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Authors were challenged with a task of developing a full vehicle simulation model, with a target to simulate the electrical system performance and perform digital tests like Battery Charge Balance, in addition to the fuel efficiency estimation. A vehicle is a complicated problem or domain to model, due to the complexities of subsystems. Even more difficult task is to have a control algorithm which controls the vehicle model with the required control signals to follow the test specification. Particularly, simulating the control of a vehicle with a manual transmission is complicated due to many associated control signals (Throttle, Brake and Clutch) and interruptions like gear changes. In this paper, the development of a full vehicle model aimed at the assessment of electrical system performance of the vehicle is discussed in brief.

Micro Hybrid Systems are a premier approach for improving fuel efficiency and reducing emissions, by improving the efficiency of electrical energy generation, storage, distribution and consumption, yet with lower costs associated with development and implementation. However, significant efforts are required while implementing micro hybrid systems, arising out of components like Intelligent Battery Sensor (IBS). IBS provides battery measurements and battery status, and in addition mission critical diagnostic data on a communication line to micro hybrid controller. However, this set of data from IBS is not available instantly after its initialization, as it enters into a lengthy learning phase, where it learns the battery parameters, before it gives the required data on the communication line. This learning period spans from 3 to 8 hours, until the IBS is fully functional and is capable of supporting the system functionalities.

Battery Thermal management is a major challenge for occupant safety in an electric vehicle. Predicting the battery electrical losses and thermal behaviour is another challenge for the battery management system. Different virtual models are developed for cell level and pack level thermal evaluation. All these models have a varying degree of accuracy and limitation. The latest developed model is more accurate and can predict the battery cell & pack level temperatures. The battery can be modeled in different ways, ECM (Electrochemical model), EIS (Electrochemical Impedance Spectroscopy) [1]. Newman model is a well-known electrochemical model. [2]. EIS uses a combination of DC and small AC signal [3,4]. ECM model also used for estimating SOC and in BMS [5]. The cell temperature in the battery pack not only depends upon the cell inside physics but also depends upon cell outside cooling physics. Cell outside physics is simulated by 3D CFD software during the design process [6].

With the current state of ever rising fuel prices and unavailability of affordable alternate technologies, significant research and development efforts have been invested in recent times towards improving fuel efficiency of vehicles powered with conventional internal combustion engines. To achieve this, a varied approach has been adopted by researchers to cover the entire energy chain including fuel quality, combustion quality, power generation efficiency, down-sizing, power consumption efficiency, etc. Apart from energy generation, distribution and consumption, another domain that has been subjected to significant scrutiny is energy recuperation or recovery. A moving vehicle and a running engine provide a number of opportunities for useful back-recovery and storage of energy. The most significant sources for recuperation are the kinetic energy of the moving vehicle or running engine and to a lesser extent the thermal energy from medium such as exhaust gas.

This paper describes the Common Automobile Program (CAP) that can be implemented to improve mass transportation. CAP is the use of automated electric vehicles using smart navigation and control technologies to improve mass transportation. In CAP, common vehicles are used by different passengers, thus, reducing the on-road traffic and also the parking space required. Various low-cost stations are to be built along specified paths and the vehicle can be used at the convenience of the commuter. Currently, buses and trains require the passengers to wait at the station and a significant amount of time is spent at intermediate stops. The vehicle in CAP runs directly from origin to destination and also eliminates the waiting time at stations. Passengers do not wait for vehicles; instead vehicles wait for the passengers. The journey starts as the passenger enters the station and selects the destination.

A number of market factors such as customer demand for improved connectivity and infotainment systems, automated driver assist systems and electrification of powertrain have driven an increase in the number of electrical systems within the cabin of automotive vehicles. These systems have limited operating temperature windows, therefore markets with high ambient temperatures and solar loading represent a significant challenge due to high cabin temperatures. Traditionally climatic facilities have been used replicate the conditions seen in these markets in order to understand the performance of the electrical systems. However such facilities have a number of limitations such as fixed solar arrays, secondary radiation from the walls and substantial operating costs limiting testing to envelope tests. Therefore the requirement for CAE based approach to more accurately represent the conditions seen in the real world is clear.

In a Mild hybrid electric vehicle, a battery serves as a continuous source of energy but is inefficient in supplying peak power demands required during torque assists for short duration. Moreover, the random charging and discharging that result due to varying drive cycle of the vehicle affects the life of the battery. In this paper, an Ultra-capacitor based hybrid energy storage system (HESS) has been developed for mild hybrid vehicle which aims at utilizing the advantages of ultracapacitors by combining them with lead-acid batteries, to improve the overall performance of the battery, and to increase their useful life. Active current-sharing is achieved by interfacing ultracapacitor to the battery through a bi-directional boost dc-dc converter.