Biblio
Recent changes to greenhouse gas emission policies are catalyzing the electric vehicle (EV) market making it readily accessible to consumers. While there are challenges that arise with dense deployment of EVs, one of the major future concerns is cyber security threat. In this paper, cyber security threats in the form of tampering with EV battery's State of Charge (SOC) was explored. A Back Propagation (BP) Neural Network (NN) was trained and tested based on experimental data to estimate SOC of battery under normal operation and cyber-attack scenarios. NeuralWare software was used to run scenarios. Different statistic metrics of the predicted values were compared against the actual values of the specific battery tested to measure the stability and accuracy of the proposed BP network under different operating conditions. The results showed that BP NN was able to capture and detect the false entries due to a cyber-attack on its network.
Cyber-Physical Systems (CPS) are growing with added complexity and functionality. Multidisciplinary interactions with physical systems are the major keys to CPS. However, sensors, actuators, controllers, and wireless communications are prone to attacks that compromise the system. Machine learning models have been utilized in controllers of automotive to learn, estimate, and provide the required intelligence in the control process. However, their estimation is also vulnerable to the attacks from physical or cyber domains. They have shown unreliable predictions against unknown biases resulted from the modeling. In this paper, we propose a novel control design using conditional generative adversarial networks that will enable a self-secured controller to capture the normal behavior of the control loop and the physical system, detect the anomaly, and recover from them. We experimented our novel control design on a self-secured BMS by driving a Nissan Leaf S on standard driving cycles while under various attacks. The performance of the design has been compared to the state-of-the-art; the self-secured BMS could detect the attacks with 83% accuracy and the recovery estimation error of 21% on average, which have improved by 28% and 8%, respectively.
The use of green energy is becoming increasingly more important in today's world. Therefore, the use of electric vehicles (EVs) is proving to be the best choice for the environment in terms of public and personal transportation. As the electric vehicles are battery powered, their management becomes very important because using batteries beyond their safe operating area can be dangerous for the entire vehicle and the person onboard. To maintain the safety and reliability of the battery, it is necessary to implement the functionalities of continuous cell monitoring and evaluation, charge control and cell balancing in battery management systems (BMS). This paper presents the development of platform software required for the implementation of these functionalities. This platform is based on a digital signal processing platform which is a master-slave structure. Serial communication technology is adopted between master and slave. This system allows easier controllability and expandability.
Smart Grid (SG) technology has been developing for years, which facilitates users with portable access to power through being applied in numerous application scenarios, one of which is the electric vehicle charging. In order to ensure the security of the charging process, users need authenticating with the smart meter for the subsequent communication. Although there are many researches in this field, few of which have endeavored to protect the anonymity and the untraceability of users during the authentication. Further, some studies consider the problem of user anonymity, but they are non-light-weight protocols, even some can not assure any fairness in key agreement. In this paper, we first points out that existing authentication schemes for Smart Grid are neither lack of critical security nor short of important property such as untraceability, then we propose a new two-factor lightweight user authentication scheme based on password and biometric. The authentication process of the proposed scheme includes four message exchanges among the user mobile, smart meter and the cloud server, and then a security one-time session key is generated for the followed communication process. Moreover, the scheme has some new features, such as the protection of the user's anonymity and untraceability. Security analysis shows that our proposed scheme can resist various well-known attacks and the performance analysis shows that compared to other three schemes, our scheme is more lightweight, secure and efficient.
The inevitable temperature raise leads to the demagnetization of permanent magnet synchronous motor (PMSM), that is undesirable in the application of electrical vehicle. This paper presents a nonlinear demagnetization model taking into account temperature with the Wiener structure and neural network characteristics. The remanence and intrinsic coercivity are chosen as intermediate variables, thus the relationship between motor temperature and maximal permanent magnet flux is described by the proposed neural Wiener model. Simulation and experimental results demonstrate the precision of temperature dependent demagnetization model. This work makes the basis of temperature compensation for the output torque from PMSM.