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It is founded on DataBases (DB), Expert Systems (ES) created by heuristic logical programming methods using an artificial intelligence approach. There are also Hybrid Expert Systems (HES) which have "pure" Expert System parts and calculation modules (developed by traditional methods of software creating).

Abstract Electric vehicles (EVs) with enormous batteries are capable of storing a high quantity of energy. This stored energy can be used as a standby power supply or as backup energy storage for domestic loads whenever a blackout or load shedding happens. Further, solar photovoltaic (PV) energy can also be utilized to charge the battery of EV and also improve the backup energy source for domestic loads. The main purposes of this article are to reduce the energy cost of a household and also to minimize the dependency of domestic loads on the grid, to enhance the reliability of power supply to the residential loads during load shedding and blackouts, and to maximize the utilization of power produced by the solar photovoltaic array (SPVA) mounted on the rooftop. Moreover, Alternating Current (AC) loads are connected to the AC bus, and Direct Current (DC) loads are connected separately to the DC bus to avoid power losses in conversion (DC to AC and AC to DC).

The consequent means towards improved enhancement of the safety of commercial vehicles will in future times require more and more electronic intelligence, in case a distinct optimization of the systems will not be possible with conventional means.

The proposed beam element method, with a significant reduction of model size and computation time, is capable of extracting essential safety dynamic characteristics. An artificial neural network is employed and the recurrent back-propagation learning rule trains the network to obtain optimized beam element features.

For the decision difficulty in roundabout scenarios, this paper proposes an artificial potential field based Soft Actor-Critic (APF-SAC) algorithm. Firstly, based on the Carla simulator and Gym framework, a reinforcement learning simulation system for roundabout driving is built. ...Then, considering the complex interactions between vehicles in roundabouts, a Markov decision process model is constructed, and a coupled longitudinal and lateral action space, a vectorized state space based on roundabout scenarios, and a reward function based on artificial potential field are designed, and the APF-SAC algorithm is proposed. Finally, simulation experiments under different traffic densities show that compared to rule-based driving decisions, the deep reinforcement learning method can significantly improve decision safety and driving efficiency in roundabout scenarios, with the maximum safety improvement of 10.4% and the maximum driving efficiency improvement of 13.2%, demonstrating the superior performance of the APF-SAC algorithm for roundabout driving decisions.

This study focuses on analyzing the path planning problem for intelligent vehicles in complex terrains by utilizing the optimization evaluation function of the artificial bee colony (ABC) algorithm. Additionally, the impact of turning radius at different speeds is considered during the planning process.

To optimize the flow performance of intake port, a new optimization method combining genetic algorithm (GA) and artificial neural network (ANN) was proposed. First, an automatic system for generating the geometry of the tangential intake port was constructed to create various port geometries through inputting the 18 pre-defined structural parameters.

Hence, the Artificial Neural Networks (ANN) are trained with high-fidelity simulation results and used as surrogate models, representing engine's response to different control variable combinations with greatly reduced computational time.

This paper presents the details of the study to optimize and arrive at a design base for a vacuum pump in an automotive engine using resilient back propagation algorithm for Artificial Neural Networking (ANN). The reason for using neural networks is to capture the accuracy of experimental data while saving computational time, so that system simulations can be performed within a reasonable time frame.

By using an artificial immune algorithm to conduct optimizing calculation, the optimal result of the suspension parameters were obtained.

The main aim of this study is to create a mathematical model, and use Artificial Neural Networking (ANN) to arrive at a design base. In the process of building the mathematical model, two key design parameters namely profile and performance were identified and model was constructed.

It is an initial attempt to integrate artificial intelligence techniques (via expert systems) with conventional quantitative modeling tools for advanced physical-chemical life support systems. ...The addition of artificial intelligence techniques will assist the designer in the definition and simulation of loosely/well-defined life support processes/problems as well as assist in the capture of design knowledge, both quantitative and qualitative.

In order to doing so, we propose an intelligent approach for controlling this managing system using of artificial and computational intelligence techniques such as Bayesian Nets, Neural networks and Genetic Algorithms.

Intelligent driving is an important research direction in the field of artificial intelligence. The fourth industrial revolution represented by the Internet of things provides more prospects for the development of intelligent vehicles.

The applications of Artificial Intelligence (AI) in the product design software, and the advancements in the Direct Digital Manufacturing (DDM) such as Additive Manufacturing have been leading the aerospace and automobile industries into the next level.

To facilitate design and analysis of these machines, an Al (artificial intelligence) and symbolic manipulation based software package is being developed in MATHEMATICA and used at JI CASE.

For the numerical analysis, the fluid flow at car speeds of 70, 90 and 110 km/h were examined. The Artificial Intelligence coding was applied to train an Artificial Neural Network to extend the calculated data.

It was only a vision at the time, but the introduction of Artificial Intelligence (AI) in manufacturing has since enabled it to become attainable. Since most mature manufacturing organizations have merged traditional quality philosophies and techniques, their processes generate only a few Defects Per Million of Opportunities (DPMO).

A new design approach, PD-free and reliability redundant, is briefly discussed, highlighting the importance to develop an innovative PD detection and analytics approach where measurements and interpretation are fully automatic and unsupervised, and experts are replaced by artificial intelligence. The aim is to remove the adverb “potentially” applied to the usefulness of PD detection and monitoring.

In this study, we explored the application of artificial intelligence to optimize the NVH performance of motors during the design phase. Firstly, we selected and modeled three benchmark motor types using Motor-CAD.