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State of Charge (SoC) estimation of battery plays a key role in strategizing the power distribution across the vehicle in Battery Management System. In this paper, a model for SoC estimation using Extended Kalman Filter (EKF) is developed in Simulink. This model uses a 2nd order Resistance-Capacitance (2RC) Equivalent Circuit Model (ECM) of Lithium Ferrous Phosphate (LFP) cell to simulate the cell behaviour. This cell model was developed using the Simscape library in Simulink. The parameter identification experiments were performed on a new and a used LFP cell respectively, to identify two sets of parameters of ECM. The cell model parameters were identified for the range of 0% to 100% SoC at a constant temperature and it was observed that they vary as a function of SoC. Hence, variable resistance and capacitance blocks are used in the cell model so that the cell parameters can vary as a function of SoC.

The necessity of hybrid vehicles and electric vehicles is well known by now for reasons like fossil fuel depletion, climate change, emission norms mandated by regulations etc. With the addition of electric motor, battery and associated power electronics, the cost of powertrain and hence the vehicle goes up, which is often a hindrance for OEMs and end-customers. With the objective to make a cost-effective strong hybrid, Tata Motors has approached this problem by taking economies of scale approach i.e. developing an add-on gearbox module, which can fit a family of cars, in a P3-off axis hybrid configuration to an electrically actuated AMT. This paper presents the preliminary simulations, which show this architecture yielding a reduction of fuel consumption by ~20% for a B-class sedan/compact SUV of ~1800kg and ~15% improvement in acceleration performance. Additionally, the design and packaging studies show the fitment possibility in a highly congested powertrain bay.

Regular vehicle has the advantage of Engine resistance even when it is not fired, hence chances of vehicle roll back on gradients will be minimized. This is not the case for Electric vehicles, which uses an electric motor that does not have any resistance offered to wheels that prevent vehicle roll back on gradient. This leads to increased load on the conventional hydraulic brakes due to absence of engine inertia. Hence, there is a need for a low cost and reliable automatic braking system which can help in holding the vehicle and assists the driver during launch in case he need to stop at a gradient. An Electromagnetic brake (EM brake) system can be used as a solution for the above-mentioned requirement. EM brake can provide hill hold and hill assist effect in addition to automatic parking brake application when the vehicle is turned-off. This system will assist anyone who need to halt the vehicle at a gradient and then relaunch it without much struggle.

Rising environmental concerns and depleting natural resources have resulted in faster adoption of green technologies. These technologies are pushed by the government of states through certain schemes and policies as to make the orbit shift ensuring greener environment in near future. Major actions can be easily seen in transportation sector. Hybrid Electric Vehicle (EV), EV and Fuel cell EV are being deployed on roads rapidly but even though some challenges are still unsolved such as battery cost, fast charging and life cycle of the automotive battery. Automotive batteries (Lithium ions) are declared as unfit for automotive usage after the loss of 20% to 15% of their initial capacity. Still 80% to 85% of battery capacity can be utilized in stationary applications other than automotive. Stationary application doesn’t demand high current density or energy density from the battery pack as of automotive requirements.

Various vehicle manufacturers are launching electric vehicles, which are more sustainable and environmentally friendly. The major component in electric vehicles is the battery, and its performance plays a vital role. Usually, the end of life of a battery in the automobile sector is when the battery capacity reaches 80% of its maximum rated capacity. The capacity of a lithium-ion cell declines with the number of cycles. So, a semi-empirical model is developed for estimating the maximum stored capacity at the end of each cycle. The parameters considered in the model explain the changes in battery internal structure, like capacity losses at different conditions. The capacity estimated using the semi-empirical model is further taken as the inputs for estimating capacity using the Artificial Neural Network (ANN) and Machine Learning (ML) techniques i.e., Linear Regression (LR), Gaussian Process Regression (GPR), Support Vector Machine methods (SVM).