Depot Orchestrator · v1 · ETH Zurich Spin-off

The orchestration layer for electrified depot operations.

No siloed solutions: the nihito orchestrator combines routing, charging, and energy management in a single decision – and responds in real time to operational changes.

Request a potential analysis Our solution

−39%

total daily depot cost (case study reference)

minimum number of optimized systems

< 1 min

orchestrator optimization cycle time

Optimal solution

mathematically guaranteed

01 — The depot electrification dilemma

Electrification changes the operating logic of the depot.

Depots used to run as a sequence of independent processes — dispatch, charging, energy procurement. With electrification these become tightly coupled. No legacy system sees the whole picture. Europe will not become an electric continent by building more grid alone — it has to use the grid more intelligently.

FIG.00 — depot transition reference

Transition from a conventional diesel truck depot to an electrified, orchestrated depot with chargers, PV and wind

t₀ — siloed

t₁ — co-optimized

Routes → Charging
A driver returns at 14:00 from an unexpectedly long tour, the next departure is at 17:00 — a 3-hour window is not enough. Charging power must be reprioritized. Dispatch has no view into this.
Charging → Grid load
Five trucks arrive simultaneously from morning service and all plug in at 12:00. The peak triggers a demand charge billed for the entire month.
Grid load → Tariff & PV
On-site PV generates a midday surplus. Without coordination none of the vehicles charge then — even though three would be available until 15:00.

→ A central, real-time orchestrator is missing.

02 — The Orchestrator

Co-optimization across grid, energy, charging, fleet and depot.

The nihito orchestrator - one optimizer across all silos with central monitoring and real-time feedback.

In development· not production-ready

FIG.00 — co-optimization across silos orchestrator · live

orchestrator → subsystemtelemetry / state ← subsystem

Co-optimization, monitoring, and feedback must work in concert – in real time.

FIG.01 — closed-loop control · co-optimize → monitor → feedback cycle · < 1 min

control flowupdated boundaries fed back continuous optional critical

03 — WHY nihito

Why is this different?

C/01

FIG.A — cross-silo co-optimization

Cross-domain co-optimization instead of silo optimization

Most existing solutions optimize only one subsystem – while they claim integrations with other systems, the important difference of the nihito orchestrator is the joint optimization across conflicting objectives, under shared constraints, and with a single optimization engine.

C/02

FIG.B — real-time feedback & disruption response

Real-time orchestration and disruption response – not static planning

Most existing EMS/CPMS tools can forecast, monitor, or apply predefined charging rules – but they usually cannot dynamically rebalance charging power because a route changed, understand depot-level operational criticality, or coordinate energy decisions with dispatch constraints in real time.

C/03

FIG.C — provably optimal solution

Optimization-first DNA rooted in mathematics and control theory

Unlike most competitors, nihito is built on ETH-grade optimization, control theory, and operations research. Instead of static rules or isolated heuristics, we solve depot operations as a mathematically coordinated system under real-world operational and grid constraints.

04 — OUR OFFERING

From a first potential analysis to product rollout – in 3 steps and 12 months.

<1 month

01PoC

Potential analysis

Data-driven benchmark study identifying operational and energy optimization potential without changing existing systems.

Goal: Determine the depot benchmark – the mathematical optimum
Integr.: Read-only, CSV exports
Outcome: We show what is possible without touching live systems
Timeline: 2–4 weeks delivery

3 months

02Pilot

nihito Orchestrator light

Partially automated web-based orchestrator already improving your depot operations under real-world operational constraints.

Goal: Operational improvement under real-world constraints
Integr.: Existing APIs, no rip-and-replace
Outcome: A lightweight web-based version of the orchestrator
Timeline: 3 months after data availability

12 months

03Product

nihito Orchestrator

First version of an integrated orchestrator coordinating selected operations, charging, and energy systems across organizational boundaries.

Goal: Holistic orchestration across system boundaries
Integr.: Custom APIs, industry standards
Outcome: Full orchestrator - you operate and decide, nihito optimizes
Timeline: 12 months after data availability

05 — Case studies

Performance benchmarked against live depot operations

FS/01PostAuto Schweiz

Peak shaving & energy procurement timing

Fleet

12 buses, fixed schedules

Demand charge

12 CHF/kW · month

Energy price

0.05 – 0.12 CHF/kWh

Measured impact

Energy245 CHF−21%
Demand86 CHF−86%
Total / day332 CHF−39%

Compared to immediate charging upon depot arrival, a mathematically optimized charging strategy reduced total electricity costs by 39% for the same operational schedule.

FS/02Swiss waste-management operator

Self-consumption optimization with on-site PV

Fleet

10 vehicles, mixed schedules

~1,400 m² rooftop

Tariff

0.15 CHF/kWh · feed-in 0.05 CHF/kWh

Measured impact

Energy / week902 CHF−16%
Feed-in revenue−101 CHF−35%
Total / week1,582 CHF−7%

Initial analysis indicates that simply shifting weekend charging schedules can already reduce electricity costs by 7% without advanced optimization. Further optimization potential is currently being evaluated.

FS/03Your company

Looks interesting? Your case study could go here.

Fleet

—

Energy setup

—

Tariff

—

Measured impact

Energy——%
Demand / feed-in——%
Total——%

Curious what optimization could yield at your depot? We run a focused, data-driven potential analysis on your operations and energy setup.

Run a potential analysis with us

06 — For depot operators

Made for operators managing the transition to electrified depots.

Designed for fleets where charging, energy, routing, and depot operations become operationally interdependent – and failure is not an option.

01
Public transport operators
Bus depots transitioning from diesel to BEV — typically 40–400 vehicles, tight schedules, high availability requirements.
02
Waste management operators
Refuse collection fleets with predictable routes and high energy intensity per shift.
03
Logistics & last-mile
Multi-shift parcel and distribution depots optimizing across routes, vehicles and shared chargers.
04
Fleet operators at scale
Mixed fleets across multiple depots, coordinated under one energy and operations regime.

07 — The nihito Team

An experienced, diverse team behind nihito.

From ETH research to depot reality – in one team.

Markus Wenig

Co-founder & CEO

Strategy, business & market
Dr.-Ing. Mechanical Engineering (USTUTT), MAS ETH MTEC
12+ years at the interface of engineering and business

Fabio Widmer

Co-founder & CPO

Product & optimization
Dr. sc. ETH Zürich, Mechanical Engineering — top graduate
10+ years in control and optimization

Mohammad Moradi

Co-founder & CTO

Software, AI & implementation
Dr.-Ing. Mechanical Engineering (KIT), routing-optimization expert
8+ years in AI/ML and software engineering

Christopher Onder

Advisor

Professor at the Institute for Dynamic Systems and Control (IDSC, ETH Zürich)
30+ years bridging optimization, control theory and industrial practice

08 — Engage

Wonder how to max out your infrastructure while stabilizing operations?

One step at a time – let's start with a data-driven potential analysis to understand how charging, fleet operations, and energy usage can be optimized across your depot.

mawenig@nihito.io +41 76 291 78 32