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Suspension tuning is a very crucial part in development stage of two-wheeler as it addresses two major attributes: Rider comfort and Vehicle Road holding capability. Various analyses like pitch, bounce, unsprung masses behavior, etc. can be done through simulation models before getting on to test vehicles for objective and subjective feel and tuning. Tire being a series element with suspension, plays a crucial part in the tuning because of is radial stiffness. Also, the size of tire matters as it envelops a surface and transmits a modified road profile to the axle of wheel, e.g. a very small pothole almost fully gets absorbed by tire itself and therefore hardly any movement is observed at the wheel axle. In this paper, an extension on half-car model is done which captures the enveloping behavior of the tire over the road surface, as enveloping behavior leads to change in suspension stroke values from what we observe from standard half-car model.

Electric Vehicles provide a sustainable transportation alternative for the future due to zero carbon emissions during running, higher efficiency, and lower operating cost along with various other benefits. One of the key challenges in the adoption of Electric Vehicles is to improve the range and reduce the anxiety associated with it, at the same time provide good drivability characteristics. A good tuning of riding mode is very crucial to make sure a good range is achieved without making any unwanted compromise on drivability. In this paper we try to elaborate on the methodology to be used to provide a good throttle response, for which it is very important to understand the torque requirement by the customer in different scenarios encountered during his/her day-to-day riding. For this purpose, various user drive cycles are imported in a MATLAB-Simulink model, where it was analyzed to provide the density map of the Torque Requirements v/s RPM for Motor to meet the ride pattern.

With the increase in number of vehicles and amount of traffic, safety has come out to be a big concern in vehicle’s dynamic stability. There are certain system’s limits beyond which if a vehicle is pushed it may become unstable. One of the major areas of research in vehicle dynamics control has been lateral velocity and yaw rate control. With this, situations like vehicle spinning, oversteer, understeer etc. can be addressed. The challenge for the next generations of vehicle control is the integration of the available actuators into a unique holistic control concept. This paper presents the driver reference generator developed for the Integrated Vehicle Dynamics Control concept. The driver reference generator processes the driver inputs to determine the target vehicle behavior. The generation of reference behavior is a key factor for the integrated control design. The driver reference generation is validated on a real vehicle.

Reducing the number of traffic accidents is a declared target of most governments leading to mandating Combined Braking System (CBS) or Anti-lock Braking System (ABS) in two wheelers. Traditional friction braking torque and motor braking torque can be used in braking for electric 2wheeler. Use of CBS and ABS helps in active control of vehicle braking leading to better deceleration, prevention of tire locking, control on vehicle pitch etc. A braking model (friction braking + motor regenerative braking) along with battery dynamics is developed in Matlab/Simulink and validated with real vehicle response. This paper presents an analysis on vehicle braking separately for heavy and mild braking in various vehicle load conditions. During heavy braking a feedback control algorithm is used to maintain optimal slip ratio both at the front and rear tire, active control on CBS and ABS+Regen is done and performance is compared.

Various current control algorithms are presented in this paper to prevent vehicle cut-off and increase the range of an electric 2 wheeler based on SoC, battery temperature and motor temperature. At lower SoCs if the current demand is very high there is a possibility of cell voltage hitting the lower threshold voltage leading to cut-off. An algorithm is proposed where current (maximum allowed) derating is done based on reducing SoC, battery voltage and real time throttle demand. Lithium ion cells operating temperature has an upper cap. Rate of increase of battery temperature mainly depends on current demand by motor while the initial battery temperature also depends on ambient temperature. To prevent the battery temperature from reaching the upper threshold a battery temperature based current (maximum allowed) derating algorithm is used. As one algorithm affects the other, this leads to Multi Input Single Output (MISO) system configuration.

To reduce the number of traffic accidents, most of the governments have mandated to include Combi Brake System (CBS) or Anti-lock Braking System (ABS) in two wheelers. While most of the homologation requirements for CBS can be fulfilled by straight line motion, CBS behavior is crucial while cornering for safety aspects. When vehicle is in cornering motion, the lateral forces generated at the tire decreases the effective longitudinal force available, which implies lesser braking force at tire. This paper represents a design methodology for tuning CBS for various critical scenarios mainly during cornering maneuver. A detailed study has been made at various combination of vehicle lean angle, vehicle speed and friction coefficient of road (μ) in straight line and cornering maneuver to effectively decide on front to rear brake force distribution to avoid either of the tires’ lock-up.

The standard usage of Combined Braking System (CBS) in lower cc/power 2-wheeler vehicles serves to reduce stopping distance and improve braking stability. The CBS system achieves this by engaging both the front and rear wheel brakes, taking advantage of the high load transfer characteristic during 2-wheeler braking. However, the current design of the CBS system relies on linear system analysis, based on vehicle geometry, load distribution, and tire-road friction. This approach overlooks the non-linearities inherent in braking dynamics, such as tire behavior and dynamic Center of Gravity (CoG) location. Consequently, the current CBS design methodology exhibits limitations, particularly in extreme scenarios where wheel lock-up may occur, such as on low friction surfaces or during panic braking. This paper proposes the incorporation of tire non-linearities into the design of CBS systems using Pacejka’s tire model.

With the increase of electric vehicles and lack of standardization in charging infrastructure, the variance in the charger cable length, battery health, and battery capacity can result in unevenness in the charging of lithium-ion batteries (LIBs), which increases the charging time and can deteriorate the battery’s health. Enabling adaptive charging of LIBs can accelerate the commercial application of electric vehicles (EVs). Charging of LIBs is critical and can be optimized to curtail the effective charging time. In this paper, a multistage adaptive charging strategy is presented for LIB-based EV applications to boost the SOC of the battery system in the shortest time. In the proposed charging strategy, initially, multiple pre-charge CC stages are employed to bring the battery out of the deep discharge state and to simultaneously calculate the resistances of the harness (line resistance), and the battery.

Battery packs used in Electric Vehicles (EVs) pose significant safety risks and can incur additional costs and downtime when facing extreme conditions such as thermal and undervoltage hard cut-off. This article emphasizes the importance of implementing thermal and voltage based derating techniques to ensure the safe operation of battery packs. Thermal derating controls the maximum allowed battery current to prevent thermal runaway along with maintaining the health of the cells. While voltage derating prevents cut-off at low SOC regions by managing the cell voltage operating range through real time calculation of DCIR based voltage drop. By adopting these methods, battery packs can operate more safely and reliably in various environment conditions, which is essential in many applications.

This paper presents a model-based algorithm designed for electric vehicles to estimate, control and optimize their range. By utilizing both short-term and long-term energy consumption data, the algorithm accurately predicts the range based on the current riding pattern. To achieve the desired range, the algorithm incorporates Hamilton-Jacobi-Bellman (HJB) optimization, which optimizes a cost function. The algorithm leverages short-term energy consumption patterns to smoothen the real-time watt-hour consumption for accurate range estimation. Simultaneously, it monitors long-term energy consumption patterns to account for factors such as vehicle aging, wear, terrain dynamics, and initial wh/km calculation. A comprehensive cost function, considering parameters like wh/km, rider demand, and terrain requirements, ensures optimal range without compromising the overall ride experience.