology [10]. As our methodology is dependent on the drive profile, for fairness of the comparisons, all methodologies have been applied for the same system model, drive profile, and comfort zone.…”

Section: B Comparison To State-of-the-artmentioning

confidence: 99%

“…Fuzzy-based [10] On/Off [8,9] On the other hand, as shown in Fig. 6, our methodology demonstrates different temperature management; it reduces the HVAC consumption when the electrical motor is consuming high power and it would precool the cabin (in this case outside is warmer) before the electrical motor consumption gets higher.…”

Section: Our Battery Lifetime-awarementioning

confidence: 99%

“…While, up until now, the power consumption of the Heating, Ventilation, and Air Conditioning (HVAC) has been assumed to be constant. On the other hand, controlling the HVAC (automotive climate control) is mostly done using switching On/Off the system or fuzzy-based methodologies implemented on Proportional-Integral-Derivative (PID) controllers [8][9] [10]. These methodologies typically try to stabilize the temperature and humidity inside the cabin within the comfort zone [11] without considering the battery lifetime.…”

Section: Introduction and Related Workmentioning

confidence: 99%

See 4 more Smart Citations

Battery lifetime-aware automotive climate control for electric vehicles

Vatanparvar

Faruque

2015

Proceedings of the 52nd Annual Design Automation Conference

View full text Add to dashboard Cite

Electric Vehicle (EV) optimization involves stringent constraints on driving range and battery lifetime. Sophisticated embedded systems and huge number of computing resources have enabled researchers to implement advanced Battery Management Systems (BMS) for optimizing the driving range and battery lifetime. However, the Heating, Ventilation, and Air Conditioning (HVAC) control and BMS have not been considered together in this optimization. This paper presents a novel automotive climate control methodology that manages the HVAC power consumption to improve the battery lifetime and driving range. Our experiments demonstrate that the HVAC consumption is considerable and flexible in an EV which significantly influences the driving range and battery lifetime. Hence, this influence on the above-mentioned constraints has been modeled and analyzed precisely, then it has been considered thoroughly in the EV optimization process. Our methodology provides significant improvement in battery lifetime (on average 14%) and average power consumption (on average 39% reduction) compared to the stateof-the-art methodologies. I. INTRODUCTION AND RELATED WORKElectric Vehicles (EVs) have been accepted as sustainable solution and a new paradigm of transportation [1] to address the environmental issues caused by greenhouse gases and other pollutants coming from road transportation [2]. Despite the incentives provided by governments to promote EV deployment [3], EVs pose new challenges in the trade-off between costs and performance [4]. The driving range and battery lifetime are the challenges that have become major design objectives for EVs. The cost, volume, and weight constraints in battery pack design make them the major bottleneck restricting the amount of energy stored for driving [5]. On the other hand, the battery lifetime is directly related to the State-of-Health (SoH) which represents the battery capability to store and deliver energy. The SoH degrades over time according to the battery usage pattern and the battery will become useless when it degrades for about 20% [6]. In order to alleviate the driving range and battery lifetime issue, a Battery Management System (BMS) is typically implemented to monitor and control the battery cells [1]. The BMS prevents overcharging, overdischarging, overheating, and imbalance of battery cells to improve their energy efficiency and lifetime. By presenting Hybrid Energy Storage System (HESS) [3] that may consist of ultracapacitors accompanied with battery cells, the BMS evolved to handle the charge management for heterogeneous energy storage to improve energy efficiency and battery lifetime. Other components inside EV, e.g. power converters, inverters, electrical motor, etc. demonstrate different efficiency in various conditions. Hence, the BMS may optimize the battery or HESS usage based on the components' efficiency map. Also, [3] [7] have illustrated that the BMS may predict and optimize the energy consumption more efficiently by having the route information. In the process of optimizin...

show abstract

Section: Hvac Modelmentioning

confidence: 99%

Section: Hvac Modelmentioning

confidence: 99%

Section: B Comparison To State-of-the-artmentioning

confidence: 99%

Section: Our Battery Lifetime-awarementioning

confidence: 99%

Section: Introduction and Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Battery lifetime-aware automotive climate control for electric vehicles

Vatanparvar

Faruque

2015

Proceedings of the 52nd Annual Design Automation Conference

View full text Add to dashboard Cite

show abstract

Energy‐efficient cabin climate control of electric vehicles using linear time‐varying model predictive control

Chen

Kwak

Kim

et al. 2021

Optim Control Appl Methods

View full text Add to dashboard Cite

A cabin climate control system, often referred to as a heating, ventilation, and air conditioning (HVAC) system, is one of the largest auxiliary loads of an electric vehicle (EV), and the real-time optimal control of HVAC brings a significant energy-saving potential. In this article, a linear-time-varying (LTV) model predictive control (MPC)-based approach is presented for energy-efficient cabin climate control of EVs. A modification is made to the cost function in the considered MPC problem to simplify the Hessian matrix in utilizing quadratic programming for real-time computation. A rigorous parametric study is conducted to determine optimal weighting factors that work robustly under various operating conditions. Then, the performance of the proposed LTV-MPC controller is compared against a rule-based (RB) controller and a nonlinear economic MPC (NEMPC) benchmark. Compared with the RB controller benchmark, the LTV-MPC reaches the target cabin temperature at least 69 s faster with 3.2% to 15% less HVAC system energy consumption, and the averaged cabin temperature difference is 0.7 • C at most. Compared with the NEMPC, the LTV-MPC controller can achieve comparable performance in temperature regulation and energy consumption with fast computation time: the maximum differences in temperature and energy consumption are 0.4 • C and 2.6%, respectively, and the computational time is reduced 72.4% on average with the LTV-MPC.

show abstract