Advances in Li-Ion battery management for electric vehicles

This paper aims at presenting new solutions for advanced Li-Ion battery management to meet the performance, cost and safety requirements of automotive applications. Emphasis is given to monitoring and controlling the battery temperature, a parameter which dramatically affects the performance, lifetime, and safety of Li-Ion batteries. In addition...

Enhanced Battery Thermal Management Systems with Optimal Charge Control for Electric Vehicles

Predictive battery thermal management strategy for fast charging electric vehicles - Type the subtitle of the paper

Battery thermal management is essential to efficienct and safe operation of Li-ion batteries, particularly in challenging applications such as fast charging. This work proposes a thermal management control framework based on MPC. The novelty of this approach resides in the focus on incorporating disturbance prediction into the control decision....

Battery pack state estimation algorithms – Model based development and estimation

Model based development methodologies have strong potential in reducing development effort of complex automotive systems. This paper presents a model based development methodology used to develop battery state estimation algorithms, and steps through all the steps required for this. The benefits of the mixed simulation and testing methodology...

Model identification for thermal modelling of a battery pack

Optimised battery capacity utilisation within battery management systems

Battery Management Systems (BMSs) play a key role in the performance of both hybrid and fully electric vehicles. Typically, the role of the BMS is to help maintain safety, performance, and overall efficiency of the battery pack. One important aspect of its operation is the estimation of the state of the cells, to support functionalities such as...

Predictive Model Based Battery Constraints for Electric Motor Control within EV Powertrains

This paper presents a method of predicting the maximum power capability of a Li-Ion battery, to be used for electric motor control within automotive powertrains. As maximum power is highly dependent on battery state, the method consists of a pack level state observer coupled with a predictive battery model. Results indicate that the battery...

Enhanced battery model including temperature effects

Within electric and hybrid vehicles, batteries are used to provide/buffer the energy required for driving. However, battery performance varies throughout the temperature range specific to automotive applications, and as such, models that describe this behaviour are required. This paper presents a dynamic battery model describing the battery...

Model-based eco-driving and integrated powertrain control for (hybrid) electric vehicles

The Netherlands Organisation for Applied Scientific Research (TNO) is engaged in research, development and testing of a range of technologies relating to hybrid and electric vehicle energy management and performance. The impact of driver behaviour on vehicle energy consumption is a significant factor, and one which can often be reduced with eco...

On-line parameter, state-of-charge and aging estimation of Li-ion batteries

This paper presents an on-line model identification method for Li-ion battery parameters that combines high accuracy and low computational complexity. Experimental results show that modeling errors are smaller than 1% throughout the feasible operating range. The identified model is used in a state observer - an Extended Kalman Filter (EKF) - to...

On the sensitivity of the state of batteries on driver behaviour within the EV powertrain

Driveability of BEVs is subject to the driver behaviour and in more extreme cases the powertrain is unable to adapt. The behaviour of drivers is categorized and presented here with comparison against more standard drive cycles. The extremes of the driver behaviour and this distribution is based on real-world measurements. A driver grading...

On-line battery identification for electric driving range prediction

Hybrid and electric vehicles require accurate knowledge of the battery to make an educated guess about the expected electric driving range. Range prediction is complicated by the fact that batteries are subject to external influences and aging. Also the future driving behavior is often unknown. This paper presents a model-based identification...