Urban and extra-urban hybrid vehicles: a technological review

Pollution derived from transportation systems is a worldwide, timelier issue than ever. The abatement actions of harmful substances in the air are on the agenda and they are necessary today to safeguard our welfare and that of the planet. Environmental pollution in large cities is approximately 20% due to the transportation system. In addition, private traffic contributes greatly to city pollution. Further, “vehicle operating life” is most often exceeded and vehicle emissions do not comply with European antipollution standards. It becomes mandatory to find a solution that respects the environment and, realize an appropriate transportation service to the customers. New technologies related to hybrid –electric engines are making great strides in reducing emissions, and the funds allocated by public authorities should be addressed. In addition, the use (implementation) of new technologies is also convenient from an economic point of view. In fact, by implementing the use of hybrid vehicles, fuel consumption can be reduced. The different hybrid configurations presented refer to such a series architecture, developed by the researchers and Research and Development groups. Regarding energy flows, different strategy logic or vehicle management units have been illustrated. Various configurations and vehicles were studied by simulating different driving cycles, both European approval and homologation and customer ones (typically municipal and university). The simulations have provided guidance on the optimal proposed configuration and information on the component to be used

Evolution engine technology in exhaust gas recirculation for heavy-duty diesel engine

In this present year, engineers have been researching and inventing to get the optimum of less emission in every vehicle for a better environmental friendly. Diesel engines are known reusing of the exhaust gas in order to reduce the exhaust emissions such as NOx that contribute high factors in the pollution. In this paper, we have conducted a study that EGR instalment in the vehicle can be good as it helps to prevent highly amount of toxic gas formation, which NOx level can be lowered. But applying the EGR it can lead to more cooling and more space which will affect in terms of the costing. Throughout the research, fuelling in the engine affects the EGR producing less emission. Other than that, it contributes to the less of performance efficiency when vehicle load is less

Multi objective optimization in charge management of micro grid based multistory carpark

Distributed power supply with the use of renewable energy sources and intelligent energy flow management has undoubtedly become one of the pressing trends in modern power engineering, which also inspired researchers from other fields to contribute to the topic. There are several kinds of micro grid platforms, each facing its own challenges and thus making the problem purely multi objective. In this paper, an evolutionary driven algorithm is applied and evaluated on a real platform represented by a private multistory carpark equipped with photovoltaic solar panels and several battery packs. The algorithm works as a core of an adaptive charge management system based on predicted conditions represented by estimated electric load and production in the future hours. The outcome of the paper is a comparison of the optimized and unoptimized charge management on three different battery setups proving that optimization may often outperform a battery setup with larger capacity in several criteria.Web of Science117art. no. 179

Accuracy versus simplicity in online battery model identification

This paper presents a framework for battery modeling in online, real-time applications where accuracy is important but speed is the key. The framework allows users to select model structures with the smallest number of parameters that is consistent with the accuracy requirements of the target application. The tradeoff between accuracy and speed in a battery model identification process is explored using different model structures and parameter-fitting algorithms. Pareto optimal sets are obtained, allowing a designer to select an appropriate compromise between accuracy and speed. In order to get a clearer understanding of the battery model identification problem, “identification surfaces” are presented. As an outcome of the battery identification surfaces, a new analytical solution is derived for battery model identification using a closed-form formula to obtain a battery’s ohmic resistance and open circuit voltage from measurement data. This analytical solution is used as a benchmark for comparison of other fitting algorithms and it is also used in its own right in a practical scenario for state-of-charge estimation. A simulation study is performed to demonstrate the effectiveness of the proposed framework and the simulation results are verified by conducting experimental tests on a small NiMH battery pack

On-Line Optimal Charging Coordination of Plug-In Electric Vehicles in Smart Grid Environment

This PhD research proposes a new objective function for optimal on-line PEV coordination. A new enhanced on-line coordinated charging using coordinated aggregated particle swarm particle optimization (OLCC-CAPSO) has been used to solve the PEV coordination objective objection and associated constraints. The objective function provides a chance for all PEVs to start charging as quickly as possible, while customer satisfaction function is being optimized subject to network criteria including voltage profiles, generator and distribution transformer ratings

Optimization for Integration of Plug-in Hybrid Electric Vehicles into Distribution Grid

