optimized multi-chemistry design for the electric vehicle industry:


Design Characteristics where Optimized Multi-Chemistry Design Outperforms All Conventional Systems

- The use of low-cost, energy-dense cells with insufficient power density as the multi-chemistry Energy Unit

- Design for use in systems with high power demands

- Drive cycles with frequent instances of regenerative braking and high power demand

- Optimizing for multiple design criteria

- Extending range and increasing power under the same cost constraint

- The lowest cost of the systems which maximize system power

Compact Luxury EV: Showcasing Benefits for All Optimization Cases

EnPowerTM allows for user-defined optimization cases to best satisfy the desired system characteristics. Particularly for cases which require more baseline power relative to the range of the electric car, EnPower’s optimized dual-chemistry cases outperform even the best single-chemistry cases to minimize cost, reduce weight, extend vehicle range, or increase vehicle power. The table below showcases the extent of these benefits for our Luxury Compact EV Optimization case, based on the BMW i3, with all cases held to the same optimization limitations and criteria.

The four cases highlighted above and their relative benefits are not unique to just the Luxury Compact EV, as EnPower’s optimized dual-chemistry design offers benefits for nearly every vehicle and every optimization case, for a select set of energy storage systems. The energy storage systems selected for this analysis include one of the top-selling single-battery systems, two dual-battery systems, and a system comprised of a high-energy battery and an ultracapacitor. The dual-battery cases can be controlled by multiple dynamic, self-learning strategies: Range Extender or Power Boost controls, which determine how to best utilize the high-power secondary energy storage to enhance system performance. Range Extender Controls make the most use of the energy assets available in the secondary energy storage unit to increase vehicle range, while Power Boost Controls simplify the charging circuit and ensure that there is always power available in the secondary unit to provide quick boosts of supplemental power.

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self-learning controls


EVE.AI TM Smart Controls dynamically manage each energy storage unit of a given asset throughout a given use cycle and throughout the life of the energy storage system, while adapting to external environments, cell degradation, and trends in power demand over time. These controls exist as a master BMS within the ECU of an EV and communicate directly with the BMS associated with each energy storage unit module (for vehicles containing more than one complementary energy storage unit). The self-learning algorithm prepares the energy storage system for future discharge conditions, increasing energy recovery under braking, and ensuring that peak power is always on demand for when users need it most.

A Connected Network of Electric Systems

EVE.Ai controlled systems communicate with each other to collect more information regarding the operating conditions, user habits, and trends of battery degradation to better inform the Ai-enhanced control algorithm and improve system performance. This means the energy storage system connected to solar panels at your office will proactively detect that you will likely be using the onsite chargers as your vehicle is approaching the building and is low on battery. Alternatively, path data from connected EVs up the road would detect anomalies in traffic patterns, which would update the likely power demand of your vehicle and adjust the discharge strategy as needed.