A Low Cost, Portable Engine Electronic Control Unit Hardware-in-the-Loop Test System

Cebi, A.; Güvenç, Levent; Demirci, Murat; Karadeniz, C.K.; Kanar, Korhan; Guraslan, E.

doi:10.1109/isie.2005.1528927

Cited by 23 publications

(10 citation statements)

References 6 publications

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“…In Figures 7a through 7e, the key ROM parameters (R e The reason is that b 1 [k] and b 2 [k] are estimated recursively from initial guesses, and it may take several iterations to settle these parameter values. As shown in Figure 7, the profiles for a specific parameter vary dramatically in different operation cases.…”

Section: Resultsmentioning

confidence: 99%

“…In this work, the variable φ ref S is split into three terms: the equilibrium voltage U eq , the electrical over voltage V e , and the diffusional over voltage V d : [2] and therefore V P2D is given by:…”

Section: Mathematical Modelmentioning

confidence: 99%

“…Substitute Equations B11 into B7, and the individual parameters b 1 [k] and b 2 [k] can be calculated using the following equations:…”

Section: Physics Equationsmentioning

confidence: 99%

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An Adaptive Reduced-Order-Modeling Approach for Simulating Real-Time Performances of Li-Ion Battery Systems

Jin

White

2017

J. Electrochem. Soc.

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In this work, a reduced-order-modeling(ROM) approach is introduced to simulate a battery system with multiple cells. The ROM is described by only two state variable equations and several explicit expressions. While running a stand-alone pseudo-2D (P2D) model by the nonlinear state variable modeling (NSVM) algorithm at a specified current, several parameters can be estimated through the NSVM procedure; and with these parameters, the cell voltage under additional current perturbations can be computed approximately by the ROM. This modeling scheme was tested for a 24P battery module in a co-simulation process: the stand-alone P2D model is solved at the battery-average current to estimate certain parameters to share with its ROM, then the ROM is implemented to evaluate the voltage response of each cell at its respective cell current. The ROM shows excellent accuracy and a much higher speed than the full-order-model with P2D models used for each cell. The battery management system (BMS) plays a crucial role in electrical vehicles (EV). During a standard vehicle development cycle, in-loop simulation is a useful technique to test and verify complex real-time embedded systems.1-3 To conduct in-loop simulation, the embedded system to be tested is connected to a mathematical representation of all related dynamic systems, because this arrangement lowers cost and improves safety as compared with using the real system. The mathematical representation interacting with embedded system is referred as "plant simulation". For the BMS software development, the plant simulation is performed by system-level battery models while the corresponding embedded system includes certain battery control and management algorithms. Therefore an accurate plant model for the battery system is vital for a successful BMS design. Besides accuracy, the plant battery model is also expected to work under high frequency inputs with noise and generate real-time output signals at low computational costs.The pseudo-2D (P2D) model developed by Fueler et al. 4 is a physical model that can be used to provide reliable predictions for the transport and electrochemical phenomena in Li-ion cells. However, it is still difficult to use the P2D model in plant simulation. The first challenge is the numerical issues in solving the nonlinear partialdifferential-equations (PDEs) contained in the P2D model at highfrequency inputs. During practical driving modes, the power requests from the powertrain to the EV battery change very quickly with time, and the currents and voltages are processed as discrete-time electrical signals. Most multi-physics PDE solvers (e.g., COMSOL) are designed to model continuous-time processes (e.g., constant current) and are slow for high-frequency simulation cases. Another challenge is the high computational costs to scale-up the P2D model from celllevel to pack-level. A single-cell P2D model normally includes more than 100 discretized state variables even using a very coarse mesh. To meet the high power and energy demands, typical EV battery...

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Section: Resultsmentioning

confidence: 99%

Section: Mathematical Modelmentioning

confidence: 99%

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An Adaptive Reduced-Order-Modeling Approach for Simulating Real-Time Performances of Li-Ion Battery Systems

Jin

White

2017

J. Electrochem. Soc.

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“…The engine ECU is the most vehicles is contacted to onboard diagnostic connector and then report all diagnostic information to all other ECUs. This technique is helping in reducing the amount of wire that is needed or not needed to go to every ECU in order to test them [15]. Tuohy et al [16] stated in their analysis research that intra-vehicle networks demonstrate that each electronic sensor in a vehicle requires a new ECU device and subsystem and calls for standardization of automotive networks in intra-vehicular networks.…”

Section: Can Busmentioning

confidence: 99%

Classification Approach for Intrusion Detection in Vehicle Systems

Alshammari¹,

Zohdy²,

Debnath³

et al. 2018

WET

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Vehicular ad hoc networks (VANETs) enable wireless communication among Vehicles and Infrastructures. Connected vehicles are promising in Intelligent Transportation Systems (ITSs) and smart cities. The main objective of VANET is to improve the safety, comfort, driving efficiency and waiting time on the road. VANET is unlike other ad hoc networks due to its unique characteristics and high mobility. However, it is vulnerable to various security attacks due to the lack of centralized infrastructure. This is a serious threat to the safety of road traffic. The Controller Area Network (CAN) is a bus communication protocol which defines a standard for reliable and efficient transmission between in-vehicle parts simultaneously. The message moves through CAN bus from one node to another node, but it does not have information about the source and destination address for authentication. Thus, the attacker can easily inject any message to lead to system faults. In this paper, we present machine learning techniques to cluster and classify the intrusions in VANET by KNN and SVM algorithms. The intrusion detection technique relies on the analysis of the offset ratio and time interval between the messages request and the response in the CAN.

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“…Scope of this testing is to evaluate interactions among parts of the distributed system. HIL testing method is a key concept in this stage [3]. Sometimes it is not possible to test complete cluster of ECUs but, for this approach it is necessary to have available an environment of the rest of a car (simulation or real components).…”

Section: Current Practisementioning

confidence: 99%