Plug-in hybrid electric vehicles (PHEVs) feature combined electric and gasoline powertrains with internal combustion engine and electric motors powered by battery packs. The battery packs of PHEVs are mostly charged during off-peaks hours at lower prices and meanwhile absorb the excess power from the grid. Similarly, the power stored in the batteries can also flow back to the electric grid in response to ease the peak load demands. With the increasing penetration and integration of PHEVs, the reliability of PHEVs is essential to overall power system reliability since the charging mechanisms of PHEVs can influence the reliability of power system. Furthermore, due to the direct integration of PHEVs into the residential distribution network, the PHEVs can work as backup batteries for power systems in case of power outage. Therefore, the reliability analysis of power systems and the ancillary services provided by PHEVs are also proposed in this thesis study. For the driving pattern of each PHEV, there are three basic elements modeled, which are the departure time, the arrival time and the daily mileage, all represented by probability density functions. Based on these basic concepts, the methodology for modeling the load demand of PHEVs is introduced. In the proposed system, both the Differential Evolution and the Particle Swarm Optimization are proposed to optimize the control strategies for power systems with integration of PHEVs. Aside from using the minimum cost as a figure of merit when designing and determining the best PHEV charging mechanism, the reliability improvement brought to the power systems by PHEVs and the extra earnings obtained by performing frequency regulation services are also quantified and taken into account. Although the reliability of power systems with PHEV penetrations has been well-studied, the adoption of the Differential Evolution algorithm for minimizing the cost of overall system is not exercised, not to mention a thorough comparative study with other common optimization algorithms. To sum up, the Differential Evolution can not only achieve multiple goals by improving the power quality, reducing the peak load, providing regulation services and minimizing the total virtual cost in this system, it can also offer better results compared with the Particle Swarm Optimization in terms of minimizing the cost

Development Schemes of Electric Vehicle Charging Protocols and Implementation of Algorithms for Fast Charging under Dynamic Environments Leading towards Grid-to-Vehicle Integration

This thesis focuses on the development of electric vehicle (EV) charging protocols under a dynamic environment using artificial intelligence (AI), to achieve Vehicle-to-Grid (V2G) integration and promote automobile electrification. The proposed framework comprises three major complementary steps. Firstly, the DC fast charging scheme is developed under different ambient conditions such as temperature and relative humidity. Subsequently, the transient performance of the controller is improved while implementing the proposed DC fast charging scheme. Finally, various novel techno-economic scenarios and case studies are proposed to integrate EVs with the utility grid. The proposed novel scheme is composed of hierarchical stages; In the first stage, an investigation of the temperature or/and relative humidity impact on the charging process is implemented using the constant current-constant voltage (CC-CV) protocol. Where the relative humidity impact on the charging process was not investigated or mentioned in the literature survey. This was followed by the feedforward backpropagation neural network (FFBP-NN) classification algorithm supported by the statistical analysis of an instant charging current sample of only 10 seconds at any ambient condition. Then the FFBP-NN perfectly estimated the EV’s battery terminal voltage, charging current, and charging interval time with an error of 1% at the corresponding temperature and relative humidity. Then, a nonlinear identification model of the lithium-polymer ion battery dynamic behaviour is introduced based on the Hammerstein-Wiener (HW) model with an experimental error of 1.1876%. Compared with the CC-CV fast charging protocol, intelligent novel techniques based on the multistage charging current protocol (MSCC) are proposed using the Cuckoo optimization algorithm (COA). COA is applied to the Hierarchical technique (HT) and the Conditional random technique (CRT). Compared with the CC-CV charging protocol, an improvement in the charging efficiency of 8% and 14.1% was obtained by the HT and the CRT, respectively, in addition to a reduction in energy losses of 7.783% and 10.408% and a reduction in charging interval time of 18.1% and 22.45%, respectively. The stated charging protocols have been implemented throughout a smart charger. The charger comprises a DC-DC buck converter controlled by an artificial neural network predictive controller (NNPC), trained and supported by the long short-term memory neural network (LSTM). The LSTM network model was utilized in the offline forecasting of the PV output power, which was fed to the NNPC as the training data. The NNPC–LSTM controller was compared with the fuzzy logic (FL) and the conventional PID controllers and perfectly ensured that the optimum transient performance with a minimum battery terminal voltage ripple reached 1 mV with a very high-speed response of 1 ms in reaching the predetermined charging current stages. Finally, to alleviate the power demand pressure of the proposed EV charging framework on the utility grid, a novel smart techno-economic operation of an electric vehicle charging station (EVCS) in Egypt controlled by the aggregator is suggested based on a hierarchical model of multiple scenarios. The deterministic charging scheduling of the EVs is the upper stage of the model to balance the generated and consumed power of the station. Mixed-integer linear programming (MILP) is used to solve the first stage, where the EV charging peak demand value is reduced by 3.31% (4.5 kW). The second challenging stage is to maximize the EVCS profit whilst minimizing the EV charging tariff. In this stage, MILP and Markov Decision Process Reinforcement Learning (MDP-RL) resulted in an increase in EVCS revenue by 28.88% and 20.10%, respectively. Furthermore, the grid-to-vehicle (G2V) and vehicle-to-grid (V2G) technologies are applied to the stochastic EV parking across the day, controlled by the aggregator to alleviate the utility grid load demand. The aggregator determined the number of EVs that would participate in the electric power trade and sets the charging/discharging capacity level for each EV. The proposed model minimized the battery degradation cost while maximizing the revenue of the EV owner and minimizing the utility grid load demand based on the genetic algorithm (GA). The implemented procedure reduced the degradation cost by an average of 40.9256%, increased the EV SOC by 27%, and ensured an effective grid stabilization service by shaving the load demand to reach a predetermined grid average power across the day where the grid load demand decreased by 26.5% (371 kW)